{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Breast Cancer Detection Using Machine Learning Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import essential libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd # for data manupulation or analysis\n",
    "import numpy as np # for numeric calculation\n",
    "import matplotlib.pyplot as plt # for data visualization\n",
    "import seaborn as sns # for data visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load breast cancer dataset\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "cancer_dataset = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Manupulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
       "         1.189e-01],\n",
       "        [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
       "         8.902e-02],\n",
       "        [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n",
       "         8.758e-02],\n",
       "        ...,\n",
       "        [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n",
       "         7.820e-02],\n",
       "        [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n",
       "         1.240e-01],\n",
       "        [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
       "         7.039e-02]]),\n",
       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
       "        1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
       "        1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
       "        0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "        1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "        0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "        1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "        1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
       "        0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "        0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "        1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
       "        1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
       "        1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "        1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "        1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
       "        1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]),\n",
       " 'frame': None,\n",
       " 'target_names': array(['malignant', 'benign'], dtype='<U9'),\n",
       " 'DESCR': '.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n:Number of Instances: 569\\n\\n:Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n:Attribute Information:\\n    - radius (mean of distances from center to points on the perimeter)\\n    - texture (standard deviation of gray-scale values)\\n    - perimeter\\n    - area\\n    - smoothness (local variation in radius lengths)\\n    - compactness (perimeter^2 / area - 1.0)\\n    - concavity (severity of concave portions of the contour)\\n    - concave points (number of concave portions of the contour)\\n    - symmetry\\n    - fractal dimension (\"coastline approximation\" - 1)\\n\\n    The mean, standard error, and \"worst\" or largest (mean of the three\\n    worst/largest values) of these features were computed for each image,\\n    resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n    10 is Radius SE, field 20 is Worst Radius.\\n\\n    - class:\\n            - WDBC-Malignant\\n            - WDBC-Benign\\n\\n:Summary Statistics:\\n\\n===================================== ====== ======\\n                                        Min    Max\\n===================================== ====== ======\\nradius (mean):                        6.981  28.11\\ntexture (mean):                       9.71   39.28\\nperimeter (mean):                     43.79  188.5\\narea (mean):                          143.5  2501.0\\nsmoothness (mean):                    0.053  0.163\\ncompactness (mean):                   0.019  0.345\\nconcavity (mean):                     0.0    0.427\\nconcave points (mean):                0.0    0.201\\nsymmetry (mean):                      0.106  0.304\\nfractal dimension (mean):             0.05   0.097\\nradius (standard error):              0.112  2.873\\ntexture (standard error):             0.36   4.885\\nperimeter (standard error):           0.757  21.98\\narea (standard error):                6.802  542.2\\nsmoothness (standard error):          0.002  0.031\\ncompactness (standard error):         0.002  0.135\\nconcavity (standard error):           0.0    0.396\\nconcave points (standard error):      0.0    0.053\\nsymmetry (standard error):            0.008  0.079\\nfractal dimension (standard error):   0.001  0.03\\nradius (worst):                       7.93   36.04\\ntexture (worst):                      12.02  49.54\\nperimeter (worst):                    50.41  251.2\\narea (worst):                         185.2  4254.0\\nsmoothness (worst):                   0.071  0.223\\ncompactness (worst):                  0.027  1.058\\nconcavity (worst):                    0.0    1.252\\nconcave points (worst):               0.0    0.291\\nsymmetry (worst):                     0.156  0.664\\nfractal dimension (worst):            0.055  0.208\\n===================================== ====== ======\\n\\n:Missing Attribute Values: None\\n\\n:Class Distribution: 212 - Malignant, 357 - Benign\\n\\n:Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n:Donor: Nick Street\\n\\n:Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n.. dropdown:: References\\n\\n  - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction\\n    for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on\\n    Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n    San Jose, CA, 1993.\\n  - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and\\n    prognosis via linear programming. Operations Research, 43(4), pages 570-577,\\n    July-August 1995.\\n  - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n    to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)\\n    163-171.\\n',\n",
       " 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "        'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "        'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "        'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "        'smoothness error', 'compactness error', 'concavity error',\n",
       "        'concave points error', 'symmetry error',\n",
       "        'fractal dimension error', 'worst radius', 'worst texture',\n",
       "        'worst perimeter', 'worst area', 'worst smoothness',\n",
       "        'worst compactness', 'worst concavity', 'worst concave points',\n",
       "        'worst symmetry', 'worst fractal dimension'], dtype='<U23'),\n",
       " 'filename': 'breast_cancer.csv',\n",
       " 'data_module': 'sklearn.datasets.data'}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils._bunch.Bunch"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(cancer_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# keys in dataset\n",
    "cancer_dataset.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
       "        1.189e-01],\n",
       "       [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
       "        8.902e-02],\n",
       "       [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n",
       "        8.758e-02],\n",
       "       ...,\n",
       "       [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n",
       "        7.820e-02],\n",
       "       [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n",
       "        1.240e-01],\n",
       "       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
       "        7.039e-02]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# featurs of each cells in numeric format\n",
    "cancer_dataset['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(cancer_dataset['data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
       "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
       "       0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
       "       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# malignant or benign value\n",
    "cancer_dataset['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['malignant', 'benign'], dtype='<U9')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# target value name malignant or benign tumor\n",
    "cancer_dataset['target_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _breast_cancer_dataset:\n",
      "\n",
      "Breast cancer wisconsin (diagnostic) dataset\n",
      "--------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      ":Number of Instances: 569\n",
      "\n",
      ":Number of Attributes: 30 numeric, predictive attributes and the class\n",
      "\n",
      ":Attribute Information:\n",
      "    - radius (mean of distances from center to points on the perimeter)\n",
      "    - texture (standard deviation of gray-scale values)\n",
      "    - perimeter\n",
      "    - area\n",
      "    - smoothness (local variation in radius lengths)\n",
      "    - compactness (perimeter^2 / area - 1.0)\n",
      "    - concavity (severity of concave portions of the contour)\n",
      "    - concave points (number of concave portions of the contour)\n",
      "    - symmetry\n",
      "    - fractal dimension (\"coastline approximation\" - 1)\n",
      "\n",
      "    The mean, standard error, and \"worst\" or largest (mean of the three\n",
      "    worst/largest values) of these features were computed for each image,\n",
      "    resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
      "    10 is Radius SE, field 20 is Worst Radius.\n",
      "\n",
      "    - class:\n",
      "            - WDBC-Malignant\n",
      "            - WDBC-Benign\n",
      "\n",
      ":Summary Statistics:\n",
      "\n",
      "===================================== ====== ======\n",
      "                                        Min    Max\n",
      "===================================== ====== ======\n",
      "radius (mean):                        6.981  28.11\n",
      "texture (mean):                       9.71   39.28\n",
      "perimeter (mean):                     43.79  188.5\n",
      "area (mean):                          143.5  2501.0\n",
      "smoothness (mean):                    0.053  0.163\n",
      "compactness (mean):                   0.019  0.345\n",
      "concavity (mean):                     0.0    0.427\n",
      "concave points (mean):                0.0    0.201\n",
      "symmetry (mean):                      0.106  0.304\n",
      "fractal dimension (mean):             0.05   0.097\n",
      "radius (standard error):              0.112  2.873\n",
      "texture (standard error):             0.36   4.885\n",
      "perimeter (standard error):           0.757  21.98\n",
      "area (standard error):                6.802  542.2\n",
      "smoothness (standard error):          0.002  0.031\n",
      "compactness (standard error):         0.002  0.135\n",
      "concavity (standard error):           0.0    0.396\n",
      "concave points (standard error):      0.0    0.053\n",
      "symmetry (standard error):            0.008  0.079\n",
      "fractal dimension (standard error):   0.001  0.03\n",
      "radius (worst):                       7.93   36.04\n",
      "texture (worst):                      12.02  49.54\n",
      "perimeter (worst):                    50.41  251.2\n",
      "area (worst):                         185.2  4254.0\n",
      "smoothness (worst):                   0.071  0.223\n",
      "compactness (worst):                  0.027  1.058\n",
      "concavity (worst):                    0.0    1.252\n",
      "concave points (worst):               0.0    0.291\n",
      "symmetry (worst):                     0.156  0.664\n",
      "fractal dimension (worst):            0.055  0.208\n",
      "===================================== ====== ======\n",
      "\n",
      ":Missing Attribute Values: None\n",
      "\n",
      ":Class Distribution: 212 - Malignant, 357 - Benign\n",
      "\n",
      ":Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
      "\n",
      ":Donor: Nick Street\n",
      "\n",
      ":Date: November, 1995\n",
      "\n",
      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
      "https://goo.gl/U2Uwz2\n",
      "\n",
      "Features are computed from a digitized image of a fine needle\n",
      "aspirate (FNA) of a breast mass.  They describe\n",
      "characteristics of the cell nuclei present in the image.\n",
      "\n",
      "Separating plane described above was obtained using\n",
      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
      "pp. 97-101, 1992], a classification method which uses linear\n",
      "programming to construct a decision tree.  Relevant features\n",
      "were selected using an exhaustive search in the space of 1-4\n",
      "features and 1-3 separating planes.\n",
      "\n",
      "The actual linear program used to obtain the separating plane\n",
      "in the 3-dimensional space is that described in:\n",
      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
      "Optimization Methods and Software 1, 1992, 23-34].\n",
      "\n",
      "This database is also available through the UW CS ftp server:\n",
      "\n",
      "ftp ftp.cs.wisc.edu\n",
      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
      "\n",
      ".. dropdown:: References\n",
      "\n",
      "  - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction\n",
      "    for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on\n",
      "    Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
      "    San Jose, CA, 1993.\n",
      "  - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and\n",
      "    prognosis via linear programming. Operations Research, 43(4), pages 570-577,\n",
      "    July-August 1995.\n",
      "  - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
      "    to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)\n",
      "    163-171.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# description of data\n",
    "print(cancer_dataset['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n",
      " 'mean smoothness' 'mean compactness' 'mean concavity'\n",
      " 'mean concave points' 'mean symmetry' 'mean fractal dimension'\n",
      " 'radius error' 'texture error' 'perimeter error' 'area error'\n",
      " 'smoothness error' 'compactness error' 'concavity error'\n",
      " 'concave points error' 'symmetry error' 'fractal dimension error'\n",
      " 'worst radius' 'worst texture' 'worst perimeter' 'worst area'\n",
      " 'worst smoothness' 'worst compactness' 'worst concavity'\n",
      " 'worst concave points' 'worst symmetry' 'worst fractal dimension']\n"
     ]
    }
   ],
   "source": [
    "# name of features\n",
    "print(cancer_dataset['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "breast_cancer.csv\n"
     ]
    }
   ],
   "source": [
    "# location/path of data file\n",
    "print(cancer_dataset['filename'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# create datafrmae\n",
    "cancer_df = pd.DataFrame(np.c_[cancer_dataset['data'],cancer_dataset['target']],\n",
    "             columns = np.append(cancer_dataset['feature_names'], ['target']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DataFrame to CSV file\n",
    "cancer_df.to_csv('breast_cancer_dataframe.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.45</td>\n",
       "      <td>15.70</td>\n",
       "      <td>82.57</td>\n",
       "      <td>477.1</td>\n",
       "      <td>0.12780</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>0.1578</td>\n",
       "      <td>0.08089</td>\n",
       "      <td>0.2087</td>\n",
       "      <td>0.07613</td>\n",
       "      <td>...</td>\n",
       "      <td>23.75</td>\n",
       "      <td>103.40</td>\n",
       "      <td>741.6</td>\n",
       "      <td>0.1791</td>\n",
       "      <td>0.5249</td>\n",
       "      <td>0.5355</td>\n",
       "      <td>0.1741</td>\n",
       "      <td>0.3985</td>\n",
       "      <td>0.12440</td>\n",
       "      <td>0.0</td>\n",
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       "<p>6 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "5        12.45         15.70           82.57      477.1          0.12780   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "5           0.17000          0.1578              0.08089         0.2087   \n",
       "\n",
       "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
       "0                 0.07871  ...          17.33           184.60      2019.0   \n",
       "1                 0.05667  ...          23.41           158.80      1956.0   \n",
       "2                 0.05999  ...          25.53           152.50      1709.0   \n",
       "3                 0.09744  ...          26.50            98.87       567.7   \n",
       "4                 0.05883  ...          16.67           152.20      1575.0   \n",
       "5                 0.07613  ...          23.75           103.40       741.6   \n",
       "\n",
       "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "5            0.1791             0.5249           0.5355                0.1741   \n",
       "\n",
       "   worst symmetry  worst fractal dimension  target  \n",
       "0          0.4601                  0.11890     0.0  \n",
       "1          0.2750                  0.08902     0.0  \n",
       "2          0.3613                  0.08758     0.0  \n",
       "3          0.6638                  0.17300     0.0  \n",
       "4          0.2364                  0.07678     0.0  \n",
       "5          0.3985                  0.12440     0.0  \n",
       "\n",
       "[6 rows x 31 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Head of cancer DataFrame\n",
    "cancer_df.head(6) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>mean radius</th>\n",
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       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
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       "      <th>563</th>\n",
       "      <td>20.92</td>\n",
       "      <td>25.09</td>\n",
       "      <td>143.00</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>0.10990</td>\n",
       "      <td>0.22360</td>\n",
       "      <td>0.31740</td>\n",
       "      <td>0.14740</td>\n",
       "      <td>0.2149</td>\n",
       "      <td>0.06879</td>\n",
       "      <td>...</td>\n",
       "      <td>29.41</td>\n",
       "      <td>179.10</td>\n",
       "      <td>1819.0</td>\n",
       "      <td>0.14070</td>\n",
       "      <td>0.41860</td>\n",
       "      <td>0.6599</td>\n",
       "      <td>0.2542</td>\n",
