{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lead scoring for e-commerce customers \n",
    "\n",
    "```\n",
    "HKU Space / AI and ML with Business and Financial Applications\n",
    "CHAN TSZ CHUN / Eric\n",
    "2025-01-04\n",
    "Continous assignment\n",
    "```\n",
    "\n",
    "## Business goal \n",
    "\n",
    "Predict the likelihood of a customer to convert. Marketing can focus resources on the hot leads in retargeting. \n",
    "\n",
    "### Solution\n",
    "\n",
    "Bineary classification to classify hot leads and cold leads. \n",
    "\n",
    "Machine learning algorithm used: Random Forest Classifier. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import standard random forest library \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, ConfusionMatrixDisplay, classification_report\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV, train_test_split \n",
    "from joblib import dump, load\n",
    "from scipy.stats import randint\n",
    "\n",
    "from sklearn.tree import export_graphviz\n",
    "from IPython.display import Image\n",
    "\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/eric/Documents/studies/hkuspace-ml-business/continuous-assignment/lead-scoring/notebook\n"
     ]
    }
   ],
   "source": [
    "save_dir = \"../src/model/\" \n",
    "\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation 1\n",
    "\n",
    "### Data overview \n",
    "\n",
    "Dataset source: https://archive.ics.uci.edu/dataset/468/online+shoppers+purchasing+intention+dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12330 entries, 0 to 12329\n",
      "Data columns (total 18 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Administrative           12330 non-null  int64  \n",
      " 1   Administrative_Duration  12330 non-null  float64\n",
      " 2   Informational            12330 non-null  int64  \n",
      " 3   Informational_Duration   12330 non-null  float64\n",
      " 4   ProductRelated           12330 non-null  int64  \n",
      " 5   ProductRelated_Duration  12330 non-null  float64\n",
      " 6   BounceRates              12330 non-null  float64\n",
      " 7   ExitRates                12330 non-null  float64\n",
      " 8   PageValues               12330 non-null  float64\n",
      " 9   SpecialDay               12330 non-null  float64\n",
      " 10  Month                    12330 non-null  object \n",
      " 11  OperatingSystems         12330 non-null  int64  \n",
      " 12  Browser                  12330 non-null  int64  \n",
      " 13  Region                   12330 non-null  int64  \n",
      " 14  TrafficType              12330 non-null  int64  \n",
      " 15  VisitorType              12330 non-null  object \n",
      " 16  Weekend                  12330 non-null  bool   \n",
      " 17  Revenue                  12330 non-null  bool   \n",
      "dtypes: bool(2), float64(7), int64(7), object(2)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Load the dataset \n",
    "data = '../src/data/online_shoppers_intention.csv'\n",
    "df_raw = pd.read_csv(data)\n",
    "\n",
    "# Data exploration\n",
    "df_raw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Administrative             0\n",
       "Administrative_Duration    0\n",
       "Informational              0\n",
       "Informational_Duration     0\n",
       "ProductRelated             0\n",
       "ProductRelated_Duration    0\n",
       "BounceRates                0\n",
       "ExitRates                  0\n",
       "PageValues                 0\n",
       "SpecialDay                 0\n",
       "Month                      0\n",
       "OperatingSystems           0\n",
       "Browser                    0\n",
       "Region                     0\n",
       "TrafficType                0\n",
       "VisitorType                0\n",
       "Weekend                    0\n",
       "Revenue                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comment: No null values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Administrative  Administrative_Duration  Informational  \\\n",
      "count    12330.000000             12330.000000   12330.000000   \n",
      "mean         2.315166                80.818611       0.503569   \n",
      "std          3.321784               176.779107       1.270156   \n",
      "min          0.000000                 0.000000       0.000000   \n",
      "25%          0.000000                 0.000000       0.000000   \n",
      "50%          1.000000                 7.500000       0.000000   \n",
      "75%          4.000000                93.256250       0.000000   \n",
      "max         27.000000              3398.750000      24.000000   \n",
      "\n",
      "       Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
      "count            12330.000000    12330.000000             12330.000000   \n",
      "mean                34.472398       31.731468              1194.746220   \n",
      "std                140.749294       44.475503              1913.669288   \n",
      "min                  0.000000        0.000000                 0.000000   \n",
      "25%                  0.000000        7.000000               184.137500   \n",
      "50%                  0.000000       18.000000               598.936905   \n",
      "75%                  0.000000       38.000000              1464.157214   \n",
      "max               2549.375000      705.000000             63973.522230   \n",
      "\n",
      "        BounceRates     ExitRates    PageValues    SpecialDay  \\\n",
      "count  12330.000000  12330.000000  12330.000000  12330.000000   \n",
      "mean       0.022191      0.043073      5.889258      0.061427   \n",
      "std        0.048488      0.048597     18.568437      0.198917   \n",
      "min        0.000000      0.000000      0.000000      0.000000   \n",