       "      <td>0.2929</td>\n",
       "      <td>0.09873</td>\n",
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       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>565</th>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "563        20.92         25.09          143.00     1347.0          0.10990   \n",
       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
       "566        16.60         28.08          108.30      858.1          0.08455   \n",
       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
       "568         7.76         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "563           0.22360         0.31740              0.14740         0.2149   \n",
       "564           0.11590         0.24390              0.13890         0.1726   \n",
       "565           0.10340         0.14400              0.09791         0.1752   \n",
       "566           0.10230         0.09251              0.05302         0.1590   \n",
       "567           0.27700         0.35140              0.15200         0.2397   \n",
       "568           0.04362         0.00000              0.00000         0.1587   \n",
       "\n",
       "     mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
       "563                 0.06879  ...          29.41           179.10      1819.0   \n",
       "564                 0.05623  ...          26.40           166.10      2027.0   \n",
       "565                 0.05533  ...          38.25           155.00      1731.0   \n",
       "566                 0.05648  ...          34.12           126.70      1124.0   \n",
       "567                 0.07016  ...          39.42           184.60      1821.0   \n",
       "568                 0.05884  ...          30.37            59.16       268.6   \n",
       "\n",
       "     worst smoothness  worst compactness  worst concavity  \\\n",
       "563           0.14070            0.41860           0.6599   \n",
       "564           0.14100            0.21130           0.4107   \n",
       "565           0.11660            0.19220           0.3215   \n",
       "566           0.11390            0.30940           0.3403   \n",
       "567           0.16500            0.86810           0.9387   \n",
       "568           0.08996            0.06444           0.0000   \n",
       "\n",
       "     worst concave points  worst symmetry  worst fractal dimension  target  \n",
       "563                0.2542          0.2929                  0.09873     0.0  \n",
       "564                0.2216          0.2060                  0.07115     0.0  \n",
       "565                0.1628          0.2572                  0.06637     0.0  \n",
       "566                0.1418          0.2218                  0.07820     0.0  \n",
       "567                0.2650          0.4087                  0.12400     0.0  \n",
       "568                0.0000          0.2871                  0.07039     1.0  \n",
       "\n",
       "[6 rows x 31 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tail of cancer DataFrame\n",
    "cancer_df.tail(6) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 31 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   mean radius              569 non-null    float64\n",
      " 1   mean texture             569 non-null    float64\n",
      " 2   mean perimeter           569 non-null    float64\n",
      " 3   mean area                569 non-null    float64\n",
      " 4   mean smoothness          569 non-null    float64\n",
      " 5   mean compactness         569 non-null    float64\n",
      " 6   mean concavity           569 non-null    float64\n",
      " 7   mean concave points      569 non-null    float64\n",
      " 8   mean symmetry            569 non-null    float64\n",
      " 9   mean fractal dimension   569 non-null    float64\n",
      " 10  radius error             569 non-null    float64\n",
      " 11  texture error            569 non-null    float64\n",
      " 12  perimeter error          569 non-null    float64\n",
      " 13  area error               569 non-null    float64\n",
      " 14  smoothness error         569 non-null    float64\n",
      " 15  compactness error        569 non-null    float64\n",
      " 16  concavity error          569 non-null    float64\n",
      " 17  concave points error     569 non-null    float64\n",
      " 18  symmetry error           569 non-null    float64\n",
      " 19  fractal dimension error  569 non-null    float64\n",
      " 20  worst radius             569 non-null    float64\n",
      " 21  worst texture            569 non-null    float64\n",
      " 22  worst perimeter          569 non-null    float64\n",
      " 23  worst area               569 non-null    float64\n",
      " 24  worst smoothness         569 non-null    float64\n",
      " 25  worst compactness        569 non-null    float64\n",
      " 26  worst concavity          569 non-null    float64\n",
      " 27  worst concave points     569 non-null    float64\n",
      " 28  worst symmetry           569 non-null    float64\n",
      " 29  worst fractal dimension  569 non-null    float64\n",
      " 30  target                   569 non-null    float64\n",
      "dtypes: float64(31)\n",
      "memory usage: 137.9 KB\n"
     ]
    }
   ],
   "source": [
    "# Information of cancer Dataframe\n",
    "cancer_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>...</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "      <td>0.627417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>...</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "      <td>0.483918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>...</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>...</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>...</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>...</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>0.097440</td>\n",
       "      <td>...</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean radius  mean texture  mean perimeter    mean area  \\\n",
       "count   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     14.127292     19.289649       91.969033   654.889104   \n",
       "std       3.524049      4.301036       24.298981   351.914129   \n",
       "min       6.981000      9.710000       43.790000   143.500000   \n",
       "25%      11.700000     16.170000       75.170000   420.300000   \n",
       "50%      13.370000     18.840000       86.240000   551.100000   \n",
       "75%      15.780000     21.800000      104.100000   782.700000   \n",
       "max      28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       mean symmetry  mean fractal dimension  ...  worst texture  \\\n",
       "count     569.000000              569.000000  ...     569.000000   \n",
       "mean        0.181162                0.062798  ...      25.677223   \n",
       "std         0.027414                0.007060  ...       6.146258   \n",
       "min         0.106000                0.049960  ...      12.020000   \n",
       "25%         0.161900                0.057700  ...      21.080000   \n",
       "50%         0.179200                0.061540  ...      25.410000   \n",
       "75%         0.195700                0.066120  ...      29.720000   \n",
       "max         0.304000                0.097440  ...      49.540000   \n",
       "\n",
       "       worst perimeter   worst area  worst smoothness  worst compactness  \\\n",
       "count       569.000000   569.000000        569.000000         569.000000   \n",
       "mean        107.261213   880.583128          0.132369           0.254265   \n",
       "std          33.602542   569.356993          0.022832           0.157336   \n",
       "min          50.410000   185.200000          0.071170           0.027290   \n",
       "25%          84.110000   515.300000          0.116600           0.147200   \n",
       "50%          97.660000   686.500000          0.131300           0.211900   \n",
       "75%         125.400000  1084.000000          0.146000           0.339100   \n",
       "max         251.200000  4254.000000          0.222600           1.058000   \n",
       "\n",
       "       worst concavity  worst concave points  worst symmetry  \\\n",
       "count       569.000000            569.000000      569.000000   \n",
       "mean          0.272188              0.114606        0.290076   \n",
       "std           0.208624              0.065732        0.061867   \n",
       "min           0.000000              0.000000        0.156500   \n",
       "25%           0.114500              0.064930        0.250400   \n",
       "50%           0.226700              0.099930        0.282200   \n",
       "75%           0.382900              0.161400        0.317900   \n",
       "max           1.252000              0.291000        0.663800   \n",
       "\n",
       "       worst fractal dimension      target  \n",
       "count               569.000000  569.000000  \n",
       "mean                  0.083946    0.627417  \n",
       "std                   0.018061    0.483918  \n",
       "min                   0.055040    0.000000  \n",
       "25%                   0.071460    0.000000  \n",
       "50%                   0.080040    1.000000  \n",
       "75%                   0.092080    1.000000  \n",
       "max                   0.207500    1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numerical distribution of data\n",
    "cancer_df.describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean radius                0\n",
       "mean texture               0\n",
       "mean perimeter             0\n",
       "mean area                  0\n",
       "mean smoothness            0\n",
       "mean compactness           0\n",
       "mean concavity             0\n",
       "mean concave points        0\n",
       "mean symmetry              0\n",
       "mean fractal dimension     0\n",
       "radius error               0\n",
       "texture error              0\n",
       "perimeter error            0\n",
       "area error                 0\n",
       "smoothness error           0\n",
       "compactness error          0\n",
       "concavity error            0\n",
       "concave points error       0\n",
       "symmetry error             0\n",
       "fractal dimension error    0\n",
       "worst radius               0\n",
       "worst texture              0\n",
       "worst perimeter            0\n",
       "worst area                 0\n",
       "worst smoothness           0\n",
       "worst compactness          0\n",
       "worst concavity            0\n",
       "worst concave points       0\n",
       "worst symmetry             0\n",
       "worst fractal dimension    0\n",
       "target                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Paiplot of cancer dataframe\n",
    "sns.pairplot(cancer_df, hue = 'target') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x32c288fa0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1322x1250 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pair plot of sample feature\n",
    "sns.pairplot(cancer_df, hue = 'target', \n",
    "             vars = ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness'] ) # ****** img 5 ***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the target class\n",
    "sns.countplot(cancer_df['target']) #  **************************** img 5 *************************  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# counter plot of feature mean radius\n",
    "plt.figure(figsize = (20,8))\n",
    "sns.countplot(cancer_df['mean radius']) # *** img 7 ****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap of DataFrame\n",
    "plt.figure(figsize=(16,9))\n",
    "sns.heatmap(cancer_df) # **** img 8 ****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Heatmap of a correlation matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean radius</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.323782</td>\n",
       "      <td>0.997855</td>\n",
       "      <td>0.987357</td>\n",
       "      <td>0.170581</td>\n",
       "      <td>0.506124</td>\n",
       "      <td>0.676764</td>\n",
       "      <td>0.822529</td>\n",
       "      <td>0.147741</td>\n",
       "      <td>-0.311631</td>\n",
       "      <td>...</td>\n",
       "      <td>0.297008</td>\n",
       "      <td>0.965137</td>\n",
       "      <td>0.941082</td>\n",
       "      <td>0.119616</td>\n",
       "      <td>0.413463</td>\n",
       "      <td>0.526911</td>\n",
       "      <td>0.744214</td>\n",
       "      <td>0.163953</td>\n",
       "      <td>0.007066</td>\n",
       "      <td>-0.730029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean texture</th>\n",
       "      <td>0.323782</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.329533</td>\n",
       "      <td>0.321086</td>\n",
       "      <td>-0.023389</td>\n",
       "      <td>0.236702</td>\n",
       "      <td>0.302418</td>\n",
       "      <td>0.293464</td>\n",
       "      <td>0.071401</td>\n",
       "      <td>-0.076437</td>\n",
       "      <td>...</td>\n",
       "      <td>0.912045</td>\n",
       "      <td>0.358040</td>\n",
       "      <td>0.343546</td>\n",
       "      <td>0.077503</td>\n",
       "      <td>0.277830</td>\n",
       "      <td>0.301025</td>\n",
       "      <td>0.295316</td>\n",
       "      <td>0.105008</td>\n",
       "      <td>0.119205</td>\n",
       "      <td>-0.415185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean perimeter</th>\n",
       "      <td>0.997855</td>\n",
       "      <td>0.329533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.986507</td>\n",
       "      <td>0.207278</td>\n",
       "      <td>0.556936</td>\n",
       "      <td>0.716136</td>\n",
       "      <td>0.850977</td>\n",
       "      <td>0.183027</td>\n",
       "      <td>-0.261477</td>\n",
       "      <td>...</td>\n",
       "      <td>0.303038</td>\n",
       "      <td>0.970387</td>\n",
       "      <td>0.941550</td>\n",
       "      <td>0.150549</td>\n",
       "      <td>0.455774</td>\n",
       "      <td>0.563879</td>\n",
       "      <td>0.771241</td>\n",
       "      <td>0.189115</td>\n",
       "      <td>0.051019</td>\n",
       "      <td>-0.742636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean area</th>\n",
       "      <td>0.987357</td>\n",
       "      <td>0.321086</td>\n",
       "      <td>0.986507</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.177028</td>\n",
       "      <td>0.498502</td>\n",
       "      <td>0.685983</td>\n",
       "      <td>0.823269</td>\n",
       "      <td>0.151293</td>\n",
       "      <td>-0.283110</td>\n",
       "      <td>...</td>\n",
       "      <td>0.287489</td>\n",
       "      <td>0.959120</td>\n",
       "      <td>0.959213</td>\n",
       "      <td>0.123523</td>\n",
       "      <td>0.390410</td>\n",
       "      <td>0.512606</td>\n",
       "      <td>0.722017</td>\n",
       "      <td>0.143570</td>\n",
       "      <td>0.003738</td>\n",
       "      <td>-0.708984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean smoothness</th>\n",
       "      <td>0.170581</td>\n",
       "      <td>-0.023389</td>\n",
       "      <td>0.207278</td>\n",
       "      <td>0.177028</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.659123</td>\n",
       "      <td>0.521984</td>\n",
       "      <td>0.553695</td>\n",
       "      <td>0.557775</td>\n",
       "      <td>0.584792</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036072</td>\n",
       "      <td>0.238853</td>\n",
       "      <td>0.206718</td>\n",
       "      <td>0.805324</td>\n",
       "      <td>0.472468</td>\n",
       "      <td>0.434926</td>\n",
       "      <td>0.503053</td>\n",
       "      <td>0.394309</td>\n",
       "      <td>0.499316</td>\n",
       "      <td>-0.358560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean compactness</th>\n",
       "      <td>0.506124</td>\n",
       "      <td>0.236702</td>\n",
       "      <td>0.556936</td>\n",
       "      <td>0.498502</td>\n",
       "      <td>0.659123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.883121</td>\n",
       "      <td>0.831135</td>\n",
       "      <td>0.602641</td>\n",
       "      <td>0.565369</td>\n",
       "      <td>...</td>\n",
       "      <td>0.248133</td>\n",
       "      <td>0.590210</td>\n",
       "      <td>0.509604</td>\n",
       "      <td>0.565541</td>\n",
       "      <td>0.865809</td>\n",
       "      <td>0.816275</td>\n",
       "      <td>0.815573</td>\n",
       "      <td>0.510223</td>\n",
       "      <td>0.687382</td>\n",
       "      <td>-0.596534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean concavity</th>\n",
       "      <td>0.676764</td>\n",
       "      <td>0.302418</td>\n",
       "      <td>0.716136</td>\n",
       "      <td>0.685983</td>\n",
       "      <td>0.521984</td>\n",
       "      <td>0.883121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.921391</td>\n",
       "      <td>0.500667</td>\n",
       "      <td>0.336783</td>\n",
       "      <td>...</td>\n",
       "      <td>0.299879</td>\n",
       "      <td>0.729565</td>\n",
       "      <td>0.675987</td>\n",
       "      <td>0.448822</td>\n",
       "      <td>0.754968</td>\n",
       "      <td>0.884103</td>\n",
       "      <td>0.861323</td>\n",
       "      <td>0.409464</td>\n",
       "      <td>0.514930</td>\n",
       "      <td>-0.696360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean concave points</th>\n",
       "      <td>0.822529</td>\n",
       "      <td>0.293464</td>\n",
       "      <td>0.850977</td>\n",
       "      <td>0.823269</td>\n",
       "      <td>0.553695</td>\n",
       "      <td>0.831135</td>\n",
       "      <td>0.921391</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.462497</td>\n",
       "      <td>0.166917</td>\n",
       "      <td>...</td>\n",
       "      <td>0.292752</td>\n",
       "      <td>0.855923</td>\n",
       "      <td>0.809630</td>\n",
       "      <td>0.452753</td>\n",
       "      <td>0.667454</td>\n",
       "      <td>0.752399</td>\n",
       "      <td>0.910155</td>\n",
       "      <td>0.375744</td>\n",
       "      <td>0.368661</td>\n",
       "      <td>-0.776614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean symmetry</th>\n",
       "      <td>0.147741</td>\n",
       "      <td>0.071401</td>\n",
       "      <td>0.183027</td>\n",
       "      <td>0.151293</td>\n",
       "      <td>0.557775</td>\n",
       "      <td>0.602641</td>\n",
       "      <td>0.500667</td>\n",
       "      <td>0.462497</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479921</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090651</td>\n",
       "      <td>0.219169</td>\n",
       "      <td>0.177193</td>\n",
       "      <td>0.426675</td>\n",
       "      <td>0.473200</td>\n",
       "      <td>0.433721</td>\n",
       "      <td>0.430297</td>\n",
       "      <td>0.699826</td>\n",
       "      <td>0.438413</td>\n",
       "      <td>-0.330499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <td>-0.311631</td>\n",
       "      <td>-0.076437</td>\n",
       "      <td>-0.261477</td>\n",
       "      <td>-0.283110</td>\n",
       "      <td>0.584792</td>\n",
       "      <td>0.565369</td>\n",
       "      <td>0.336783</td>\n",
       "      <td>0.166917</td>\n",
       "      <td>0.479921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051269</td>\n",
       "      <td>-0.205151</td>\n",
       "      <td>-0.231854</td>\n",
       "      <td>0.504942</td>\n",
       "      <td>0.458798</td>\n",
       "      <td>0.346234</td>\n",
       "      <td>0.175325</td>\n",
       "      <td>0.334019</td>\n",
       "      <td>0.767297</td>\n",
       "      <td>0.012838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius error</th>\n",
       "      <td>0.679090</td>\n",
       "      <td>0.275869</td>\n",
       "      <td>0.691765</td>\n",
       "      <td>0.732562</td>\n",
       "      <td>0.301467</td>\n",
       "      <td>0.497473</td>\n",
       "      <td>0.631925</td>\n",
       "      <td>0.698050</td>\n",
       "      <td>0.303379</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.194799</td>\n",
       "      <td>0.719684</td>\n",
       "      <td>0.751548</td>\n",
       "      <td>0.141919</td>\n",
       "      <td>0.287103</td>\n",
       "      <td>0.380585</td>\n",
       "      <td>0.531062</td>\n",
       "      <td>0.094543</td>\n",
       "      <td>0.049559</td>\n",
       "      <td>-0.567134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture error</th>\n",
       "      <td>-0.097317</td>\n",
       "      <td>0.386358</td>\n",
       "      <td>-0.086761</td>\n",
       "      <td>-0.066280</td>\n",
       "      <td>0.068406</td>\n",
       "      <td>0.046205</td>\n",
       "      <td>0.076218</td>\n",
       "      <td>0.021480</td>\n",
       "      <td>0.128053</td>\n",
       "      <td>0.164174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.409003</td>\n",
       "      <td>-0.102242</td>\n",
       "      <td>-0.083195</td>\n",
       "      <td>-0.073658</td>\n",
       "      <td>-0.092439</td>\n",
       "      <td>-0.068956</td>\n",
       "      <td>-0.119638</td>\n",
       "      <td>-0.128215</td>\n",
       "      <td>-0.045655</td>\n",
       "      <td>0.008303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter error</th>\n",
       "      <td>0.674172</td>\n",
       "      <td>0.281673</td>\n",
       "      <td>0.693135</td>\n",
       "      <td>0.726628</td>\n",
       "      <td>0.296092</td>\n",
       "      <td>0.548905</td>\n",
       "      <td>0.660391</td>\n",
       "      <td>0.710650</td>\n",
       "      <td>0.313893</td>\n",
       "      <td>0.039830</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200371</td>\n",
       "      <td>0.721031</td>\n",
       "      <td>0.730713</td>\n",
       "      <td>0.130054</td>\n",
       "      <td>0.341919</td>\n",
       "      <td>0.418899</td>\n",
       "      <td>0.554897</td>\n",
       "      <td>0.109930</td>\n",
       "      <td>0.085433</td>\n",
       "      <td>-0.556141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area error</th>\n",
       "      <td>0.735864</td>\n",
       "      <td>0.259845</td>\n",
       "      <td>0.744983</td>\n",
       "      <td>0.800086</td>\n",
       "      <td>0.246552</td>\n",
       "      <td>0.455653</td>\n",
       "      <td>0.617427</td>\n",
       "      <td>0.690299</td>\n",
       "      <td>0.223970</td>\n",
       "      <td>-0.090170</td>\n",
       "      <td>...</td>\n",
       "      <td>0.196497</td>\n",
       "      <td>0.761213</td>\n",
       "      <td>0.811408</td>\n",
       "      <td>0.125389</td>\n",
       "      <td>0.283257</td>\n",
       "      <td>0.385100</td>\n",
       "      <td>0.538166</td>\n",
       "      <td>0.074126</td>\n",
       "      <td>0.017539</td>\n",
       "      <td>-0.548236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness error</th>\n",
       "      <td>-0.222600</td>\n",
       "      <td>0.006614</td>\n",
       "      <td>-0.202694</td>\n",
       "      <td>-0.166777</td>\n",
       "      <td>0.332375</td>\n",
       "      <td>0.135299</td>\n",
       "      <td>0.098564</td>\n",
       "      <td>0.027653</td>\n",
       "      <td>0.187321</td>\n",
       "      <td>0.401964</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.074743</td>\n",
       "      <td>-0.217304</td>\n",
       "      <td>-0.182195</td>\n",
       "      <td>0.314457</td>\n",
       "      <td>-0.055558</td>\n",
       "      <td>-0.058298</td>\n",
       "      <td>-0.102007</td>\n",
       "      <td>-0.107342</td>\n",
       "      <td>0.101480</td>\n",
       "      <td>0.067016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness error</th>\n",
       "      <td>0.206000</td>\n",
       "      <td>0.191975</td>\n",
       "      <td>0.250744</td>\n",
       "      <td>0.212583</td>\n",
       "      <td>0.318943</td>\n",
       "      <td>0.738722</td>\n",
       "      <td>0.670279</td>\n",
       "      <td>0.490424</td>\n",
       "      <td>0.421659</td>\n",
       "      <td>0.559837</td>\n",
       "      <td>...</td>\n",
       "      <td>0.143003</td>\n",
       "      <td>0.260516</td>\n",
       "      <td>0.199371</td>\n",
       "      <td>0.227394</td>\n",
       "      <td>0.678780</td>\n",
       "      <td>0.639147</td>\n",
       "      <td>0.483208</td>\n",
       "      <td>0.277878</td>\n",
       "      <td>0.590973</td>\n",
       "      <td>-0.292999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity error</th>\n",
       "      <td>0.194204</td>\n",
       "      <td>0.143293</td>\n",
       "      <td>0.228082</td>\n",
       "      <td>0.207660</td>\n",
       "      <td>0.248396</td>\n",
       "      <td>0.570517</td>\n",
       "      <td>0.691270</td>\n",
       "      <td>0.439167</td>\n",
       "      <td>0.342627</td>\n",
       "      <td>0.446630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100241</td>\n",
       "      <td>0.226680</td>\n",
       "      <td>0.188353</td>\n",
       "      <td>0.168481</td>\n",
       "      <td>0.484858</td>\n",
       "      <td>0.662564</td>\n",
       "      <td>0.440472</td>\n",
       "      <td>0.197788</td>\n",
       "      <td>0.439329</td>\n",
       "      <td>-0.253730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points error</th>\n",
       "      <td>0.376169</td>\n",
       "      <td>0.163851</td>\n",
       "      <td>0.407217</td>\n",
       "      <td>0.372320</td>\n",
       "      <td>0.380676</td>\n",
       "      <td>0.642262</td>\n",
       "      <td>0.683260</td>\n",
       "      <td>0.615634</td>\n",
       "      <td>0.393298</td>\n",
       "      <td>0.341198</td>\n",
       "      <td>...</td>\n",
       "      <td>0.086741</td>\n",
       "      <td>0.394999</td>\n",
       "      <td>0.342271</td>\n",
       "      <td>0.215351</td>\n",
       "      <td>0.452888</td>\n",
       "      <td>0.549592</td>\n",
       "      <td>0.602450</td>\n",
       "      <td>0.143116</td>\n",
       "      <td>0.310655</td>\n",
       "      <td>-0.408042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry error</th>\n",
       "      <td>-0.104321</td>\n",
       "      <td>0.009127</td>\n",
       "      <td>-0.081629</td>\n",
       "      <td>-0.072497</td>\n",
       "      <td>0.200774</td>\n",
       "      <td>0.229977</td>\n",
       "      <td>0.178009</td>\n",
       "      <td>0.095351</td>\n",
       "      <td>0.449137</td>\n",
       "      <td>0.345007</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.077473</td>\n",
       "      <td>-0.103753</td>\n",
       "      <td>-0.110343</td>\n",
       "      <td>-0.012662</td>\n",
       "      <td>0.060255</td>\n",
       "      <td>0.037119</td>\n",
       "      <td>-0.030413</td>\n",
       "      <td>0.389402</td>\n",
       "      <td>0.078079</td>\n",
       "      <td>0.006522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal dimension error</th>\n",
       "      <td>-0.042641</td>\n",
       "      <td>0.054458</td>\n",
       "      <td>-0.005523</td>\n",
       "      <td>-0.019887</td>\n",
       "      <td>0.283607</td>\n",
       "      <td>0.507318</td>\n",
       "      <td>0.449301</td>\n",
       "      <td>0.257584</td>\n",
       "      <td>0.331786</td>\n",
       "      <td>0.688132</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003195</td>\n",
       "      <td>-0.001000</td>\n",
       "      <td>-0.022736</td>\n",
       "      <td>0.170568</td>\n",
       "      <td>0.390159</td>\n",
       "      <td>0.379975</td>\n",
       "      <td>0.215204</td>\n",
       "      <td>0.111094</td>\n",
       "      <td>0.591328</td>\n",
       "      <td>-0.077972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst radius</th>\n",
       "      <td>0.969539</td>\n",
       "      <td>0.352573</td>\n",
       "      <td>0.969476</td>\n",
       "      <td>0.962746</td>\n",
       "      <td>0.213120</td>\n",
       "      <td>0.535315</td>\n",
       "      <td>0.688236</td>\n",
       "      <td>0.830318</td>\n",
       "      <td>0.185728</td>\n",
       "      <td>-0.253691</td>\n",
       "      <td>...</td>\n",
       "      <td>0.359921</td>\n",
       "      <td>0.993708</td>\n",
       "      <td>0.984015</td>\n",
       "      <td>0.216574</td>\n",
       "      <td>0.475820</td>\n",
       "      <td>0.573975</td>\n",
       "      <td>0.787424</td>\n",
       "      <td>0.243529</td>\n",
       "      <td>0.093492</td>\n",
       "      <td>-0.776454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst texture</th>\n",
       "      <td>0.297008</td>\n",
       "      <td>0.912045</td>\n",
       "      <td>0.303038</td>\n",
       "      <td>0.287489</td>\n",
       "      <td>0.036072</td>\n",
       "      <td>0.248133</td>\n",
       "      <td>0.299879</td>\n",
       "      <td>0.292752</td>\n",
       "      <td>0.090651</td>\n",
       "      <td>-0.051269</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.365098</td>\n",
       "      <td>0.345842</td>\n",
       "      <td>0.225429</td>\n",
       "      <td>0.360832</td>\n",
       "      <td>0.368366</td>\n",
       "      <td>0.359755</td>\n",
       "      <td>0.233027</td>\n",
       "      <td>0.219122</td>\n",
       "      <td>-0.456903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst perimeter</th>\n",
       "      <td>0.965137</td>\n",
       "      <td>0.358040</td>\n",
       "      <td>0.970387</td>\n",
       "      <td>0.959120</td>\n",
       "      <td>0.238853</td>\n",
       "      <td>0.590210</td>\n",
       "      <td>0.729565</td>\n",
       "      <td>0.855923</td>\n",
       "      <td>0.219169</td>\n",
       "      <td>-0.205151</td>\n",
       "      <td>...</td>\n",
       "      <td>0.365098</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.977578</td>\n",
       "      <td>0.236775</td>\n",
       "      <td>0.529408</td>\n",
       "      <td>0.618344</td>\n",
       "      <td>0.816322</td>\n",
       "      <td>0.269493</td>\n",
       "      <td>0.138957</td>\n",
       "      <td>-0.782914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst area</th>\n",
       "      <td>0.941082</td>\n",
       "      <td>0.343546</td>\n",
       "      <td>0.941550</td>\n",
       "      <td>0.959213</td>\n",
       "      <td>0.206718</td>\n",
       "      <td>0.509604</td>\n",
       "      <td>0.675987</td>\n",
       "      <td>0.809630</td>\n",
       "      <td>0.177193</td>\n",
       "      <td>-0.231854</td>\n",
       "      <td>...</td>\n",
       "      <td>0.345842</td>\n",
       "      <td>0.977578</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.209145</td>\n",
       "      <td>0.438296</td>\n",
       "      <td>0.543331</td>\n",
       "      <td>0.747419</td>\n",
       "      <td>0.209146</td>\n",
       "      <td>0.079647</td>\n",
       "      <td>-0.733825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst smoothness</th>\n",
       "      <td>0.119616</td>\n",
       "      <td>0.077503</td>\n",
       "      <td>0.150549</td>\n",
       "      <td>0.123523</td>\n",
       "      <td>0.805324</td>\n",
       "      <td>0.565541</td>\n",
       "      <td>0.448822</td>\n",
       "      <td>0.452753</td>\n",
       "      <td>0.426675</td>\n",
       "      <td>0.504942</td>\n",
       "      <td>...</td>\n",
       "      <td>0.225429</td>\n",
       "      <td>0.236775</td>\n",
       "      <td>0.209145</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.568187</td>\n",
       "      <td>0.518523</td>\n",
       "      <td>0.547691</td>\n",
       "      <td>0.493838</td>\n",
       "      <td>0.617624</td>\n",
       "      <td>-0.421465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst compactness</th>\n",
       "      <td>0.413463</td>\n",
       "      <td>0.277830</td>\n",
       "      <td>0.455774</td>\n",
       "      <td>0.390410</td>\n",
       "      <td>0.472468</td>\n",
       "      <td>0.865809</td>\n",
       "      <td>0.754968</td>\n",
       "      <td>0.667454</td>\n",
       "      <td>0.473200</td>\n",
       "      <td>0.458798</td>\n",
       "      <td>...</td>\n",
       "      <td>0.360832</td>\n",
       "      <td>0.529408</td>\n",
       "      <td>0.438296</td>\n",
       "      <td>0.568187</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.892261</td>\n",
       "      <td>0.801080</td>\n",
       "      <td>0.614441</td>\n",
       "      <td>0.810455</td>\n",
       "      <td>-0.590998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst concavity</th>\n",
       "      <td>0.526911</td>\n",
       "      <td>0.301025</td>\n",
       "      <td>0.563879</td>\n",
       "      <td>0.512606</td>\n",
       "      <td>0.434926</td>\n",
       "      <td>0.816275</td>\n",
       "      <td>0.884103</td>\n",
       "      <td>0.752399</td>\n",
       "      <td>0.433721</td>\n",
       "      <td>0.346234</td>\n",
       "      <td>...</td>\n",
       "      <td>0.368366</td>\n",
       "      <td>0.618344</td>\n",
       "      <td>0.543331</td>\n",
       "      <td>0.518523</td>\n",
       "      <td>0.892261</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.855434</td>\n",
       "      <td>0.532520</td>\n",
       "      <td>0.686511</td>\n",
       "      <td>-0.659610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst concave points</th>\n",
       "      <td>0.744214</td>\n",
       "      <td>0.295316</td>\n",
       "      <td>0.771241</td>\n",
       "      <td>0.722017</td>\n",
       "      <td>0.503053</td>\n",
       "      <td>0.815573</td>\n",
       "      <td>0.861323</td>\n",
       "      <td>0.910155</td>\n",
       "      <td>0.430297</td>\n",
       "      <td>0.175325</td>\n",
       "      <td>...</td>\n",
       "      <td>0.359755</td>\n",
       "      <td>0.816322</td>\n",
       "      <td>0.747419</td>\n",
       "      <td>0.547691</td>\n",
       "      <td>0.801080</td>\n",
       "      <td>0.855434</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.502528</td>\n",
       "      <td>0.511114</td>\n",
       "      <td>-0.793566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst symmetry</th>\n",
       "      <td>0.163953</td>\n",
       "      <td>0.105008</td>\n",
       "      <td>0.189115</td>\n",
       "      <td>0.143570</td>\n",
       "      <td>0.394309</td>\n",
       "      <td>0.510223</td>\n",
       "      <td>0.409464</td>\n",
       "      <td>0.375744</td>\n",
       "      <td>0.699826</td>\n",
       "      <td>0.334019</td>\n",
       "      <td>...</td>\n",
       "      <td>0.233027</td>\n",
       "      <td>0.269493</td>\n",
       "      <td>0.209146</td>\n",
       "      <td>0.493838</td>\n",
       "      <td>0.614441</td>\n",
       "      <td>0.532520</td>\n",
       "      <td>0.502528</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.537848</td>\n",
       "      <td>-0.416294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <td>0.007066</td>\n",
       "      <td>0.119205</td>\n",
       "      <td>0.051019</td>\n",
       "      <td>0.003738</td>\n",
       "      <td>0.499316</td>\n",
       "      <td>0.687382</td>\n",
       "      <td>0.514930</td>\n",
       "      <td>0.368661</td>\n",
       "      <td>0.438413</td>\n",
       "      <td>0.767297</td>\n",
       "      <td>...</td>\n",
       "      <td>0.219122</td>\n",
       "      <td>0.138957</td>\n",
       "      <td>0.079647</td>\n",
       "      <td>0.617624</td>\n",
       "      <td>0.810455</td>\n",
       "      <td>0.686511</td>\n",
       "      <td>0.511114</td>\n",
       "      <td>0.537848</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.323872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.730029</td>\n",
       "      <td>-0.415185</td>\n",
       "      <td>-0.742636</td>\n",
       "      <td>-0.708984</td>\n",
       "      <td>-0.358560</td>\n",
       "      <td>-0.596534</td>\n",
       "      <td>-0.696360</td>\n",
       "      <td>-0.776614</td>\n",
       "      <td>-0.330499</td>\n",
       "      <td>0.012838</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.456903</td>\n",
       "      <td>-0.782914</td>\n",
       "      <td>-0.733825</td>\n",
       "      <td>-0.421465</td>\n",
       "      <td>-0.590998</td>\n",
       "      <td>-0.659610</td>\n",
       "      <td>-0.793566</td>\n",
       "      <td>-0.416294</td>\n",
       "      <td>-0.323872</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         mean radius  mean texture  mean perimeter  mean area  \\\n",
       "mean radius                 1.000000      0.323782        0.997855   0.987357   \n",
       "mean texture                0.323782      1.000000        0.329533   0.321086   \n",
       "mean perimeter              0.997855      0.329533        1.000000   0.986507   \n",
       "mean area                   0.987357      0.321086        0.986507   1.000000   \n",
       "mean smoothness             0.170581     -0.023389        0.207278   0.177028   \n",
       "mean compactness            0.506124      0.236702        0.556936   0.498502   \n",
       "mean concavity              0.676764      0.302418        0.716136   0.685983   \n",
       "mean concave points         0.822529      0.293464        0.850977   0.823269   \n",