      "25%        0.000000      0.014286      0.000000      0.000000   \n",
      "50%        0.003112      0.025156      0.000000      0.000000   \n",
      "75%        0.016812      0.050000      0.000000      0.000000   \n",
      "max        0.200000      0.200000    361.763742      1.000000   \n",
      "\n",
      "       OperatingSystems       Browser        Region   TrafficType  \n",
      "count      12330.000000  12330.000000  12330.000000  12330.000000  \n",
      "mean           2.124006      2.357097      3.147364      4.069586  \n",
      "std            0.911325      1.717277      2.401591      4.025169  \n",
      "min            1.000000      1.000000      1.000000      1.000000  \n",
      "25%            2.000000      2.000000      1.000000      2.000000  \n",
      "50%            2.000000      2.000000      3.000000      2.000000  \n",
      "75%            3.000000      2.000000      4.000000      4.000000  \n",
      "max            8.000000     13.000000      9.000000     20.000000  \n"
     ]
    }
   ],
   "source": [
    "print(df_raw.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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>Administrative</th>\n",
       "      <th>Administrative_Duration</th>\n",
       "      <th>Informational</th>\n",
       "      <th>Informational_Duration</th>\n",
       "      <th>ProductRelated</th>\n",
       "      <th>ProductRelated_Duration</th>\n",
       "      <th>BounceRates</th>\n",
       "      <th>ExitRates</th>\n",
       "      <th>PageValues</th>\n",
       "      <th>SpecialDay</th>\n",
       "      <th>Month</th>\n",
       "      <th>OperatingSystems</th>\n",
       "      <th>Browser</th>\n",
       "      <th>Region</th>\n",
       "      <th>TrafficType</th>\n",
       "      <th>VisitorType</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>627.500000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Administrative  Administrative_Duration  Informational  \\\n",
       "0               0                      0.0              0   \n",
       "1               0                      0.0              0   \n",
       "2               0                      0.0              0   \n",
       "3               0                      0.0              0   \n",
       "4               0                      0.0              0   \n",
       "\n",
       "   Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
       "0                     0.0               1                 0.000000   \n",
       "1                     0.0               2                64.000000   \n",
       "2                     0.0               1                 0.000000   \n",
       "3                     0.0               2                 2.666667   \n",
       "4                     0.0              10               627.500000   \n",
       "\n",
       "   BounceRates  ExitRates  PageValues  SpecialDay Month  OperatingSystems  \\\n",
       "0         0.20       0.20         0.0         0.0   Feb                 1   \n",
       "1         0.00       0.10         0.0         0.0   Feb                 2   \n",
       "2         0.20       0.20         0.0         0.0   Feb                 4   \n",
       "3         0.05       0.14         0.0         0.0   Feb                 3   \n",
       "4         0.02       0.05         0.0         0.0   Feb                 3   \n",
       "\n",
       "   Browser  Region  TrafficType        VisitorType  Weekend  Revenue  \n",
       "0        1       1            1  Returning_Visitor    False    False  \n",
       "1        2       1            2  Returning_Visitor    False    False  \n",
       "2        1       9            3  Returning_Visitor    False    False  \n",
       "3        2       2            4  Returning_Visitor    False    False  \n",
       "4        3       1            4  Returning_Visitor     True    False  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature engineering "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Administrative               int64\n",
      "Administrative_Duration    float64\n",
      "Informational                int64\n",
      "Informational_Duration     float64\n",
      "ProductRelated               int64\n",
      "ProductRelated_Duration    float64\n",
      "BounceRates                float64\n",
      "ExitRates                  float64\n",
      "PageValues                 float64\n",
      "SpecialDay                 float64\n",
      "OperatingSystems             int64\n",
      "Browser                      int64\n",
      "Region                       int64\n",
      "TrafficType                  int64\n",
      "VisitorType                 object\n",
      "Weekend                       bool\n",
      "Revenue                       bool\n",
      "Month_Aug                     bool\n",
      "Month_Dec                     bool\n",
      "Month_Feb                     bool\n",
      "Month_Jul                     bool\n",
      "Month_Jun                     bool\n",
      "Month_Mar                     bool\n",
      "Month_May                     bool\n",
      "Month_Nov                     bool\n",
      "Month_Oct                     bool\n",
      "Month_Sep                     bool\n",
      "Month_Jan                     bool\n",
      "Month_Apr                     bool\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# One-hot encoding for Month\n",
    "df = pd.get_dummies(df_raw, columns=['Month'], prefix='Month') \n",
    "\n",
    "# Define all possible months\n",
    "all_months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "\n",
    "# Add columns for missing months with initial values of 0\n",
    "for month in all_months:\n",
    "    if f'Month_{month}' not in df.columns:\n",
    "        df[f'Month_{month}'] = False \n",
    "        \n",