       "mean symmetry               0.147741      0.071401        0.183027   0.151293   \n",
       "mean fractal dimension     -0.311631     -0.076437       -0.261477  -0.283110   \n",
       "radius error                0.679090      0.275869        0.691765   0.732562   \n",
       "texture error              -0.097317      0.386358       -0.086761  -0.066280   \n",
       "perimeter error             0.674172      0.281673        0.693135   0.726628   \n",
       "area error                  0.735864      0.259845        0.744983   0.800086   \n",
       "smoothness error           -0.222600      0.006614       -0.202694  -0.166777   \n",
       "compactness error           0.206000      0.191975        0.250744   0.212583   \n",
       "concavity error             0.194204      0.143293        0.228082   0.207660   \n",
       "concave points error        0.376169      0.163851        0.407217   0.372320   \n",
       "symmetry error             -0.104321      0.009127       -0.081629  -0.072497   \n",
       "fractal dimension error    -0.042641      0.054458       -0.005523  -0.019887   \n",
       "worst radius                0.969539      0.352573        0.969476   0.962746   \n",
       "worst texture               0.297008      0.912045        0.303038   0.287489   \n",
       "worst perimeter             0.965137      0.358040        0.970387   0.959120   \n",
       "worst area                  0.941082      0.343546        0.941550   0.959213   \n",
       "worst smoothness            0.119616      0.077503        0.150549   0.123523   \n",
       "worst compactness           0.413463      0.277830        0.455774   0.390410   \n",
       "worst concavity             0.526911      0.301025        0.563879   0.512606   \n",
       "worst concave points        0.744214      0.295316        0.771241   0.722017   \n",
       "worst symmetry              0.163953      0.105008        0.189115   0.143570   \n",
       "worst fractal dimension     0.007066      0.119205        0.051019   0.003738   \n",
       "target                     -0.730029     -0.415185       -0.742636  -0.708984   \n",
       "\n",
       "                         mean smoothness  mean compactness  mean concavity  \\\n",
       "mean radius                     0.170581          0.506124        0.676764   \n",
       "mean texture                   -0.023389          0.236702        0.302418   \n",
       "mean perimeter                  0.207278          0.556936        0.716136   \n",
       "mean area                       0.177028          0.498502        0.685983   \n",
       "mean smoothness                 1.000000          0.659123        0.521984   \n",
       "mean compactness                0.659123          1.000000        0.883121   \n",
       "mean concavity                  0.521984          0.883121        1.000000   \n",
       "mean concave points             0.553695          0.831135        0.921391   \n",
       "mean symmetry                   0.557775          0.602641        0.500667   \n",
       "mean fractal dimension          0.584792          0.565369        0.336783   \n",
       "radius error                    0.301467          0.497473        0.631925   \n",
       "texture error                   0.068406          0.046205        0.076218   \n",
       "perimeter error                 0.296092          0.548905        0.660391   \n",
       "area error                      0.246552          0.455653        0.617427   \n",
       "smoothness error                0.332375          0.135299        0.098564   \n",
       "compactness error               0.318943          0.738722        0.670279   \n",
       "concavity error                 0.248396          0.570517        0.691270   \n",
       "concave points error            0.380676          0.642262        0.683260   \n",
       "symmetry error                  0.200774          0.229977        0.178009   \n",
       "fractal dimension error         0.283607          0.507318        0.449301   \n",
       "worst radius                    0.213120          0.535315        0.688236   \n",
       "worst texture                   0.036072          0.248133        0.299879   \n",
       "worst perimeter                 0.238853          0.590210        0.729565   \n",
       "worst area                      0.206718          0.509604        0.675987   \n",
       "worst smoothness                0.805324          0.565541        0.448822   \n",
       "worst compactness               0.472468          0.865809        0.754968   \n",
       "worst concavity                 0.434926          0.816275        0.884103   \n",
       "worst concave points            0.503053          0.815573        0.861323   \n",
       "worst symmetry                  0.394309          0.510223        0.409464   \n",
       "worst fractal dimension         0.499316          0.687382        0.514930   \n",
       "target                         -0.358560         -0.596534       -0.696360   \n",
       "\n",
       "                         mean concave points  mean symmetry  \\\n",
       "mean radius                         0.822529       0.147741   \n",
       "mean texture                        0.293464       0.071401   \n",
       "mean perimeter                      0.850977       0.183027   \n",
       "mean area                           0.823269       0.151293   \n",
       "mean smoothness                     0.553695       0.557775   \n",
       "mean compactness                    0.831135       0.602641   \n",
       "mean concavity                      0.921391       0.500667   \n",
       "mean concave points                 1.000000       0.462497   \n",
       "mean symmetry                       0.462497       1.000000   \n",
       "mean fractal dimension              0.166917       0.479921   \n",
       "radius error                        0.698050       0.303379   \n",
       "texture error                       0.021480       0.128053   \n",
       "perimeter error                     0.710650       0.313893   \n",
       "area error                          0.690299       0.223970   \n",
       "smoothness error                    0.027653       0.187321   \n",
       "compactness error                   0.490424       0.421659   \n",
       "concavity error                     0.439167       0.342627   \n",
       "concave points error                0.615634       0.393298   \n",
       "symmetry error                      0.095351       0.449137   \n",
       "fractal dimension error             0.257584       0.331786   \n",
       "worst radius                        0.830318       0.185728   \n",
       "worst texture                       0.292752       0.090651   \n",
       "worst perimeter                     0.855923       0.219169   \n",
       "worst area                          0.809630       0.177193   \n",
       "worst smoothness                    0.452753       0.426675   \n",
       "worst compactness                   0.667454       0.473200   \n",
       "worst concavity                     0.752399       0.433721   \n",
       "worst concave points                0.910155       0.430297   \n",
       "worst symmetry                      0.375744       0.699826   \n",
       "worst fractal dimension             0.368661       0.438413   \n",
       "target                             -0.776614      -0.330499   \n",
       "\n",
       "                         mean fractal dimension  ...  worst texture  \\\n",
       "mean radius                           -0.311631  ...       0.297008   \n",
       "mean texture                          -0.076437  ...       0.912045   \n",
       "mean perimeter                        -0.261477  ...       0.303038   \n",
       "mean area                             -0.283110  ...       0.287489   \n",
       "mean smoothness                        0.584792  ...       0.036072   \n",
       "mean compactness                       0.565369  ...       0.248133   \n",
       "mean concavity                         0.336783  ...       0.299879   \n",
       "mean concave points                    0.166917  ...       0.292752   \n",
       "mean symmetry                          0.479921  ...       0.090651   \n",
       "mean fractal dimension                 1.000000  ...      -0.051269   \n",
       "radius error                           0.000111  ...       0.194799   \n",
       "texture error                          0.164174  ...       0.409003   \n",
       "perimeter error                        0.039830  ...       0.200371   \n",
       "area error                            -0.090170  ...       0.196497   \n",
       "smoothness error                       0.401964  ...      -0.074743   \n",
       "compactness error                      0.559837  ...       0.143003   \n",
       "concavity error                        0.446630  ...       0.100241   \n",
       "concave points error                   0.341198  ...       0.086741   \n",
       "symmetry error                         0.345007  ...      -0.077473   \n",
       "fractal dimension error                0.688132  ...      -0.003195   \n",
       "worst radius                          -0.253691  ...       0.359921   \n",
       "worst texture                         -0.051269  ...       1.000000   \n",
       "worst perimeter                       -0.205151  ...       0.365098   \n",
       "worst area                            -0.231854  ...       0.345842   \n",
       "worst smoothness                       0.504942  ...       0.225429   \n",
       "worst compactness                      0.458798  ...       0.360832   \n",
       "worst concavity                        0.346234  ...       0.368366   \n",
       "worst concave points                   0.175325  ...       0.359755   \n",
       "worst symmetry                         0.334019  ...       0.233027   \n",
       "worst fractal dimension                0.767297  ...       0.219122   \n",
       "target                                 0.012838  ...      -0.456903   \n",
       "\n",
       "                         worst perimeter  worst area  worst smoothness  \\\n",
       "mean radius                     0.965137    0.941082          0.119616   \n",
       "mean texture                    0.358040    0.343546          0.077503   \n",
       "mean perimeter                  0.970387    0.941550          0.150549   \n",
       "mean area                       0.959120    0.959213          0.123523   \n",
       "mean smoothness                 0.238853    0.206718          0.805324   \n",
       "mean compactness                0.590210    0.509604          0.565541   \n",
       "mean concavity                  0.729565    0.675987          0.448822   \n",
       "mean concave points             0.855923    0.809630          0.452753   \n",
       "mean symmetry                   0.219169    0.177193          0.426675   \n",
       "mean fractal dimension         -0.205151   -0.231854          0.504942   \n",
       "radius error                    0.719684    0.751548          0.141919   \n",
       "texture error                  -0.102242   -0.083195         -0.073658   \n",
       "perimeter error                 0.721031    0.730713          0.130054   \n",
       "area error                      0.761213    0.811408          0.125389   \n",
       "smoothness error               -0.217304   -0.182195          0.314457   \n",
       "compactness error               0.260516    0.199371          0.227394   \n",
       "concavity error                 0.226680    0.188353          0.168481   \n",
       "concave points error            0.394999    0.342271          0.215351   \n",
       "symmetry error                 -0.103753   -0.110343         -0.012662   \n",
       "fractal dimension error        -0.001000   -0.022736          0.170568   \n",
       "worst radius                    0.993708    0.984015          0.216574   \n",
       "worst texture                   0.365098    0.345842          0.225429   \n",
       "worst perimeter                 1.000000    0.977578          0.236775   \n",
       "worst area                      0.977578    1.000000          0.209145   \n",
       "worst smoothness                0.236775    0.209145          1.000000   \n",
       "worst compactness               0.529408    0.438296          0.568187   \n",
       "worst concavity                 0.618344    0.543331          0.518523   \n",
       "worst concave points            0.816322    0.747419          0.547691   \n",
       "worst symmetry                  0.269493    0.209146          0.493838   \n",
       "worst fractal dimension         0.138957    0.079647          0.617624   \n",
       "target                         -0.782914   -0.733825         -0.421465   \n",
       "\n",
       "                         worst compactness  worst concavity  \\\n",
       "mean radius                       0.413463         0.526911   \n",
       "mean texture                      0.277830         0.301025   \n",
       "mean perimeter                    0.455774         0.563879   \n",
       "mean area                         0.390410         0.512606   \n",
       "mean smoothness                   0.472468         0.434926   \n",
       "mean compactness                  0.865809         0.816275   \n",
       "mean concavity                    0.754968         0.884103   \n",
       "mean concave points               0.667454         0.752399   \n",
       "mean symmetry                     0.473200         0.433721   \n",
       "mean fractal dimension            0.458798         0.346234   \n",
       "radius error                      0.287103         0.380585   \n",
       "texture error                    -0.092439        -0.068956   \n",
       "perimeter error                   0.341919         0.418899   \n",
       "area error                        0.283257         0.385100   \n",
       "smoothness error                 -0.055558        -0.058298   \n",
       "compactness error                 0.678780         0.639147   \n",
       "concavity error                   0.484858         0.662564   \n",
       "concave points error              0.452888         0.549592   \n",
       "symmetry error                    0.060255         0.037119   \n",
       "fractal dimension error           0.390159         0.379975   \n",
       "worst radius                      0.475820         0.573975   \n",
       "worst texture                     0.360832         0.368366   \n",
       "worst perimeter                   0.529408         0.618344   \n",
       "worst area                        0.438296         0.543331   \n",
       "worst smoothness                  0.568187         0.518523   \n",
       "worst compactness                 1.000000         0.892261   \n",
       "worst concavity                   0.892261         1.000000   \n",
       "worst concave points              0.801080         0.855434   \n",
       "worst symmetry                    0.614441         0.532520   \n",
       "worst fractal dimension           0.810455         0.686511   \n",
       "target                           -0.590998        -0.659610   \n",
       "\n",
       "                         worst concave points  worst symmetry  \\\n",
       "mean radius                          0.744214        0.163953   \n",
       "mean texture                         0.295316        0.105008   \n",
       "mean perimeter                       0.771241        0.189115   \n",
       "mean area                            0.722017        0.143570   \n",
       "mean smoothness                      0.503053        0.394309   \n",
       "mean compactness                     0.815573        0.510223   \n",
       "mean concavity                       0.861323        0.409464   \n",
       "mean concave points                  0.910155        0.375744   \n",
       "mean symmetry                        0.430297        0.699826   \n",
       "mean fractal dimension               0.175325        0.334019   \n",
       "radius error                         0.531062        0.094543   \n",
       "texture error                       -0.119638       -0.128215   \n",
       "perimeter error                      0.554897        0.109930   \n",
       "area error                           0.538166        0.074126   \n",
       "smoothness error                    -0.102007       -0.107342   \n",
       "compactness error                    0.483208        0.277878   \n",
       "concavity error                      0.440472        0.197788   \n",
       "concave points error                 0.602450        0.143116   \n",
       "symmetry error                      -0.030413        0.389402   \n",
       "fractal dimension error              0.215204        0.111094   \n",
       "worst radius                         0.787424        0.243529   \n",
       "worst texture                        0.359755        0.233027   \n",
       "worst perimeter                      0.816322        0.269493   \n",
       "worst area                           0.747419        0.209146   \n",
       "worst smoothness                     0.547691        0.493838   \n",
       "worst compactness                    0.801080        0.614441   \n",
       "worst concavity                      0.855434        0.532520   \n",
       "worst concave points                 1.000000        0.502528   \n",
       "worst symmetry                       0.502528        1.000000   \n",
       "worst fractal dimension              0.511114        0.537848   \n",
       "target                              -0.793566       -0.416294   \n",
       "\n",
       "                         worst fractal dimension    target  \n",
       "mean radius                             0.007066 -0.730029  \n",
       "mean texture                            0.119205 -0.415185  \n",
       "mean perimeter                          0.051019 -0.742636  \n",
       "mean area                               0.003738 -0.708984  \n",
       "mean smoothness                         0.499316 -0.358560  \n",
       "mean compactness                        0.687382 -0.596534  \n",
       "mean concavity                          0.514930 -0.696360  \n",
       "mean concave points                     0.368661 -0.776614  \n",
       "mean symmetry                           0.438413 -0.330499  \n",
       "mean fractal dimension                  0.767297  0.012838  \n",
       "radius error                            0.049559 -0.567134  \n",
       "texture error                          -0.045655  0.008303  \n",
       "perimeter error                         0.085433 -0.556141  \n",
       "area error                              0.017539 -0.548236  \n",
       "smoothness error                        0.101480  0.067016  \n",
       "compactness error                       0.590973 -0.292999  \n",
       "concavity error                         0.439329 -0.253730  \n",
       "concave points error                    0.310655 -0.408042  \n",
       "symmetry error                          0.078079  0.006522  \n",
       "fractal dimension error                 0.591328 -0.077972  \n",
       "worst radius                            0.093492 -0.776454  \n",
       "worst texture                           0.219122 -0.456903  \n",
       "worst perimeter                         0.138957 -0.782914  \n",
       "worst area                              0.079647 -0.733825  \n",
       "worst smoothness                        0.617624 -0.421465  \n",
       "worst compactness                       0.810455 -0.590998  \n",
       "worst concavity                         0.686511 -0.659610  \n",
       "worst concave points                    0.511114 -0.793566  \n",
       "worst symmetry                          0.537848 -0.416294  \n",
       "worst fractal dimension                 1.000000 -0.323872  \n",
       "target                                 -0.323872  1.000000  \n",
       "\n",
       "[31 rows x 31 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap of Correlation matrix of breast cancer DataFrame\n",
    "plt.figure(figsize=(20,20))\n",
    "sns.heatmap(cancer_df.corr(), annot = True, cmap ='coolwarm', linewidths=2) # *** img 9 ***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation Barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of 'cancer_df2' is :  (569, 30)\n"
     ]
    }
   ],
   "source": [
    "# create second DataFrame by droping target\n",
    "cancer_df2 = cancer_df.drop(['target'], axis = 1)\n",
    "print(\"The shape of 'cancer_df2' is : \", cancer_df2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#cancer_df2.corrwith(cancer_df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize correlation barplot\n",
    "plt.figure(figsize=(16, 5))\n",
    "\n",
    "# Plot using the keyword arguments 'x' and 'y'\n",
    "ax = sns.barplot(x=cancer_df2.corrwith(cancer_df.target).index, y=cancer_df2.corrwith(cancer_df.target))\n",
    "\n",
    "# Adjust the label rotation for better visibility\n",
    "ax.tick_params(labelrotation=90)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "       'smoothness error', 'compactness error', 'concavity error',\n",
       "       'concave points error', 'symmetry error', 'fractal dimension error',\n",
       "       'worst radius', 'worst texture', 'worst perimeter', 'worst area',\n",
       "       'worst smoothness', 'worst compactness', 'worst concavity',\n",
       "       'worst concave points', 'worst symmetry', 'worst fractal dimension'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_df2.corrwith(cancer_df.target).index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split DatFrame in Train and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.45</td>\n",
       "      <td>15.70</td>\n",
       "      <td>82.57</td>\n",
       "      <td>477.1</td>\n",
       "      <td>0.12780</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>0.1578</td>\n",
       "      <td>0.08089</td>\n",
       "      <td>0.2087</td>\n",
       "      <td>0.07613</td>\n",
       "      <td>...</td>\n",
       "      <td>15.47</td>\n",
       "      <td>23.75</td>\n",
       "      <td>103.40</td>\n",
       "      <td>741.6</td>\n",
       "      <td>0.1791</td>\n",
       "      <td>0.5249</td>\n",
       "      <td>0.5355</td>\n",
       "      <td>0.1741</td>\n",
       "      <td>0.3985</td>\n",
       "      <td>0.12440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "5        12.45         15.70           82.57      477.1          0.12780   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "5           0.17000          0.1578              0.08089         0.2087   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "5                 0.07613  ...         15.47          23.75           103.40   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "5       741.6            0.1791             0.5249           0.5355   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "5                0.1741          0.3985                  0.12440  \n",
       "\n",
       "[6 rows x 30 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# input variable\n",
    "X = cancer_df.drop(['target'], axis = 1) \n",
    "X.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    0.0\n",
       "2    0.0\n",
       "3    0.0\n",
       "4    0.0\n",
       "5    0.0\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# output variable\n",
    "y = cancer_df['target'] \n",
    "y.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split dataset into train and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state= 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>13.200</td>\n",
       "      <td>15.82</td>\n",
       "      <td>84.07</td>\n",
       "      <td>537.3</td>\n",
       "      <td>0.08511</td>\n",
       "      <td>0.05251</td>\n",
       "      <td>0.001461</td>\n",
       "      <td>0.003261</td>\n",
       "      <td>0.1632</td>\n",
       "      <td>0.05894</td>\n",
       "      <td>...</td>\n",
       "      <td>14.41</td>\n",
       "      <td>20.45</td>\n",
       "      <td>92.00</td>\n",
       "      <td>636.9</td>\n",
       "      <td>0.11280</td>\n",
       "      <td>0.1346</td>\n",
       "      <td>0.01120</td>\n",
       "      <td>0.02500</td>\n",
       "      <td>0.2651</td>\n",
       "      <td>0.08385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>11.360</td>\n",
       "      <td>17.57</td>\n",
       "      <td>72.49</td>\n",
       "      <td>399.8</td>\n",
       "      <td>0.08858</td>\n",
       "      <td>0.05313</td>\n",
       "      <td>0.027830</td>\n",
       "      <td>0.021000</td>\n",
       "      <td>0.1601</td>\n",
       "      <td>0.05913</td>\n",
       "      <td>...</td>\n",
       "      <td>13.05</td>\n",
       "      <td>36.32</td>\n",
       "      <td>85.07</td>\n",
       "      <td>521.3</td>\n",
       "      <td>0.14530</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.18110</td>\n",
       "      <td>0.08698</td>\n",
       "      <td>0.2973</td>\n",
       "      <td>0.07745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>18.080</td>\n",
       "      <td>21.84</td>\n",
       "      <td>117.40</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>0.07371</td>\n",
       "      <td>0.08642</td>\n",
       "      <td>0.110300</td>\n",
       "      <td>0.057780</td>\n",
       "      <td>0.1770</td>\n",
       "      <td>0.05340</td>\n",
       "      <td>...</td>\n",
       "      <td>19.76</td>\n",
       "      <td>24.70</td>\n",
       "      <td>129.10</td>\n",
       "      <td>1228.0</td>\n",
       "      <td>0.08822</td>\n",
       "      <td>0.1963</td>\n",
       "      <td>0.25350</td>\n",
       "      <td>0.09181</td>\n",
       "      <td>0.2369</td>\n",
       "      <td>0.06558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>10.570</td>\n",
       "      <td>20.22</td>\n",
       "      <td>70.15</td>\n",
       "      <td>338.3</td>\n",
       "      <td>0.09073</td>\n",
       "      <td>0.16600</td>\n",
       "      <td>0.228000</td>\n",
       "      <td>0.059410</td>\n",
       "      <td>0.2188</td>\n",
       "      <td>0.08450</td>\n",
       "      <td>...</td>\n",
       "      <td>10.85</td>\n",
       "      <td>22.82</td>\n",
       "      <td>76.51</td>\n",
       "      <td>351.9</td>\n",
       "      <td>0.11430</td>\n",
       "      <td>0.3619</td>\n",
       "      <td>0.60300</td>\n",
       "      <td>0.14650</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>19.400</td>\n",
       "      <td>23.50</td>\n",
       "      <td>129.10</td>\n",
       "      <td>1155.0</td>\n",
       "      <td>0.10270</td>\n",
       "      <td>0.15580</td>\n",
       "      <td>0.204900</td>\n",
       "      <td>0.088860</td>\n",
       "      <td>0.1978</td>\n",
       "      <td>0.06000</td>\n",
       "      <td>...</td>\n",
       "      <td>21.65</td>\n",
       "      <td>30.53</td>\n",
       "      <td>144.90</td>\n",
       "      <td>1417.0</td>\n",
       "      <td>0.14630</td>\n",
       "      <td>0.2968</td>\n",
       "      <td>0.34580</td>\n",
       "      <td>0.15640</td>\n",
       "      <td>0.2920</td>\n",
       "      <td>0.07614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13.000</td>\n",
       "      <td>21.82</td>\n",
       "      <td>87.50</td>\n",
       "      <td>519.8</td>\n",
       "      <td>0.12730</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>0.185900</td>\n",
       "      <td>0.093530</td>\n",
       "      <td>0.2350</td>\n",
       "      <td>0.07389</td>\n",
       "      <td>...</td>\n",
       "      <td>15.49</td>\n",
       "      <td>30.73</td>\n",
       "      <td>106.20</td>\n",
       "      <td>739.3</td>\n",
       "      <td>0.17030</td>\n",
       "      <td>0.5401</td>\n",
       "      <td>0.53900</td>\n",
       "      <td>0.20600</td>\n",
       "      <td>0.4378</td>\n",
       "      <td>0.10720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>13.800</td>\n",
       "      <td>15.79</td>\n",
       "      <td>90.43</td>\n",
       "      <td>584.1</td>\n",
       "      <td>0.10070</td>\n",
       "      <td>0.12800</td>\n",
       "      <td>0.077890</td>\n",
       "      <td>0.050690</td>\n",
       "      <td>0.1662</td>\n",
       "      <td>0.06566</td>\n",
       "      <td>...</td>\n",
       "      <td>16.57</td>\n",
       "      <td>20.86</td>\n",
       "      <td>110.30</td>\n",
       "      <td>812.4</td>\n",
       "      <td>0.14110</td>\n",
       "      <td>0.3542</td>\n",
       "      <td>0.27790</td>\n",
       "      <td>0.13830</td>\n",
       "      <td>0.2589</td>\n",
       "      <td>0.10300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>17.910</td>\n",
       "      <td>21.02</td>\n",
       "      <td>124.40</td>\n",
       "      <td>994.0</td>\n",
       "      <td>0.12300</td>\n",
       "      <td>0.25760</td>\n",
       "      <td>0.318900</td>\n",
       "      <td>0.119800</td>\n",
       "      <td>0.2113</td>\n",
       "      <td>0.07115</td>\n",
       "      <td>...</td>\n",
       "      <td>20.80</td>\n",
       "      <td>27.78</td>\n",
       "      <td>149.60</td>\n",
       "      <td>1304.0</td>\n",
       "      <td>0.18730</td>\n",
       "      <td>0.5917</td>\n",
       "      <td>0.90340</td>\n",
       "      <td>0.19640</td>\n",
       "      <td>0.3245</td>\n",
       "      <td>0.11980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>15.780</td>\n",
       "      <td>22.91</td>\n",
       "      <td>105.70</td>\n",
       "      <td>782.6</td>\n",
       "      <td>0.11550</td>\n",
       "      <td>0.17520</td>\n",
       "      <td>0.213300</td>\n",
       "      <td>0.094790</td>\n",
       "      <td>0.2096</td>\n",
       "      <td>0.07331</td>\n",
       "      <td>...</td>\n",
       "      <td>20.19</td>\n",
       "      <td>30.50</td>\n",
       "      <td>130.30</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>0.18550</td>\n",
       "      <td>0.4925</td>\n",
       "      <td>0.73560</td>\n",
       "      <td>0.20340</td>\n",
       "      <td>0.3274</td>\n",
       "      <td>0.12520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>9.876</td>\n",
       "      <td>17.27</td>\n",
       "      <td>62.92</td>\n",
       "      <td>295.4</td>\n",
       "      <td>0.10890</td>\n",
       "      <td>0.07232</td>\n",
       "      <td>0.017560</td>\n",
       "      <td>0.019520</td>\n",
       "      <td>0.1934</td>\n",
       "      <td>0.06285</td>\n",
       "      <td>...</td>\n",
       "      <td>10.42</td>\n",
       "      <td>23.22</td>\n",
       "      <td>67.08</td>\n",
       "      <td>331.6</td>\n",
       "      <td>0.14150</td>\n",
       "      <td>0.1247</td>\n",
       "      <td>0.06213</td>\n",
       "      <td>0.05588</td>\n",
       "      <td>0.2989</td>\n",
       "      <td>0.07380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>455 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "306       13.200         15.82           84.07      537.3          0.08511   \n",
       "410       11.360         17.57           72.49      399.8          0.08858   \n",
       "197       18.080         21.84          117.40     1024.0          0.07371   \n",
       "376       10.570         20.22           70.15      338.3          0.09073   \n",
       "244       19.400         23.50          129.10     1155.0          0.10270   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "8         13.000         21.82           87.50      519.8          0.12730   \n",
       "73        13.800         15.79           90.43      584.1          0.10070   \n",
       "400       17.910         21.02          124.40      994.0          0.12300   \n",
       "118       15.780         22.91          105.70      782.6          0.11550   \n",
       "206        9.876         17.27           62.92      295.4          0.10890   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "306           0.05251        0.001461             0.003261         0.1632   \n",
       "410           0.05313        0.027830             0.021000         0.1601   \n",
       "197           0.08642        0.110300             0.057780         0.1770   \n",
       "376           0.16600        0.228000             0.059410         0.2188   \n",
       "244           0.15580        0.204900             0.088860         0.1978   \n",
       "..                ...             ...                  ...            ...   \n",
       "8             0.19320        0.185900             0.093530         0.2350   \n",
       "73            0.12800        0.077890             0.050690         0.1662   \n",
       "400           0.25760        0.318900             0.119800         0.2113   \n",
       "118           0.17520        0.213300             0.094790         0.2096   \n",
       "206           0.07232        0.017560             0.019520         0.1934   \n",
       "\n",
       "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
       "306                 0.05894  ...         14.41          20.45   \n",
       "410                 0.05913  ...         13.05          36.32   \n",
       "197                 0.05340  ...         19.76          24.70   \n",
       "376                 0.08450  ...         10.85          22.82   \n",
       "244                 0.06000  ...         21.65          30.53   \n",
       "..                      ...  ...           ...            ...   \n",
       "8                   0.07389  ...         15.49          30.73   \n",
       "73                  0.06566  ...         16.57          20.86   \n",
       "400                 0.07115  ...         20.80          27.78   \n",
       "118                 0.07331  ...         20.19          30.50   \n",
       "206                 0.06285  ...         10.42          23.22   \n",
       "\n",
       "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
       "306            92.00       636.9           0.11280             0.1346   \n",
       "410            85.07       521.3           0.14530             0.1622   \n",
       "197           129.10      1228.0           0.08822             0.1963   \n",
       "376            76.51       351.9           0.11430             0.3619   \n",
       "244           144.90      1417.0           0.14630             0.2968   \n",
       "..               ...         ...               ...                ...   \n",
       "8             106.20       739.3           0.17030             0.5401   \n",
       "73            110.30       812.4           0.14110             0.3542   \n",
       "400           149.60      1304.0           0.18730             0.5917   \n",
       "118           130.30      1272.0           0.18550             0.4925   \n",
       "206            67.08       331.6           0.14150             0.1247   \n",
       "\n",
       "     worst concavity  worst concave points  worst symmetry  \\\n",
       "306          0.01120               0.02500          0.2651   \n",
       "410          0.18110               0.08698          0.2973   \n",
       "197          0.25350               0.09181          0.2369   \n",
       "376          0.60300               0.14650          0.2597   \n",
       "244          0.34580               0.15640          0.2920   \n",
       "..               ...                   ...             ...   \n",
       "8            0.53900               0.20600          0.4378   \n",
       "73           0.27790               0.13830          0.2589   \n",
       "400          0.90340               0.19640          0.3245   \n",
       "118          0.73560               0.20340          0.3274   \n",
       "206          0.06213               0.05588          0.2989   \n",
       "\n",
       "     worst fractal dimension  \n",
       "306                  0.08385  \n",
       "410                  0.07745  \n",
       "197                  0.06558  \n",
       "376                  0.12000  \n",
       "244                  0.07614  \n",
       "..                       ...  \n",
       "8                    0.10720  \n",
       "73                   0.10300  \n",
       "400                  0.11980  \n",
       "118                  0.12520  \n",
       "206                  0.07380  \n",
       "\n",
       "[455 rows x 30 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>15.30</td>\n",
       "      <td>25.27</td>\n",
       "      <td>102.40</td>\n",
       "      <td>732.4</td>\n",
       "      <td>0.10820</td>\n",
       "      <td>0.16970</td>\n",
       "      <td>0.16830</td>\n",
       "      <td>0.08751</td>\n",
       "      <td>0.1926</td>\n",
       "      <td>0.06540</td>\n",
       "      <td>...</td>\n",
       "      <td>20.27</td>\n",
       "      <td>36.71</td>\n",
       "      <td>149.30</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>0.1641</td>\n",
       "      <td>0.61100</td>\n",
       "      <td>0.63350</td>\n",
       "      <td>0.20240</td>\n",
       "      <td>0.4027</td>\n",
       "      <td>0.09876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>12.34</td>\n",
       "      <td>22.22</td>\n",
       "      <td>79.85</td>\n",
       "      <td>464.5</td>\n",
       "      <td>0.10120</td>\n",
       "      <td>0.10150</td>\n",
       "      <td>0.05370</td>\n",
       "      <td>0.02822</td>\n",
       "      <td>0.1551</td>\n",
       "      <td>0.06761</td>\n",