    "print(df.dtypes) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Returning_Visitor' 'New_Visitor' 'Other']\n",
      "Administrative                     int64\n",
      "Administrative_Duration          float64\n",
      "Informational                      int64\n",
      "Informational_Duration           float64\n",
      "ProductRelated                     int64\n",
      "ProductRelated_Duration          float64\n",
      "BounceRates                      float64\n",
      "ExitRates                        float64\n",
      "PageValues                       float64\n",
      "SpecialDay                       float64\n",
      "OperatingSystems                   int64\n",
      "Browser                            int64\n",
      "Region                             int64\n",
      "TrafficType                        int64\n",
      "Weekend                             bool\n",
      "Revenue                             bool\n",
      "Month_Aug                           bool\n",
      "Month_Dec                           bool\n",
      "Month_Feb                           bool\n",
      "Month_Jul                           bool\n",
      "Month_Jun                           bool\n",
      "Month_Mar                           bool\n",
      "Month_May                           bool\n",
      "Month_Nov                           bool\n",
      "Month_Oct                           bool\n",
      "Month_Sep                           bool\n",
      "Month_Jan                           bool\n",
      "Month_Apr                           bool\n",
      "VisitorType_New_Visitor             bool\n",
      "VisitorType_Other                   bool\n",
      "VisitorType_Returning_Visitor       bool\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "unique_values = df['VisitorType'].unique()\n",
    "print(unique_values)\n",
    "\n",
    "# One-hot encoding for VisitorType\n",
    "df = pd.get_dummies(df, columns=['VisitorType']) \n",
    "\n",
    "# Define all possible visitor \n",
    "all_visitors = ['Returning_Visitor' 'New_Visitor' 'Other']\n",
    "\n",
    "# Add columns for missing visitor with initial values of False\n",
    "if 'VisitorType_Returning_Visitor' not in df.columns:\n",
    "    df['VisitorType_Returning_Visitor'] = False \n",
    "if 'VisitorType_New_Visitor' not in df.columns:\n",
    "    df['VisitorType_New_Visitor'] = False \n",
    "if 'VisitorType_Other' not in df.columns:\n",
    "    df['VisitorType_Other'] = False \n",
    "    \n",
    "print(df.dtypes) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into features (X) and target (y)\n",
    "X = df.drop(['Revenue'], axis=1)\n",
    "y = df['Revenue']\n",
    "\n",
    "# Split data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling 1\n",
    "\n",
    "### Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(max_depth=8, n_estimators=238, random_state=42)\n"
     ]
    }
   ],
   "source": [
    "param_dist = {'n_estimators': randint(50,500), 'max_depth': randint(1,20)}\n",
    "\n",
    "# Create a random forest classifier\n",
    "rf = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Use random search to find the best hyperparameters\n",
    "random_search = RandomizedSearchCV(rf, param_distributions = param_dist, n_iter=5, cv=5, random_state=42)\n",
    "\n",
    "# Fit the random search object to the data\n",
    "random_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best estimator\n",
    "print(random_search.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save best parameters in variables \n",
    "best_estimator = random_search.best_estimator_ \n",
    "max_depth = best_estimator.max_depth\n",
    "n_estimators = best_estimator.n_estimators\n",
    "random_state = best_estimator.random_state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model fitting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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>RandomForestClassifier(max_depth=8, n_estimators=238, random_state=42)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=8, n_estimators=238, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=8, n_estimators=238, random_state=42)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(max_depth=max_depth, n_estimators=n_estimators, random_state=random_state)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../src/model/lead-scoring-rf-v01.joblib']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the trained model to the specified directory\n",
    "dump(rf, os.path.join(save_dir, \"lead-scoring-rf-v01.joblib\")) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model evaluating "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.89\n",
      "Precision: 0.81\n",
      "Recall: 0.44\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.90      0.98      0.94      2055\n",
      "        True       0.81      0.44      0.57       411\n",
      "\n",
      "    accuracy                           0.89      2466\n",
      "   macro avg       0.85      0.71      0.75      2466\n",
      "weighted avg       0.88      0.89      0.88      2466\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x3157f6e50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = rf.predict(X_test)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "\n",
    "# Calculate precision \n",
    "precision = precision_score(y_test, y_pred) \n",
    "print(f\"Precision: {precision:.2f}\")\n",
    "\n",
    "# Calculate recall\n",
    "recall = recall_score(y_test, y_pred)\n",
    "print(f\"Recall: {recall:.2f}\")\n",
    "\n",
    "# Generate a comprehensive classification report\n",
    "report = classification_report(y_test, y_pred)\n",
    "print(report) \n",
    "\n",
    "# Create the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "ConfusionMatrixDisplay(confusion_matrix=cm).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importances "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a pandas series of feature importances, feature name from column name\n",