       "      <td>...</td>\n",
       "      <td>13.58</td>\n",
       "      <td>28.68</td>\n",
       "      <td>87.36</td>\n",
       "      <td>553.0</td>\n",
       "      <td>0.1452</td>\n",
       "      <td>0.23380</td>\n",
       "      <td>0.16880</td>\n",
       "      <td>0.08194</td>\n",
       "      <td>0.2268</td>\n",
       "      <td>0.09082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>14.50</td>\n",
       "      <td>10.89</td>\n",
       "      <td>94.28</td>\n",
       "      <td>640.7</td>\n",
       "      <td>0.11010</td>\n",
       "      <td>0.10990</td>\n",
       "      <td>0.08842</td>\n",
       "      <td>0.05778</td>\n",
       "      <td>0.1856</td>\n",
       "      <td>0.06402</td>\n",
       "      <td>...</td>\n",
       "      <td>15.70</td>\n",
       "      <td>15.98</td>\n",
       "      <td>102.80</td>\n",
       "      <td>745.5</td>\n",
       "      <td>0.1313</td>\n",
       "      <td>0.17880</td>\n",
       "      <td>0.25600</td>\n",
       "      <td>0.12210</td>\n",
       "      <td>0.2889</td>\n",
       "      <td>0.08006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>13.30</td>\n",
       "      <td>21.57</td>\n",
       "      <td>85.24</td>\n",
       "      <td>546.1</td>\n",
       "      <td>0.08582</td>\n",
       "      <td>0.06373</td>\n",
       "      <td>0.03344</td>\n",
       "      <td>0.02424</td>\n",
       "      <td>0.1815</td>\n",
       "      <td>0.05696</td>\n",
       "      <td>...</td>\n",
       "      <td>14.20</td>\n",
       "      <td>29.20</td>\n",
       "      <td>92.94</td>\n",
       "      <td>621.2</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.16670</td>\n",
       "      <td>0.12120</td>\n",
       "      <td>0.05614</td>\n",
       "      <td>0.2637</td>\n",
       "      <td>0.06658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>10.82</td>\n",
       "      <td>24.21</td>\n",
       "      <td>68.89</td>\n",
       "      <td>361.6</td>\n",
       "      <td>0.08192</td>\n",
       "      <td>0.06602</td>\n",
       "      <td>0.01548</td>\n",
       "      <td>0.00816</td>\n",
       "      <td>0.1976</td>\n",
       "      <td>0.06328</td>\n",
       "      <td>...</td>\n",
       "      <td>13.03</td>\n",
       "      <td>31.45</td>\n",
       "      <td>83.90</td>\n",
       "      <td>505.6</td>\n",
       "      <td>0.1204</td>\n",
       "      <td>0.16330</td>\n",
       "      <td>0.06194</td>\n",
       "      <td>0.03264</td>\n",
       "      <td>0.3059</td>\n",
       "      <td>0.07626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>15.13</td>\n",
       "      <td>29.81</td>\n",
       "      <td>96.71</td>\n",
       "      <td>719.5</td>\n",
       "      <td>0.08320</td>\n",
       "      <td>0.04605</td>\n",
       "      <td>0.04686</td>\n",
       "      <td>0.02739</td>\n",
       "      <td>0.1852</td>\n",
       "      <td>0.05294</td>\n",
       "      <td>...</td>\n",
       "      <td>17.26</td>\n",
       "      <td>36.91</td>\n",
       "      <td>110.10</td>\n",
       "      <td>931.4</td>\n",
       "      <td>0.1148</td>\n",
       "      <td>0.09866</td>\n",
       "      <td>0.15470</td>\n",
       "      <td>0.06575</td>\n",
       "      <td>0.3233</td>\n",
       "      <td>0.06165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>11.34</td>\n",
       "      <td>18.61</td>\n",
       "      <td>72.76</td>\n",
       "      <td>391.2</td>\n",
       "      <td>0.10490</td>\n",
       "      <td>0.08499</td>\n",
       "      <td>0.04302</td>\n",
       "      <td>0.02594</td>\n",
       "      <td>0.1927</td>\n",
       "      <td>0.06211</td>\n",
       "      <td>...</td>\n",
       "      <td>12.47</td>\n",
       "      <td>23.03</td>\n",
       "      <td>79.15</td>\n",
       "      <td>478.6</td>\n",
       "      <td>0.1483</td>\n",
       "      <td>0.15740</td>\n",
       "      <td>0.16240</td>\n",
       "      <td>0.08542</td>\n",
       "      <td>0.3060</td>\n",
       "      <td>0.06783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>18.31</td>\n",
       "      <td>18.58</td>\n",
       "      <td>118.60</td>\n",
       "      <td>1041.0</td>\n",
       "      <td>0.08588</td>\n",
       "      <td>0.08468</td>\n",
       "      <td>0.08169</td>\n",
       "      <td>0.05814</td>\n",
       "      <td>0.1621</td>\n",
       "      <td>0.05425</td>\n",
       "      <td>...</td>\n",
       "      <td>21.31</td>\n",
       "      <td>26.36</td>\n",
       "      <td>139.20</td>\n",
       "      <td>1410.0</td>\n",
       "      <td>0.1234</td>\n",
       "      <td>0.24450</td>\n",
       "      <td>0.35380</td>\n",
       "      <td>0.15710</td>\n",
       "      <td>0.3206</td>\n",
       "      <td>0.06938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.68690</td>\n",
       "      <td>0.25750</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>17.35</td>\n",
       "      <td>23.06</td>\n",
       "      <td>111.00</td>\n",
       "      <td>933.1</td>\n",
       "      <td>0.08662</td>\n",
       "      <td>0.06290</td>\n",
       "      <td>0.02891</td>\n",
       "      <td>0.02837</td>\n",
       "      <td>0.1564</td>\n",
       "      <td>0.05307</td>\n",
       "      <td>...</td>\n",
       "      <td>19.85</td>\n",
       "      <td>31.47</td>\n",
       "      <td>128.20</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>0.1240</td>\n",
       "      <td>0.14860</td>\n",
       "      <td>0.12110</td>\n",
       "      <td>0.08235</td>\n",
       "      <td>0.2452</td>\n",
       "      <td>0.06515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "28         15.30         25.27          102.40      732.4          0.10820   \n",
       "163        12.34         22.22           79.85      464.5          0.10120   \n",
       "123        14.50         10.89           94.28      640.7          0.11010   \n",
       "361        13.30         21.57           85.24      546.1          0.08582   \n",
       "549        10.82         24.21           68.89      361.6          0.08192   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "414        15.13         29.81           96.71      719.5          0.08320   \n",
       "515        11.34         18.61           72.76      391.2          0.10490   \n",
       "186        18.31         18.58          118.60     1041.0          0.08588   \n",
       "3          11.42         20.38           77.58      386.1          0.14250   \n",
       "261        17.35         23.06          111.00      933.1          0.08662   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "28            0.16970         0.16830              0.08751         0.1926   \n",
       "163           0.10150         0.05370              0.02822         0.1551   \n",
       "123           0.10990         0.08842              0.05778         0.1856   \n",
       "361           0.06373         0.03344              0.02424         0.1815   \n",
       "549           0.06602         0.01548              0.00816         0.1976   \n",
       "..                ...             ...                  ...            ...   \n",
       "414           0.04605         0.04686              0.02739         0.1852   \n",
       "515           0.08499         0.04302              0.02594         0.1927   \n",
       "186           0.08468         0.08169              0.05814         0.1621   \n",
       "3             0.28390         0.24140              0.10520         0.2597   \n",
       "261           0.06290         0.02891              0.02837         0.1564   \n",
       "\n",
       "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
       "28                  0.06540  ...         20.27          36.71   \n",
       "163                 0.06761  ...         13.58          28.68   \n",
       "123                 0.06402  ...         15.70          15.98   \n",
       "361                 0.05696  ...         14.20          29.20   \n",
       "549                 0.06328  ...         13.03          31.45   \n",
       "..                      ...  ...           ...            ...   \n",
       "414                 0.05294  ...         17.26          36.91   \n",
       "515                 0.06211  ...         12.47          23.03   \n",
       "186                 0.05425  ...         21.31          26.36   \n",
       "3                   0.09744  ...         14.91          26.50   \n",
       "261                 0.05307  ...         19.85          31.47   \n",
       "\n",
       "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
       "28            149.30      1269.0            0.1641            0.61100   \n",
       "163            87.36       553.0            0.1452            0.23380   \n",
       "123           102.80       745.5            0.1313            0.17880   \n",
       "361            92.94       621.2            0.1140            0.16670   \n",
       "549            83.90       505.6            0.1204            0.16330   \n",
       "..               ...         ...               ...                ...   \n",
       "414           110.10       931.4            0.1148            0.09866   \n",
       "515            79.15       478.6            0.1483            0.15740   \n",
       "186           139.20      1410.0            0.1234            0.24450   \n",
       "3              98.87       567.7            0.2098            0.86630   \n",
       "261           128.20      1218.0            0.1240            0.14860   \n",
       "\n",
       "     worst concavity  worst concave points  worst symmetry  \\\n",
       "28           0.63350               0.20240          0.4027   \n",
       "163          0.16880               0.08194          0.2268   \n",
       "123          0.25600               0.12210          0.2889   \n",
       "361          0.12120               0.05614          0.2637   \n",
       "549          0.06194               0.03264          0.3059   \n",
       "..               ...                   ...             ...   \n",
       "414          0.15470               0.06575          0.3233   \n",
       "515          0.16240               0.08542          0.3060   \n",
       "186          0.35380               0.15710          0.3206   \n",
       "3            0.68690               0.25750          0.6638   \n",
       "261          0.12110               0.08235          0.2452   \n",
       "\n",
       "     worst fractal dimension  \n",
       "28                   0.09876  \n",
       "163                  0.09082  \n",
       "123                  0.08006  \n",
       "361                  0.06658  \n",
       "549                  0.07626  \n",
       "..                       ...  \n",
       "414                  0.06165  \n",
       "515                  0.06783  \n",
       "186                  0.06938  \n",
       "3                    0.17300  \n",
       "261                  0.06515  \n",
       "\n",
       "[114 rows x 30 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "306    1.0\n",
       "410    1.0\n",
       "197    0.0\n",
       "376    1.0\n",
       "244    0.0\n",
       "      ... \n",
       "8      0.0\n",
       "73     0.0\n",
       "400    0.0\n",
       "118    0.0\n",
       "206    1.0\n",
       "Name: target, Length: 455, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28     0.0\n",
       "163    1.0\n",
       "123    1.0\n",
       "361    1.0\n",
       "549    1.0\n",
       "      ... \n",
       "414    0.0\n",
       "515    1.0\n",
       "186    0.0\n",
       "3      0.0\n",
       "261    0.0\n",
       "Name: target, Length: 114, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train_sc = sc.fit_transform(X_train)\n",
    "X_test_sc = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Suppor vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9385964912280702"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Support vector classifier\n",
    "from sklearn.svm import SVC\n",
    "svc_classifier = SVC()\n",
    "svc_classifier.fit(X_train, y_train)\n",
    "y_pred_scv = svc_classifier.predict(X_test)\n",
    "accuracy_score(y_test, y_pred_scv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9649122807017544"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train with Standard scaled Data\n",
    "svc_classifier2 = SVC()\n",
    "svc_classifier2.fit(X_train_sc, y_train)\n",
    "y_pred_svc_sc = svc_classifier2.predict(X_test_sc)\n",
    "accuracy_score(y_test, y_pred_svc_sc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy: 0.9649122807017544\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/soumyadip/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Use 'liblinear' solver for 'l1' penalty\n",
    "lr_classifier = LogisticRegression(random_state=51, penalty='l1', solver='liblinear')\n",
    "\n",
    "# Fit the model\n",
    "lr_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Predict on test data\n",
    "y_pred_lr = lr_classifier.predict(X_test)\n",
    "\n",
    "# Calculate and print accuracy score\n",
    "accuracy = accuracy_score(y_test, y_pred_lr)\n",
    "print(\"Logistic Regression Accuracy:\", accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression with Scaled Data Accuracy: 0.9824561403508771\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression with Standard Scaled Data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Use 'liblinear' solver for 'l1' penalty\n",
    "lr_classifier2 = LogisticRegression(random_state=51, penalty='l1', solver='liblinear')\n",
    "\n",
    "# Fit the model on scaled training data\n",
    "lr_classifier2.fit(X_train_sc, y_train)\n",
    "\n",
    "# Predict on scaled test data\n",
    "y_pred_lr_sc = lr_classifier2.predict(X_test_sc)\n",
    "\n",
    "# Calculate and print accuracy score\n",
    "accuracy = accuracy_score(y_test, y_pred_lr_sc)\n",
    "print(\"Logistic Regression with Scaled Data Accuracy:\", accuracy)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K – Nearest Neighbor Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9385964912280702"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# K – Nearest Neighbor Classifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
    "knn_classifier.fit(X_train, y_train)\n",
    "y_pred_knn = knn_classifier.predict(X_test)\n",
    "accuracy_score(y_test, y_pred_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/soumyadip/Library/Python/3.9/lib/python/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5789473684210527"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train with Standard scaled Data\n",
    "knn_classifier2 = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
    "knn_classifier2.fit(X_train_sc, y_train)\n",
    "y_pred_knn_sc = knn_classifier.predict(X_test_sc)\n",
    "accuracy_score(y_test, y_pred_knn_sc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9473684210526315"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Naive Bayes Classifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb_classifier = GaussianNB()\n",
    "nb_classifier.fit(X_train, y_train)\n",
    "y_pred_nb = nb_classifier.predict(X_test)\n",
    "accuracy_score(y_test, y_pred_nb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9385964912280702"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train with Standard scaled Data\n",
    "nb_classifier2 = GaussianNB()\n",
    "nb_classifier2.fit(X_train_sc, y_train)\n",
    "y_pred_nb_sc = nb_classifier2.predict(X_test_sc)\n",
    "accuracy_score(y_test, y_pred_nb_sc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9473684210526315"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree Classifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt_classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 51)\n",
    "dt_classifier.fit(X_train, y_train)\n",
    "y_pred_dt = dt_classifier.predict(X_test)\n",
    "accuracy_score(y_test, y_pred_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/soumyadip/Library/Python/3.9/lib/python/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7543859649122807"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train with Standard scaled Data\n",
    "dt_classifier2 = DecisionTreeClassifier(criterion = 'entropy', random_state = 51)\n",
    "dt_classifier2.fit(X_train_sc, y_train)\n",
    "y_pred_dt_sc = dt_classifier.predict(X_test_sc)\n",
    "accuracy_score(y_test, y_pred_dt_sc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9736842105263158"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Classifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf_classifier = RandomForestClassifier(n_estimators = 20, criterion = 'entropy', random_state = 51)\n",
    "rf_classifier.fit(X_train, y_train)\n",
    "y_pred_rf = rf_classifier.predict(X_test)\n",
    "accuracy_score(y_test, y_pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/soumyadip/Library/Python/3.9/lib/python/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7543859649122807"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train with Standard scaled Data\n",
    "rf_classifier2 = RandomForestClassifier(n_estimators = 20, criterion = 'entropy', random_state = 51)\n",
    "rf_classifier2.fit(X_train_sc, y_train)\n",
    "y_pred_rf_sc = rf_classifier.predict(X_test_sc)\n",
    "accuracy_score(y_test, y_pred_rf_sc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AdaBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/soumyadip/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9473684210526315"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adaboost Classifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "adb_classifier = AdaBoostClassifier(DecisionTreeClassifier(criterion = 'entropy', random_state = 200),\n",
    "                                    n_estimators=2000,\n",
    "                                    learning_rate=0.1,\n",
    "                                    algorithm='SAMME.R',\n",
    "                                    random_state=1,)\n",
    "adb_classifier.fit(X_train, y_train)\n",
    "y_pred_adb = adb_classifier.predict(X_test)\n",
    "accuracy_score(y_test, y_pred_adb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/soumyadip/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9473684210526315"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train with Standard scaled Data\n",
    "adb_classifier2 = AdaBoostClassifier(DecisionTreeClassifier(criterion = 'entropy', random_state = 200),\n",
    "                                    n_estimators=2000,\n",
    "                                    learning_rate=0.1,\n",
    "                                    algorithm='SAMME.R',\n",
    "                                    random_state=1,)\n",