    "feature_importances = pd.Series(rf.feature_importances_, index=X_train.columns).sort_values(ascending=False)\n",
    "\n",
    "# Plot a bar chart\n",
    "feature_importances.plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comment: The most important feature is PageValues. \n",
    "\n",
    "According to the dataset metadata, the \"Page Value\" features represent the metrics measured by \"Google Analytics\" for each page in the e-commerce site -- represents the average value for a web page that a user visited before completing an e-commerce transaction. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation 2\n",
    "\n",
    "Drop Month feature which has the lowest value on feature importances, rebuild the model and compare its performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Administrative               int64\n",
      "Administrative_Duration    float64\n",
      "Informational                int64\n",
      "Informational_Duration     float64\n",
      "ProductRelated               int64\n",
      "ProductRelated_Duration    float64\n",
      "BounceRates                float64\n",
      "ExitRates                  float64\n",
      "PageValues                 float64\n",
      "SpecialDay                 float64\n",
      "OperatingSystems             int64\n",
      "Browser                      int64\n",
      "Region                       int64\n",
      "TrafficType                  int64\n",
      "VisitorType                 object\n",
      "Weekend                       bool\n",
      "Revenue                       bool\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "df_dropMonth = df_raw.drop('Month', axis=1)\n",
    "\n",
    "print(df_dropMonth.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Returning_Visitor' 'New_Visitor' 'Other']\n",
      "Administrative                     int64\n",
      "Administrative_Duration          float64\n",
      "Informational                      int64\n",
      "Informational_Duration           float64\n",
      "ProductRelated                     int64\n",
      "ProductRelated_Duration          float64\n",
      "BounceRates                      float64\n",
      "ExitRates                        float64\n",
      "PageValues                       float64\n",
      "SpecialDay                       float64\n",
      "OperatingSystems                   int64\n",
      "Browser                            int64\n",
      "Region                             int64\n",
      "TrafficType                        int64\n",
      "Weekend                             bool\n",
      "Revenue                             bool\n",
      "VisitorType_New_Visitor             bool\n",
      "VisitorType_Other                   bool\n",
      "VisitorType_Returning_Visitor       bool\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "unique_values = df_dropMonth['VisitorType'].unique()\n",
    "print(unique_values)\n",
    "\n",
    "# One-hot encoding for VisitorType\n",
    "df_dropMonth = pd.get_dummies(df_dropMonth, columns=['VisitorType']) \n",
    "print(df_dropMonth.dtypes) \n",
    "\n",
    "# Split data into features (X) and target (y)\n",
    "X = df_dropMonth.drop(['Revenue'], axis=1)\n",
    "y = df_dropMonth['Revenue']\n",
    "\n",
    "# Split data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(max_depth=7, n_estimators=171, random_state=42)\n",
      "Accuracy: 0.88\n",
      "Precision: 0.76\n",
      "Recall: 0.45\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.90      0.97      0.93      2055\n",
      "        True       0.76      0.45      0.56       411\n",
      "\n",
      "    accuracy                           0.88      2466\n",
      "   macro avg       0.83      0.71      0.75      2466\n",
      "weighted avg       0.87      0.88      0.87      2466\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x3152e02d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_dist = {'n_estimators': randint(50,500), 'max_depth': randint(1,20)}\n",
    "\n",
    "# Create a random forest classifier\n",
    "rf_dropMonth = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Use random search to find the best hyperparameters\n",
    "random_search = RandomizedSearchCV(rf_dropMonth, param_distributions = param_dist, n_iter=5, cv=5, random_state=42)\n",
    "\n",
    "# Fit the random search object to the data\n",
    "random_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best estimator\n",
    "print(random_search.best_estimator_)\n",
    "\n",
    "# Save best parameters in variables \n",
    "best_estimator = random_search.best_estimator_ \n",
    "max_depth = best_estimator.max_depth\n",
    "n_estimators = best_estimator.n_estimators\n",
    "random_state = best_estimator.random_state\n",
    "\n",
    "rf_dropMonth = RandomForestClassifier(max_depth=max_depth, n_estimators=n_estimators, random_state=random_state)\n",
    "rf_dropMonth.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rf_dropMonth.predict(X_test)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "\n",
    "# Calculate precision \n",
    "precision = precision_score(y_test, y_pred) \n",
    "print(f\"Precision: {precision:.2f}\")\n",
    "\n",
    "# Calculate recall\n",
    "recall = recall_score(y_test, y_pred)\n",
    "print(f\"Recall: {recall:.2f}\")\n",
    "\n",
    "# Generate a comprehensive classification report\n",
    "report = classification_report(y_test, y_pred)\n",
    "print(report) \n",
    "\n",
    "# Create the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "ConfusionMatrixDisplay(confusion_matrix=cm).plot() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../src/model/lead-scoring-rf-dropMonth-v01.joblib']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_dir = \"../src/model/\" \n",
    "\n",
    "# Save the trained model to the specified directory\n",
    "dump(rf, os.path.join(save_dir, \"lead-scoring-rf-dropMonth-v01.joblib\")) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comment: The accuracy and precision scores after dropping Month features are lower then the 1st attempt. Therefore the 1st random forest classifer will be used. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation 3\n",
    "\n",
    "Data preparation is the same and based on model 1. \n",
    "\n",
    "## Modelling 3\n",
    "\n",
    "Recall score is low for model 1, using class_weight='balanced', rebuild the model and compare its performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(class_weight='balanced', max_depth=19, n_estimators=264,\n",
      "                       random_state=42)\n",
      "Accuracy: 0.89\n",
      "Precision: 0.69\n",
      "Recall: 0.56\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.92      0.95      0.93      2055\n",
      "        True       0.69      0.56      0.62       411\n",
      "\n",
      "    accuracy                           0.89      2466\n",
      "   macro avg       0.81      0.76      0.78      2466\n",
      "weighted avg       0.88      0.89      0.88      2466\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x31870b7d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_dist = {'n_estimators': randint(50,500), 'max_depth': randint(1,20)}\n",
    "\n",
    "# Create a random forest classifier\n",
    "rf_bal = RandomForestClassifier(random_state=42, class_weight='balanced')\n",
    "\n",
    "# Use random search to find the best hyperparameters\n",
    "random_search = RandomizedSearchCV(rf_bal, param_distributions = param_dist, n_iter=5, cv=5, random_state=42)\n",
    "\n",
    "# Fit the random search object to the data\n",
    "random_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best estimator\n",
    "print(random_search.best_estimator_)\n",
    "\n",
    "# Save best parameters in variables \n",
    "best_estimator = random_search.best_estimator_ \n",
    "max_depth = best_estimator.max_depth\n",
    "n_estimators = best_estimator.n_estimators\n",
    "random_state = best_estimator.random_state\n",
    "\n",
    "rf_bal = RandomForestClassifier(max_depth=max_depth, n_estimators=n_estimators, random_state=random_state, class_weight='balanced')\n",
    "rf_bal.fit(X_train, y_train)\n",
    "\n",
    "save_dir = \"../src/model/\" \n",
    "\n",
    "# Save the trained model to the specified directory\n",
    "dump(rf_bal, os.path.join(save_dir, \"lead-scoring-rf-balanced-v01.joblib\")) \n",
    "\n",
    "y_pred = rf_bal.predict(X_test)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "\n",
    "# Calculate precision \n",
    "precision = precision_score(y_test, y_pred) \n",
    "print(f\"Precision: {precision:.2f}\")\n",
    "\n",
    "# Calculate recall\n",
    "recall = recall_score(y_test, y_pred)\n",
    "print(f\"Recall: {recall:.2f}\")\n",
    "\n",
    "# Generate a comprehensive classification report\n",
    "report = classification_report(y_test, y_pred)\n",
    "print(report) \n",
    "\n",
    "# Create the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "ConfusionMatrixDisplay(confusion_matrix=cm).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comment: Model 3 provides a higher recall score but a lower precision score, indicating this model is having a better ability to identify true positives. This is important for this business case as to identify potental customers. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation 4\n",
    "\n",
    "Data preparation is the same and based on model 1. \n",
    "\n",
    "## Modelling 4\n",
    "\n",
    "Recall score is low for model 1, using grid search with recall as scoring parameter. Rebuild the model and compare its performance. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 225 candidates, totalling 1125 fits\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   2.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   2.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   2.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   2.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   2.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   2.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   2.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   2.4s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   2.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   2.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   2.4s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   2.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   2.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   1.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   2.1s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   2.1s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   2.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   2.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   2.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   2.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   2.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   2.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   2.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   2.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   2.