    "adb_classifier2.fit(X_train_sc, y_train)\n",
    "y_pred_adb_sc = adb_classifier2.predict(X_test_sc)\n",
    "accuracy_score(y_test, y_pred_adb_sc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9824561403508771"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# XGBoost Classifier\n",
    "from xgboost import XGBClassifier\n",
    "xgb_classifier = XGBClassifier()\n",
    "xgb_classifier.fit(X_train, y_train)\n",
    "y_pred_xgb = xgb_classifier.predict(X_test)\n",
    "accuracy_score(y_test, y_pred_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9824561403508771"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train with Standard scaled Data\n",
    "xgb_classifier2 = XGBClassifier()\n",
    "xgb_classifier2.fit(X_train_sc, y_train)\n",
    "y_pred_xgb_sc = xgb_classifier2.predict(X_test_sc)\n",
    "accuracy_score(y_test, y_pred_xgb_sc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # XGBoost Parameter Tuning Randomized Search "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "params={\n",
    " \"learning_rate\"    : [0.05, 0.10, 0.15, 0.20, 0.25, 0.30 ] ,\n",
    " \"max_depth\"        : [ 3, 4, 5, 6, 8, 10, 12, 15],\n",
    " \"min_child_weight\" : [ 1, 3, 5, 7 ],\n",
    " \"gamma\"            : [ 0.0, 0.1, 0.2 , 0.3, 0.4 ],\n",
    " \"colsample_bytree\" : [ 0.3, 0.4, 0.5 , 0.7 ] \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.981 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           callbacks=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None, device=None,\n",
       "                                           early_stopping_rounds=None,\n",
       "                                           enable_categorical=False,\n",
       "                                           eval_metric=None, feature_types=None,\n",
       "                                           gamma=None, grow_policy=None,\n",
       "                                           importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate=None...\n",
       "                                           monotone_constraints=None,\n",
       "                                           multi_strategy=None,\n",
       "                                           n_estimators=None, n_jobs=None,\n",
       "                                           num_parallel_tree=None,\n",
       "                                           random_state=None, ...),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;colsample_bytree&#x27;: [0.3, 0.4, 0.5,\n",
       "                                                             0.7],\n",
       "                                        &#x27;gamma&#x27;: [0.0, 0.1, 0.2, 0.3, 0.4],\n",
       "                                        &#x27;learning_rate&#x27;: [0.05, 0.1, 0.15, 0.2,\n",
       "                                                          0.25, 0.3],\n",
       "                                        &#x27;max_depth&#x27;: [3, 4, 5, 6, 8, 10, 12,\n",
       "                                                      15],\n",
       "                                        &#x27;min_child_weight&#x27;: [1, 3, 5, 7]},\n",
       "                   scoring=&#x27;roc_auc&#x27;, verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           callbacks=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None, device=None,\n",
       "                                           early_stopping_rounds=None,\n",
       "                                           enable_categorical=False,\n",
       "                                           eval_metric=None, feature_types=None,\n",
       "                                           gamma=None, grow_policy=None,\n",
       "                                           importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate=None...\n",
       "                                           monotone_constraints=None,\n",
       "                                           multi_strategy=None,\n",
       "                                           n_estimators=None, n_jobs=None,\n",
       "                                           num_parallel_tree=None,\n",
       "                                           random_state=None, ...),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;colsample_bytree&#x27;: [0.3, 0.4, 0.5,\n",
       "                                                             0.7],\n",
       "                                        &#x27;gamma&#x27;: [0.0, 0.1, 0.2, 0.3, 0.4],\n",
       "                                        &#x27;learning_rate&#x27;: [0.05, 0.1, 0.15, 0.2,\n",
       "                                                          0.25, 0.3],\n",
       "                                        &#x27;max_depth&#x27;: [3, 4, 5, 6, 8, 10, 12,\n",
       "                                                      15],\n",
       "                                        &#x27;min_child_weight&#x27;: [1, 3, 5, 7]},\n",
       "                   scoring=&#x27;roc_auc&#x27;, verbose=3)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.4, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0.3, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=3, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.4, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0.3, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=3, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           callbacks=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None, device=None,\n",
       "                                           early_stopping_rounds=None,\n",
       "                                           enable_categorical=False,\n",
       "                                           eval_metric=None, feature_types=None,\n",
       "                                           gamma=None, grow_policy=None,\n",
       "                                           importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate=None...\n",
       "                                           monotone_constraints=None,\n",
       "                                           multi_strategy=None,\n",
       "                                           n_estimators=None, n_jobs=None,\n",
       "                                           num_parallel_tree=None,\n",
       "                                           random_state=None, ...),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'colsample_bytree': [0.3, 0.4, 0.5,\n",
       "                                                             0.7],\n",
       "                                        'gamma': [0.0, 0.1, 0.2, 0.3, 0.4],\n",
       "                                        'learning_rate': [0.05, 0.1, 0.15, 0.2,\n",
       "                                                          0.25, 0.3],\n",
       "                                        'max_depth': [3, 4, 5, 6, 8, 10, 12,\n",
       "                                                      15],\n",
       "                                        'min_child_weight': [1, 3, 5, 7]},\n",
       "                   scoring='roc_auc', verbose=3)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Randomized Search\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "random_search = RandomizedSearchCV(xgb_classifier, param_distributions=params, scoring= 'roc_auc', n_jobs= -1, verbose= 3)\n",
    "random_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_child_weight': 3,\n",
       " 'max_depth': 5,\n",
       " 'learning_rate': 0.3,\n",
       " 'gamma': 0.3,\n",
       " 'colsample_bytree': 0.4}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.4, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0.3, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=3, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.4, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0.3, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=3, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.4, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0.3, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=3, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Classifier Accuracy: 0.9824561403508771\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# XGBoost classifier with best parameters\n",
    "xgb_classifier_pt = XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "                                  colsample_bynode=1, colsample_bytree=0.4, gamma=0.2,\n",
    "                                  learning_rate=0.1, max_delta_step=0, max_depth=15,\n",
    "                                  min_child_weight=1, n_estimators=100, n_jobs=1,\n",
    "                                  objective='binary:logistic', random_state=0,\n",
    "                                  reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
    "                                  subsample=1, verbosity=1)\n",
    "\n",
    "# Fit the model\n",
    "xgb_classifier_pt.fit(X_train, y_train)\n",
    "\n",
    "# Predict on test data\n",
    "y_pred_xgb_pt = xgb_classifier_pt.predict(X_test)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred_xgb_pt)\n",
    "print(\"XGBoost Classifier Accuracy:\", accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9824561403508771"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred_xgb_pt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 3840 candidates, totalling 19200 fits\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.982 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.991 total time=   0.0s[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.0s[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.980 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.980 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.989 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.979 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.980 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.990 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.980 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.980 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.1s[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.981 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.989 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.980 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.990 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.990 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.979 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.979 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.979 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.979 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.979 total time=   0.1s\n",
      "\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.989 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.989 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.983 total time=   0.0s[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.979 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.980 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.996 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.980 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.980 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.980 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.980 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.976 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.986 total time=   0.0s[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.987 total time=   0.0s[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.980 total time=   0.0s[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.980 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.996 total time=   0.0s[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.979 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.979 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.979 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.979 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.979 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.988 total time=   0.0s[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.990 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.981 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.998 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.977 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.0s[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.982 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.1s[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.990 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.976 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.1s[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.990 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.996 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.988 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.989 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.977 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.991 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.979 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.982 total time=   0.0s[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.985 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.988 total time=   0.0s[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.979 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.4, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.988 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.988 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.985 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.985 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.986 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.980 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.982 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.982 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.985 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.979 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.980 total time=   0.0s[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.977 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.998 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.981 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.981 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.988 total time=   0.0s[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.981 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.975 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.980 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.975 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.975 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.996 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.975 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.975 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.975 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.2s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.975 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.979 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.986 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.979 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.973 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.975 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.975 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.975 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.975 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.975 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.975 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.974 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.986 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.974 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.974 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.986 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.974 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.974 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.974 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.981 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.986 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.974 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.978 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.978 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.978 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.980 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.978 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.978 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.978 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.978 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.980 total time=   0.0s[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.980 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.988 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.978 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.978 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.978 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.978 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.978 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.978 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.978 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.978 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.0, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.975 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.973 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.973 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.973 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.973 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.973 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.973 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.995 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.985 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.1, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.996 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.998 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.975 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.975 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.975 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.975 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.998 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.975 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.975 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.985 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.985 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.985 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.985 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.986 