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.7s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.7s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.7s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   1.5s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.2s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.3s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=6, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.4s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.4s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.4s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.4s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.4s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.5s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.6s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.6s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   2.1s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=2, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=238; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=240; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   2.2s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=5, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=1, min_samples_split=10, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=2, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=5, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   2.1s\n",
      "[CV] END max_depth=12, min_samples_leaf=2, min_samples_split=10, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=2, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=5, n_estimators=245; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=230; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=238; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   2.0s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=240; total time=   1.9s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.8s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.7s\n",
      "[CV] END max_depth=12, min_samples_leaf=4, min_samples_split=10, n_estimators=245; total time=   1.7s\n",
      "Accuracy: 0.87\n",
      "Precision: 0.59\n",
      "Recall: 0.81\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.96      0.89      0.92      2055\n",
      "        True       0.59      0.81      0.68       411\n",
      "\n",
      "    accuracy                           0.87      2466\n",
      "   macro avg       0.77      0.85      0.80      2466\n",
      "weighted avg       0.90      0.87      0.88      2466\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x3186bea10>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4A157uR05J0MICLQzfNRXPP/KTh68pxf5ZwOJjComIqqYZQvbciyrAdGxRTw2/TMioopJm9m5lt+hVJUdC3Y31gG42nfgwIEMHDjQaZuTJ0/y2GOP8d577zFo0CCHazabjWXLlrFixQr69OkDwMqVK4mPj2fTpk3079+fgwcPsn79enbs2EFiYiIAS5cuJSkpiUOHDtG6desqx1vrUwaLFi2iRYsWBAUFkZCQwEcffVTbIdVrXf7nHL9+Iofud9kue/1gRgh97zlDh24FxMaXctevvqNl2yK++DQEgOY3F/P0n7+ma7984pqX0rF7Ab9+IpudG8Op+NGX/xHjT9Mm4TwxTcq4pct57n3sFJ/vCaG8rCbepcgPnv5tIpvejedYVhhZR8KZ90IHohsXcePNP/w/sHnD9WR+fB0534RyLCuMpa+0JbRBOS1uPAfA0a/CSf1dZ3ZtjSHnZCifZkTxlyWtSeyei4+v/Uo/Wuqp/Px8h6OkpOSqxrHb7SQnJzNt2jRuueWWStczMjIoKyujX79+5rm4uDjatWvHtm3bANi+fTtWq9VMBgC6du2K1Wo121RVrSYEb731FikpKcycOZO9e/fys5/9jIEDB3Ls2LHaDMur3XJ7ITs2WPk22x/DgMz/NuDkV4Ek9Dh3xT6F+b6ENLDje4V6U36eL+//vRFtOxfip+qq1LLQBhcy14L8y/9l9POzM3D4MQrO+ZH1RfgVxwkJLed8oR/2ilr/XiVVdPFOhe4cAPHx8eZ8vdVqJS0t7arimTNnDn5+fkyaNOmy13NycggICKBRo0YO52NiYsjJyTHbREdHV+obHR1ttqmqWp0ymDt3LmPHjuXBBx8EYP78+bz33nssXrz4qn/B4p4Jz59k/rR47ku4BV8/Ax8fg5Q/HqddYuFl2+ef8WXV/FjuSv620rU/v9CYd96IoqTIlzYJhTy3/KvqDl/kJxiMm3SAfZkRHP3K8cO+yx2neOK5PQQGVXDmu0B+/3hX8m0Blx0lLLyUXz7wBf9e27QmghYP8dQaguPHjxMe/sPfn8DAQJfHysjI4JVXXmHPnj1YLK5NRRiG4dDncv0vbVMVtZbalpaWkpGR4VAKAejXr98VyxwlJSWVSjXiWWuXRfF5RgjPpn/FwvWHGPf0Nyyc0YQ9WxpUalt4zoen7m9J05uK+dXkypnoPY/ksmjDYVL/egQfH4M/PN4Uo55v25Fr2yNT99H8xnxeerpTpWufZkQyccydTB1/B3t2RPPkCxlYG1UuBQeHlDHr5V0c+7oBq5bdVBNhyzUmPDzc4biahOCjjz4iNzeXpk2b4ufnh5+fH0ePHmXKlCk0b94cgNjYWEpLS8nLy3Pom5ubS0xMjNnm1KlTlcY/ffq02aaqai0h+Pbbb6moqKgU8I9LIZdKS0tzKNPEx8fXRKheo6TIQvqLjRk/6xu69sunZdtihv3mW3oMPcv/veZYkjpf4MPM0TcQFGLnmWVZl50KsEZW0OSGEhJ6FDBj8VF2/cfKwYyQGno3Io4enryPxO6nmPFoEt+dDq50vaTYj+wToRza34hXUjtQUWGh35DjDm2CQ8p5fv4uiov8eOHJzlRouqBOsWMxn2dwVYcHb0yUnJzMp59+SmZmpnnExcUxbdo03nvvPQASEhLw9/dn48aNZr/s7Gz27dtHt27dAEhKSsJms7Fr1y6zzc6dO7HZbGabqqr1XQaXljSclTlmzJjB5MmTzdf5+flKCjyovNxCeZkPPj6OX+N9fA2MH62bKjx3IRnwDzB4Nv0rAoJ++mv/xcpAWan+AZWaZvDwlH0k9chhxoQkTmVXLSm1WMDfv8J8HRxSxvPzd1JW5sNz07pQVupbXQFLNTHc3GVguNi3oKCAI0eOmK+zsrLIzMwkIiKCpk2bEhkZ6dDe39+f2NhYc2eA1Wpl7NixTJkyhcjISCIiIpg6dSrt27c3dx20adOGAQMGMG7cOJYsWQJc2HY4ePBgl3YYQC0mBFFRUfj6+laqBvy4FHKpwMDAqyrNyA+KCn34JuuH32HO8QC+3BdMWMNyopuUcWtSAUufjyMg6CQxTUr5dHsDNv1fBOOfOQlcqAz87pc3UFLkw/QFWZwv8OV8wYWxrJHl+PrC53tDOLQ3hHa3F9KgYTnZRwP5yx9iady8hDYJl1+LIFJdJkzdR49+J3n+iS4UnfejUUQxAIWF/pSW+BIYVM69vz7Czo9iOPNdIOHhZQy6+2uiritm6/sX9oUHh5Tzwis7CQyq4I/PdiIktIyQ0AtbZmxnA7Hb6/ctbeuLmn7a4e7du+nVq5f5+uIX2jFjxpCenl6lMebNm4efnx8jR46kqKiI3r17k56ebt6DAODNN99k0qRJ5hT80KFDWbhwoUuxAlgMo/ZmdRMTE0lISGDRokXmubZt2zJs2LAqLSrMz8/HarWSd7gl4WH65lkVn2xrwPRf3FjpfN+RZ5g6/xhncv3439TG7NkSxrmzfkRff2Hr4Yjxp7FYrtwfYPnOA8TGl5J1MIjFT1/PVweCKT7vQ0R0GZ17nWP046eIaqx9h64alDSktkOo0/61/Z+XPT/v+Q5sejce/4AKpj+7l5tuycNqLSPf5s8XBxuyOr0VXxxsCED7Tt/y4qIdlx3ngZ//D7k5mgq7WuX2EjYdW4TNZnNYqOdJFz8r7t40Bv/Qyy8UrYqywlL+1md5tcZam2o1IXjrrbdITk7mtddeIykpiddff52lS5eyf/9+mjVr9pP9lRCIN1BCIPVZTSYEP9/4gNsJwZq+b9TbhKBW1xDce++9fPfddzz33HNkZ2fTrl073n333SolAyIiIq6o6SmDuqbWFxVOmDCBCRMm1HYYIiIiXq3WEwIREZGaUNPPMqhrlBCIiIhX0JSBc1qJJyIiIqoQiIiId1CFwDklBCIi4hWUEDinKQMRERFRhUBERLyDKgTOKSEQERGvYODe1sH6/vR2JQQiIuIVVCFwTmsIRERERBUCERHxDqoQOKeEQEREvIISAuc0ZSAiIiKqEIiIiHdQhcA5JQQiIuIVDMOC4caHujt96wJNGYiIiIgqBCIi4h3sWNy6MZE7fesCJQQiIuIVtIbAOU0ZiIiIiCoEIiLiHbSo0DklBCIi4hU0ZeCcEgIREfEKqhA4pzUEIiIiogqBiIh4B8PNKYP6XiFQQiAiIl7BAAzDvf71maYMRERERBUCERHxDnYsWHSnwitSQiAiIl5Buwyc05SBiIhINdiyZQtDhgwhLi4Oi8XC2rVrzWtlZWU88cQTtG/fntDQUOLi4rj//vv55ptvHMYoKSlh4sSJREVFERoaytChQzlx4oRDm7y8PJKTk7FarVitVpKTkzl79qzL8SohEBERr3DxxkTuHK4oLCykQ4cOLFy4sNK18+fPs2fPHp566in27NnD3//+dw4fPszQoUMd2qWkpLBmzRpWr17N1q1bKSgoYPDgwVRUVJhtRo8eTWZmJuvXr2f9+vVkZmaSnJzs8u9HUwYiIuIVDMPNXQYu9h04cCADBw687DWr1crGjRsdzi1YsIDbb7+dY8eO0bRpU2w2G8uWLWPFihX06dMHgJUrVxIfH8+mTZvo378/Bw8eZP369ezYsYPExEQAli5dSlJSEocOHaJ169ZVjlcVAhERERfk5+c7HCUlJR4Z12azYbFYaNiwIQAZGRmUlZXRr18/s01cXBzt2rVj27ZtAGzfvh2r1WomAwBdu3bFarWabapKCYGIiHiFi4sK3TkA4uPjzfl6q9VKWlqa27EVFxfz5JNPMnr0aMLDwwHIyckhICCARo0aObSNiYkhJyfHbBMdHV1pvOjoaLNNVWnKQEREvIKndhkcP37c/NAGCAwMdCuusrIyRo0ahd1uZ9GiRVWIw8Bi+eF9/PjPV2pTFUoIRETEK9gNCxYPPO0wPDzcISFwR1lZGSNHjiQrK4v333/fYdzY2FhKS0vJy8tzqBLk5ubSrVs3s82pU6cqjXv69GliYmJcikVTBiIiIrXgYjLwxRdfsGnTJiIjIx2uJyQk4O/v77D4MDs7m3379pkJQVJSEjabjV27dpltdu7cic1mM9tUlSoEIiLiFWp6l0FBQQFHjhwxX2dlZZGZmUlERARxcXH84he/YM+ePfzzn/+koqLCnPOPiIggICAAq9XK2LFjmTJlCpGRkURERDB16lTat29v7jpo06YNAwYMYNy4cSxZsgSA8ePHM3jwYJd2GIASAhER8RIXEgJ31hC41n737t306tXLfD158mQAxowZw6xZs3jnnXcA6Nixo0O/Dz74gJ49ewIwb948/Pz8GDlyJEVFRfTu3Zv09HR8fX3N9m+++SaTJk0ydyMMHTr0svc++ClKCERERKpBz549MZxkEc6uXRQUFMSCBQtYsGDBFdtERESwcuXKq4rxx5QQiIiIV9CzDJxTQiAiIl7B+P5wp399pl0GIiIiogqBiIh4B00ZOKeEQEREvIPmDJxSQiAiIt7BzQoB9bxCoDUEIiIiogqBiIh4h5q+U2Fdo4RARES8ghYVOqcpAxEREVGFQEREvIRhcW9hYD2vECghEBERr6A1BM5pykBERERUIRARES+hGxM5VaWE4NVXX63ygJMmTbrqYERERKqLdhk4V6WEYN68eVUazGKxKCEQERGpg6qUEGRlZVV3HCIiItWvnpf93XHViwpLS0s5dOgQ5eXlnoxHRESkWlycMnDnqM9cTgjOnz/P2LFjCQkJ4ZZbbuHYsWPAhbUDL774oscDFBER8QjDA0c95nJCMGPGDD755BM+/PBDgoKCzPN9+vThrbfe8mhwIiIiUjNc3na4du1a3nrrLbp27YrF8kP5pG3btnz55ZceDU5ERMRzLN8f7vSvv1xOCE6fPk10dHSl84WFhQ4JgoiIyDVF9yFwyuUpgy5duvCvf/3LfH0xCVi6dClJSUmei0xERERqjMsVgrS0NAYMGMCBAwcoLy/nlVdeYf/+/Wzfvp3NmzdXR4wiIiLuU4XAKZcrBN26deO///0v58+f54YbbmDDhg3ExMSwfft2EhISqiNGERER91182qE7Rz12Vc8yaN++PcuXL/d0LCIiIlJLriohqKioYM2aNRw8eBCLxUKbNm0YNmwYfn56VpKIiFyb9Phj51z+BN+3bx/Dhg0jJyeH1q1bA3D48GGuu+463nnnHdq3b+/xIEVERNymNQROubyG4MEHH+SWW27hxIkT7Nmzhz179nD8+HFuvfVWxo8fXx0xioiISDVzuULwySefsHv3bho1amSea9SoEbNnz6ZLly4eDU5ERMRj3F0YWM8XFbpcIWjdujWnTp2qdD43N5cbb7zRI0GJiIh4msVw/6jPqlQhyM/PN/+cmprKpEmTmDVrFl27dgVgx44dPPfcc8yZM6d6ohQREXGX1hA4VaUKQcOGDWnUqBGNGjViyJAhHDhwgJEjR9KsWTOaNWvGyJEj2bdvH0OGDKnueEVEROqELVu2MGTIEOLi4rBYLKxdu9bhumEYzJo1i7i4OIKDg+nZsyf79+93aFNSUsLEiROJiooiNDSUoUOHcuLECYc2eXl5JCcnY7VasVqtJCcnc/bsWZfjrVKF4IMPPnB5YBERkWtKDa8hKCwspEOHDjzwwAPcfffdla6/9NJLzJ07l/T0dG666SZeeOEF+vbty6FDhwgLCwMgJSWFdevWsXr1aiIjI5kyZQqDBw8mIyMDX19fAEaPHs2JEydYv349AOPHjyc5OZl169a5FG+VEoIePXq4NKiIiMg1p4anDAYOHMjAgQMvP5RhMH/+fGbOnMmIESMAWL58OTExMaxatYqHHnoIm83GsmXLWLFiBX369AFg5cqVxMfHs2nTJvr378/BgwdZv349O3bsIDExEfjh2UKHDh0ybw9QFS4vKrzo/PnzfP7553z66acOh4iISH2Wn5/vcJSUlLg8RlZWFjk5OfTr1888FxgYSI8ePdi2bRsAGRkZlJWVObSJi4ujXbt2Zpvt27djtVrNZACga9euWK1Ws01VXdXjjx944AH+/e9/X/Z6RUWFq0OKiIhUPw9VCOLj4x1OP/PMM8yaNculoXJycgCIiYlxOB8TE8PRo0fNNgEBAQ7b/C+2udg/JyeH6OjoSuNHR0ebbarK5YQgJSWFvLw8duzYQa9evVizZg2nTp3ihRde4OWXX3Z1OBERkZrhoYTg+PHjhIeHm6cDAwOvekiLxXFdgmEYlc5VCuOSNpdrX5VxLuVyQvD+++/zj3/8gy5duuDj40OzZs3o27cv4eHhpKWlMWjQIFeHFBERqTPCw8MdEoKrERsbC1z4ht+4cWPzfG5urlk1iI2NpbS0lLy8PIcqQW5uLt26dTPbXO7eQKdPn65UffgpLq8hKCwsNMsTERERnD59GrjwBMQ9e/a4OpyIiEjNuIYef9yiRQtiY2PZuHGjea60tJTNmzebH/YJCQn4+/s7tMnOzmbfvn1mm6SkJGw2G7t27TLb7Ny5E5vNZrapKpcrBK1bt+bQoUM0b96cjh07smTJEpo3b85rr73mkOWIiIhcS9y926CrfQsKCjhy5Ij5Oisri8zMTCIiImjatCkpKSmkpqbSqlUrWrVqRWpqKiEhIYwePRoAq9XK2LFjmTJlCpGRkURERDB16lTat29v7jpo06YNAwYMYNy4cSxZsgS4sO1w8ODBLu0wgKtcQ5CdnQ1cWEjRv39/3nzzTQICAkhPT3d1OBERkXpp9+7d9OrVy3w9efJkAMaMGUN6ejrTp0+nqKiICRMmkJeXR2JiIhs2bDDvQQAwb948/Pz8GDlyJEVFRfTu3Zv09HTzHgQAb775JpMmTTJ3IwwdOpSFCxe6HK/FMNx7wvPF7YdNmzYlKirKnaFclp+fj9VqJe9wS8LDrnoHpcg1bVCS7gAq9Ve5vYRNxxZhs9ncnpe/koufFU3nvIBPcNBVj2MvKubYE7+v1lhrk8sVgkuFhIRw2223eSIWERERqSVVSgguljmqYu7cuVcdjIiISHWx4OYaAo9Fcm2qUkKwd+/eKg3m6p5HERERuTbUi4cb/fym9vhZ/Gs7DJFq4dMx7KcbidRR9ooAOFZDP6yGH25U17i9hkBERKROqOGHG9U1WpovIiIiqhCIiIiXUIXAKSUEIiLiFWr6ToV1jaYMRERE5OoSghUrVnDHHXcQFxdnPrd5/vz5/OMf//BocCIiIh5jeOCox1xOCBYvXszkyZO56667OHv2LBUVFQA0bNiQ+fPnezo+ERERz1BC4JTLCcGCBQtYunQpM2fOdHi4QufOnfnss888GpyIiIjUDJcXFWZlZdGpU6dK5wMDAyksLPRIUCIiIp6mRYXOuVwhaNGiBZmZmZXO//vf/6Zt27aeiElERMTzLt6p0J2jHnO5QjBt2jQeffRRiouLMQyDXbt28de//pW0tDT+/Oc/V0eMIiIi7tN9CJxyOSF44IEHKC8vZ/r06Zw/f57Ro0dz/fXX88orrzBq1KjqiFFERESq2VXdmGjcuHGMGzeOb7/9FrvdTnR0tKfjEhER8SitIXDOrTsVRkVFeSoOERGR6qUpA6dcTghatGiBxXLlhRVfffWVWwGJiIhIzXM5IUhJSXF4XVZWxt69e1m/fj3Tpk3zVFwiIiKe5eaUgSoEl3j88ccve/5Pf/oTu3fvdjsgERGRaqEpA6c89nCjgQMH8re//c1Tw4mIiEgN8tjjj//v//6PiIgITw0nIiLiWaoQOOVyQtCpUyeHRYWGYZCTk8Pp06dZtGiRR4MTERHxFG07dM7lhGD48OEOr318fLjuuuvo2bMnN998s6fiEhERkRrkUkJQXl5O8+bN6d+/P7GxsdUVk4iIiNQwlxYV+vn58cgjj1BSUlJd8YiIiFQPwwNHPebyLoPExET27t1bHbGIiIhUm4trCNw56jOX1xBMmDCBKVOmcOLECRISEggNDXW4fuutt3osOBEREakZVU4IfvOb3zB//nzuvfdeACZNmmRes1gsGIaBxWKhoqLC81GKiIh4Qj3/lu+OKicEy5cv58UXXyQrK6s64xEREakeug+BU1VeQ2AYF34TzZo1c3qIiIjIhZ15v//972nRogXBwcG0bNmS5557DrvdbrYxDINZs2YRFxdHcHAwPXv2ZP/+/Q7jlJSUMHHiRKKioggNDWXo0KGcOHHC4/G6tKjQ2VMORURErmU1vahwzpw5vPbaayxcuJCDBw/y0ksv8Yc//IEFCxaYbV566SXmzp3LwoUL+fjjj4mNjaVv376cO3fObJOSksKaNWtYvXo1W7dupaCggMGDB3t8it6lRYU33XTTTyYFZ86ccSsgERGRalHDUwbbt29n2LBhDBo0CIDmzZvz17/+1XwQoGEYzJ8/n5kzZzJixAjgwvR8TEwMq1at4qGHHsJms7Fs2TJWrFhBnz59AFi5ciXx8fFs2rSJ/v37u/GGHLmUEDz77LNYrVaP/XAREZG6Jj8/3+F1YGAggYGBldp1796d1157jcOHD3PTTTfxySefsHXrVubPnw9AVlYWOTk59OvXz2GsHj16sG3bNh566CEyMjIoKytzaBMXF0e7du3Ytm1b7SUEo0aNIjo62mM/XEREpKZ46lkG8fHxDuefeeYZZs2aVan9E088gc1m4+abb8bX15eKigpmz57NL3/5SwBycnIAiImJcegXExPD0aNHzTYBAQE0atSoUpuL/T2lygmB1g+IiEid5qEpg+PHjxMeHm6evlx1AOCtt95i5cqVrFq1iltuuYXMzExSUlKIi4tjzJgxZrtLP18vbuN3GkoV2riqygnBxV0GIiIi3iw8PNwhIbiSadOm8eSTTzJq1CgA2rdvz9GjR0lLS2PMmDHmM4FycnJo3Lix2S83N9esGsTGxlJaWkpeXp5DlSA3N5du3bp58m1VfZeB3W7XdIGIiNRdNfwsg/Pnz+Pj4/gx6+vra247bNGiBbGxsWzcuNG8XlpayubNm80P+4SEBPz9/R3aZGdns2/fPo8nBC7fulhERKQu8tQagqoaMmQIs2fPpmnTptxyyy3s3buXuXPn8pvf/ObCeBYLKSkppKam0qpVK1q1akVqaiohISGMHj0aAKvVytixY5kyZQqRkZFEREQwdepU2rdvb+468BQlBCIi4h1qeNvhggULeOqpp5gwYQK5ubnExcXx0EMP8fTTT5ttpk+fTlFRERMmTCAvL4/ExEQ2bNhAWFiY2WbevHn4+fkxcuRIioqK6N27N+np6fj6+rrxZiqzGHV4cUB+fj5Wq5WeDMPP4l/b4YhUC5+ObWs7BJFqU15RwvufzsFms1VpXv5qXPysaJ2Sim9g0FWPU1FSzKH5v6vWWGuTKgQiIuId9CwDp5QQiIiIV6jpNQR1jUvPMhAREZH6SRUCERHxDpoycEoJgYiIeAVNGTinKQMRERFRhUBERLyEpgycUkIgIiLeQQmBU5oyEBEREVUIRETEO1i+P9zpX58pIRAREe+gKQOnlBCIiIhX0LZD57SGQERERFQhEBERL6EpA