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.995 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.982 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.995 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.988 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.980 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.979 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.985 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.981 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.981 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.2, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.974 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.981 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.978 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.996 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.978 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.978 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.978 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.991 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.978 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.978 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.976 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.991 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.978 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.981 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.986 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.986 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.986 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.986 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.986 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.986 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.986 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.982 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.995 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.981 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.984 total time=   0.1s[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.985 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.980 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.986 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.980 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.982 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.982 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.983 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.979 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.980 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.984 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.980 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.988 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.979 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.979 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.985 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.993 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.986 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.977 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.3, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.975 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.988 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.989 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.985 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.996 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.988 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.972 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.990 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.972 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=5, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.988 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.972 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=6, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.988 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.972 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.985 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.988 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.972 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=5;, score=0.985 total time=   0.0s[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.991 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=10, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.988 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.995 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.997 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.972 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=5;, score=0.985 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.991 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=12, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.988 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.991 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.996 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.05, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.978 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=5, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.987 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=10, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.978 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=3;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.987 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.992 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=3;, score=0.996 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.1, max_depth=15, min_child_weight=7;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.987 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.981 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.981 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.984 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.981 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.981 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.989 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.990 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=3;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=5;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.15, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=5;, score=0.980 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.988 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.985 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.977 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.977 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=1;, score=0.999 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=5, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.995 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.977 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.985 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.977 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.985 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.977 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.980 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=5;, score=0.980 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.985 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.980 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.977 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.991 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.2, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.987 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=3, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.995 total time=   0.0s[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=4, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.980 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.993 total time=   0.1s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.984 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=5, min_child_weight=7;, score=0.999 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=1;, score=0.998 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.996 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=6, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=8, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.996 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=10, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.981 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.993 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.986 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.981 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=12, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.993 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.996 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.984 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.996 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=5;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=1;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.979 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.25, max_depth=15, min_child_weight=7;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.975 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=1;, score=0.999 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=3;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.985 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.978 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=3, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=4, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.978 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.978 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=5, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=3;, score=0.992 total time=   0.0s[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=1.000 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.982 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=6, min_child_weight=7;, score=0.982 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.978 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=1.000 total time=   0.1s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.995 total time=   0.1s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.980 total time=   0.1s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=1;, score=0.997 total time=   0.1s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.978 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=8, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.978 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=10, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=1.000 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.982 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.980 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.992 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.982 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.999 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=1.000 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=3;, score=0.978 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.994 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=12, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.983 total time=   0.0s\n",
      "[CV 1/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.998 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=5;, score=0.998 total time=   0.0s\n",
      "[CV 3/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.995 total time=   0.0s\n",
      "[CV 2/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.983 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=1;, score=0.997 total time=   0.0s\n",
      "[CV 4/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.994 total time=   0.0s\n",
      "[CV 5/5] END colsample_bytree=0.7, gamma=0.4, learning_rate=0.3, max_depth=15, min_child_weight=7;, score=0.982 total time=   0.0s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None, max_b...\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;colsample_bytree&#x27;: [0.3, 0.4, 0.5, 0.7],\n",
       "                         &#x27;gamma&#x27;: [0.0, 0.1, 0.2, 0.3, 0.4],\n",
       "                         &#x27;learning_rate&#x27;: [0.05, 0.1, 0.15, 0.2, 0.25, 0.3],\n",
       "                         &#x27;max_depth&#x27;: [3, 4, 5, 6, 8, 10, 12, 15],\n",
       "                         &#x27;min_child_weight&#x27;: [1, 3, 5, 7]},\n",
       "             scoring=&#x27;roc_auc&#x27;, verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None, max_b...\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;colsample_bytree&#x27;: [0.3, 0.4, 0.5, 0.7],\n",
       "                         &#x27;gamma&#x27;: [0.0, 0.1, 0.2, 0.3, 0.4],\n",
       "                         &#x27;learning_rate&#x27;: [0.05, 0.1, 0.15, 0.2, 0.25, 0.3],\n",
       "                         &#x27;max_depth&#x27;: [3, 4, 5, 6, 8, 10, 12, 15],\n",
       "                         &#x27;min_child_weight&#x27;: [1, 3, 5, 7]},\n",
       "             scoring=&#x27;roc_auc&#x27;, verbose=3)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.3, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0.1, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.3, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0.1, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None, max_b...\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'colsample_bytree': [0.3, 0.4, 0.5, 0.7],\n",
       "                         'gamma': [0.0, 0.1, 0.2, 0.3, 0.4],\n",
       "                         'learning_rate': [0.05, 0.1, 0.15, 0.2, 0.25, 0.3],\n",
       "                         'max_depth': [3, 4, 5, 6, 8, 10, 12, 15],\n",
       "                         'min_child_weight': [1, 3, 5, 7]},\n",
       "             scoring='roc_auc', verbose=3)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV \n",
    "grid_search = GridSearchCV(xgb_classifier, param_grid=params, scoring= 'roc_auc', n_jobs= -1, verbose= 3)\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.3, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0.1, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.3, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0.1, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.3, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0.1, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Classifier (Grid Search) Accuracy: 0.9736842105263158\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# XGBoost classifier with new parameters\n",
    "xgb_classifier_pt_gs = XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "                                     colsample_bynode=1, colsample_bytree=0.3, gamma=0.0,\n",
    "                                     learning_rate=0.3, max_delta_step=0, max_depth=3,\n",
    "                                     min_child_weight=1, n_estimators=100, n_jobs=1,\n",
    "                                     objective='binary:logistic', random_state=0,\n",
    "                                     reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
    "                                     subsample=1, verbosity=1)\n",
    "\n",
    "# Fit the model\n",
    "xgb_classifier_pt_gs.fit(X_train, y_train)\n",
    "\n",
    "# Predict on test data\n",
    "y_pred_xgb_pt_gs = xgb_classifier_pt_gs.predict(X_test)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred_xgb_pt_gs)\n",
    "print(\"XGBoost Classifier (Grid Search) Accuracy:\", accuracy)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_xgb_pt)\n",
    "plt.title('Heatmap of Confusion Matrix', fontsize = 15)\n",
    "sns.heatmap(cm, annot = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The model is giving 0 type II error and it is best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Report Of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      0.96      0.98        48\n",
      "         1.0       0.97      1.00      0.99        66\n",
      "\n",
      "    accuracy                           0.98       114\n",
      "   macro avg       0.99      0.98      0.98       114\n",
      "weighted avg       0.98      0.98      0.98       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_xgb_pt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross-validation of the ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validation accuracy of XGBoost model =  [1.         0.97826087 0.97826087 0.97826087 0.93478261 0.95555556\n",
      " 1.         0.97777778 0.95555556 0.86666667]\n",
      "\n",
      "Cross validation mean accuracy of XGBoost model =  0.9625120772946861\n"
     ]
    }
   ],
   "source": [
    "# Cross validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "cross_validation = cross_val_score(estimator = xgb_classifier_pt, X = X_train_sc,y = y_train, cv = 10)\n",
    "print(\"Cross validation accuracy of XGBoost model = \", cross_validation)\n",
    "print(\"\\nCross validation mean accuracy of XGBoost model = \", cross_validation.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save XGBoost Classifier model using Pickel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix of XGBoost model: \n",
      " [[46  2]\n",
      " [ 0 66]] \n",
      "\n",
      "Accuracy of XGBoost model =  0.9824561403508771\n"
     ]
    }
   ],
   "source": [
    "## Pickle\n",
    "import pickle\n",
    "\n",
    "# save model\n",
    "pickle.dump(xgb_classifier_pt, open('breast_cancer_detector.pickle', 'wb'))\n",
    "\n",
    "# load model\n",
    "breast_cancer_detector_model = pickle.load(open('breast_cancer_detector.pickle', 'rb'))\n",
    "\n",
    "# predict the output\n",
    "y_pred = breast_cancer_detector_model.predict(X_test)\n",
    "\n",
    "# confusion matrix\n",
    "print('Confusion matrix of XGBoost model: \\n',confusion_matrix(y_test, y_pred),'\\n')\n",
    "\n",
    "# show the accuracy\n",
    "print('Accuracy of XGBoost model = ',accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming X_train has 512 features\n",
    "breast_cancer_detector_model = XGBClassifier()\n",
    "breast_cancer_detector_model.fit(X_train, y_train)\n",
    "\n",
    "# Save the newly trained model\n",
    "with open('breast_cancer_detector_512.pickle', 'wb') as model_file:\n",
    "    pickle.dump(breast_cancer_detector_model, model_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the re-trained model with 512 features\n",
    "with open('/Users/soumyadip/Desktop/Cancer Detection Tool/breast_cancer_detector_512.pickle', 'rb') as model_file:\n",
    "    breast_cancer_detector_model = pickle.load(model_file)\n",
    "\n",
    "# Now it will accept the 512 features dataset\n",
    "y_pred = breast_cancer_detector_model.predict(X_test)\n"
   ]
  }
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