6eUEIiIiPeo5x/q7tCUgYiIiKhCICIi3kGLCp1TQiAiIt5Bawic0pSBiIiIqEIgIiLeQVMGzikhEBER76ApA6c0ZSAiIiKqEIiIiHfQlIFzSghERMQ7aMrAKSUEIiLiHZQQOKU1BCIiIqIKgYiIeAetIXBOCYGIiHgHTRk4pSkDERERUYVARES8g8UwsBhX/zXfnb51gRICERHxDpoycEpTBiIiItXk5MmT/OpXvyIyMpKQkBA6duxIRkaGed0wDGbNmkVcXBzBwcH07NmT/fv3O4xRUlLCxIkTiYqKIjQ0lKFDh3LixAmPx6qEQEREvMLFXQbuHK7Iy8vjjjvuwN/fn3//+98cOHCAl19+mYYNG5ptXnrpJebOncvChQv5+OOPiY2NpW/fvpw7d85sk5KSwpo1a1i9ejVbt26loKCAwYMHU1FR4aHfzAWaMhAREe9Qw1MGc+bMIT4+njfeeMM817x58x+GMwzmz5/PzJkzGTFiBADLly8nJiaGVatW8dBDD2Gz2Vi2bBkrVqygT58+AKxcuZL4+Hg2bdpE//793XhDjlQhEBERcUF+fr7DUVJSctl277zzDp07d+aee+4hOjqaTp06sXTpUvN6VlYWOTk59OvXzzwXGBhIjx492LZtGwAZGRmUlZU5tImLi6Ndu3ZmG09RQiAiIl7BU1MG8fHxWK1W80hLS7vsz/vqq69YvHgxrVq14r333uPhhx9m0qRJ/OUvfwEgJycHgJiYGId+MTEx5rWcnBwCAgJo1KjRFdt4iqYMRETEO3hoyuD48eOEh4ebpwMDAy/b3G6307lzZ1JTUwHo1KkT+/fvZ/Hixdx///1mO4vF4vhjDKPSuUqhVKGNq1QhEBERr+CpCkF4eLjDcaWEoHHjxrRt29bhXJs2bTh27BgAsbGxAJW+6efm5ppVg9jYWEpLS8nLy7tiG09RQiAiIlIN7rjjDg4dOuRw7vDhwzRr1gyAFi1aEBsby8aNG83rpaWlbN68mW7dugGQkJCAv7+/Q5vs7Gz27dtntvEUTRmIiIh3qOFdBr/97W/p1q0bqampjBw5kl27dvH666/z+uuvAxemClJSUkhNTaVVq1a0atWK1NRUQkJCGD16NABWq5WxY8cyZcoUIiMjiYiIYOrUqbRv397cdeApSghERMRr1OQTC7t06cKaNWuYMWMGzz33HC1atGD+/Pncd999Zpvp06dTVFTEhAkTyMvLIzExkQ0bNhAWFma2mTdvHn5+fowcOZKioiJ69+5Neno6vr6+Ho3XYhh19+bM+fn5WK1WejIMP4t/bYcjUi18Orb96UYidVR5RQnvfzoHm83msFDPky5+ViSMnI2ff9BVj1NeVkzG2zOrNdbapAqBiIh4B8O4cLjTvx5TQiAiIl7ham4/fGn/+ky7DEREREQVAhER8RJ6/LFTSghERMQrWOwXDnf612eaMhARERFVCKQyH1+D5Ck5/M+IszS6rowzuf5sfLsRq+bHYBgX751t8Kspp7jrvu9oYK3g870h/Ol3TTh6+Oq39IhUh0F3fcGgQV8QE1MIwNGjVlb9tR27d8cBcN99n9HjzqNcd915ysp8OHIkguV/uZVDh6LMMQYOOELPnke58cYzhISU84t77qawMKBW3o+4QVMGTtVqhWDLli0MGTKEuLg4LBYLa9eurc1w5Hv3PprLoPu/408zr2dcj5v58wuN+cUjpxn2m2/NNiMfPc2I8af508zrmXhXK/JO+5O2+kuCQytqMXKRyr79NoQ33ujIpMf7M+nx/nzySQxPP/URTZvaADh5MoxFizvzyIS7mDqtL6dyQ5n9wodYw4vNMQIDy9md0ZjVb91SW29DPMBTzzKor2o1ISgsLKRDhw4sXLiwNsOQS7RJKGT7e1Z2/SecUycC2PqvhuzZHEarDkXftzAY/uBpVr8aw3//3ZCjh4L54+PxBAbb6fXzs7UZukglO3ddz8e74zh5MpyTJ8NZ/pcOFBf7cfPNFxLcDz9sTmZmLDk5DTh2zMrS128jNLSMFi3OmmOs/cfN/L//15bPP4+spXchHnHxPgTuHPVYrSYEAwcO5IUXXmDEiBG1GYZcYt/HoXTsfo7rW5YA0LJtEbfcXsjH71+4lWZs01IiY8rJ2NzA7FNW6sNnOxrQtnNhrcQsUhU+PnZ63HmUoKByPj8YVem6n18FAwceoaDAn6+yGl1mBJH6q06tISgpKaGkpMR8nZ+fX4vR1F9vL4wmNMzOn7d8jr0CfHwh/cVYPlx74R/IiOhyAPJOO94uOu+0H9FNSms8XpGf0rz5Wea+vJGAgAqKivx4/vmfcey41bx+++0nefKJbQQGlnPmTDAzZ/YiP//yj7SVuks3JnKuTu0ySEtLw2q1mkd8fHxth1Qv9Rh2lt535/Hio015tP9N/PHxeH7x8Gn63HPGseEl/3NYLIC56FDk2nHiRBiPPjaA307uy7/evZEpU3bQNN5mXv/kkxgefWwAU6b0JSOjMTNm/BertdjJiFInGR446rE6lRDMmDEDm81mHsePH6/tkOqlcU9l89bCaDb/oxFffx7Mf/4Wwd+XXseoibkAnMm9UFhqFF3m0K9hVDl5p+tU0Um8RHm5L9nZYXzxRSTp6R356quGDBv2w3PqS0r8yM4O4/NDUcx/JZGKCgv9+39ZixGL1Lw6lRAEBgYSHh7ucIjnBQbZMS65AYe9Aizf18tyjgXw3Sk/bruzwLzu52+nfdcCDuwOrclQRa6KxQL+/le+y8xPXZe6SbsMnNPXOalkx8ZwRk3KJfdkAEcPBXFDuyJGPHSaDasjvm9hYe2fr2PUxFOc/CqQk1kB/HJSLiVFPnywpmFthi5SyZgxn7B7d2NOnw4hJKScHncepX37XJ56ugeBgeWMGrWfnTuu50xeMGFhJQwe/AVRUef56KOm5hiNGhXRqFExcXEXkuDmzc9SVORPbm4IBQVaa1Bn6GmHTtVqQlBQUMCRI0fM11lZWWRmZhIREUHTpk2d9JTqtOj31zNmeg6PpZ2gYWQ5353y590Vkbw5L8Zs8/afriMgyM5jaScI+/7GRDN+2ZKiQt9ajFykskYNi5k2dQcREUUUFvqTldWQp57uwd69jfH3ryC+ST59ZmZhtZaQnx/I4cMRTJvWh2PHflh0eNddR/jVffvM13/8w38AeHluIps2tazx9yRSHSyGUXspz4cffkivXr0qnR8zZgzp6ek/2T8/Px+r1UpPhuFn8f/J9iJ1kU/HtrUdgki1Ka8o4f1P52Cz2aptGvjiZ0XSwOfw87/6u6mWlxWz/d9PV2ustalWKwQ9e/akFvMRERHxJrp1sVN1alGhiIiIVA8tKhQREa+gGxM5p4RARES8g924cLjTvx5TQiAiIt5Bawic0hoCERERUYVARES8gwU31xB4LJJrkxICERHxDrpToVOaMhARERFVCERExDto26FzSghERMQ7aJeBU5oyEBEREVUIRETEO1gMA4sbCwPd6VsXKCEQERHvYP/+cKd/PaYpAxEREVFCICIi3uHilIE7x9VKS0vDYrGQkpJinjMMg1mzZhEXF0dwcDA9e/Zk//79Dv1KSkqYOHEiUVFRhIaGMnToUE6cOHHVcTijhEBERLyD4YHjKnz88ce8/vrr3HrrrQ7nX3rpJebOncvChQv5+OOPiY2NpW/fvpw7d85sk5KSwpo1a1i9ejVbt26loKCAwYMHU1FRcXXBOKGEQEREvMPFOxW6c7iooKCA++67j6VLl9KoUaMfhWIwf/58Zs6cyYgRI2jXrh3Lly/n/PnzrFq1CgCbzcayZct4+eWX6dOnD506dWLlypV89tlnbNq0yWO/louUEIiIiLggPz/f4SgpKbli20cffZRBgwbRp08fh/NZWVnk5OTQr18/81xgYCA9evRg27ZtAGRkZFBWVubQJi4ujnbt2pltPEkJgYiIeIWLdyp05wCIj4/HarWaR1pa2mV/3urVq9mzZ89lr+fk5AAQExPjcD4mJsa8lpOTQ0BAgENl4dI2nqRthyIi4h089HCj48ePEx4ebp4ODAys1PT48eM8/vjjbNiwgaCgoCsOabE4PkPRMIxK5yqH8dNtroYqBCIiIi4IDw93OC6XEGRkZJCbm0tCQgJ+fn74+fmxefNmXn31Vfz8/MzKwKXf9HNzc81rsbGxlJaWkpeXd8U2nqSEQEREvILF7v5RVb179+azzz4jMzPTPDp37sx9991HZmYmLVu2JDY2lo0bN5p9SktL2bx5M926dQMgISEBf39/hzbZ2dns27fPbONJmjIQERHv4KEpg6oICwujXbt2DudCQ0OJjIw0z6ekpJCamkqrVq1o1aoVqamphISEMHr0aACsVitjx45lypQpREZGEhERwdSpU2nfvn2lRYqeoIRARESkFkyfPp2ioiImTJhAXl4eiYmJbNiwgbCwMLPNvHnz8PPzY+TIkRQVFdG7d2/S09Px9fX1eDwWw6i7T2vIz8/HarXSk2H4WfxrOxyRauHTsW1thyBSbcorSnj/0znYbDaHhXqeZH5WdJmJn9+VF/j9lPLyYj78eHa1xlqbVCEQERGvoKcdOqdFhSIiIqIKgYiIeIkaXFRYFykhEBER72AALmwdvGz/ekwJgYiIeAWtIXBOawhEREREFQIREfESBm6uIfBYJNckJQQiIuIdtKjQKU0ZiIiIiCoEIiLiJeyAO08NdmeHQh2ghEBERLyCdhk4pykDERERUYVARES8hBYVOqWEQEREvIMSAqc0ZSAiIiKqEIiIiJdQhcApJQQiIuIdtO3QKSUEIiLiFbTt0DmtIRARERFVCERExEtoDYFTSghERMQ72A2wuPGhbq/fCYGmDEREREQVAhER8RKaMnBKCYGIiHgJNxMC6ndCoCkDERERUYVARES8hKYMnFJCICIi3sFu4FbZX7sMREREpL5ThUBERLyDYb9wuNO/HlNCICIi3kFrCJxSQiAiIt5Bawic0hoCERGRapCWlkaXLl0ICwsjOjqa4cOHc+jQIYc2hmEwa9Ys4uLiCA4OpmfPnuzfv9+hTUlJCRMnTiQqKorQ0FCGDh3KiRMnPB6vEgIREfEOF6cM3DlcsHnzZh599FF27NjBxo0bKS8vp1+/fhQWFpptXnrpJebOncvChQv5+OOPiY2NpW/fvpw7d85sk5KSwpo1a1i9ejVbt26loKCAwYMHU1FR4bFfDWjKQEREvIWBm2sIXGu+fv16h9dvvPEG0dHRZGRkcOedd2IYBvPnz2fmzJmMGDECgOXLlxMTE8OqVat46KGHsNlsLFu2jBUrVtCnTx8AVq5cSXx8PJs2baJ///5X/34uoQqBiIiIC/Lz8x2OkpKSKvWz2WwAREREAJCVlUVOTg79+vUz2wQGBtKjRw+2bdsGQEZGBmVlZQ5t4uLiaNeundnGU5QQiIiId/DQlEF8fDxWq9U80tLSqvCjDSZPnkz37t1p164dADk5OQDExMQ4tI2JiTGv5eTkEBAQQKNGja7YxlM0ZSAiIt7BbgfcuJeA/ULf48ePEx4ebp4ODAz8ya6PPfYYn376KVu3bq10zWKxOLw2DKPSuUtVpY2rVCEQERFxQXh4uMPxUwnBxIkTeeedd/jggw9o0qSJeT42Nhag0jf93Nxcs2oQGxtLaWkpeXl5V2zjKUoIRETEO9TwLgPDMHjsscf4+9//zvvvv0+LFi0crrdo0YLY2Fg2btxonistLWXz5s1069YNgISEBPz9/R3aZGdns2/fPrONp2jKQEREvEMN36nw0UcfZdWqVfzjH/8gLCzMrARYrVaCg4OxWCykpKSQmppKq1ataNWqFampqYSEhDB69Giz7dixY5kyZQqRkZFEREQwdepU2rdvb+468BQlBCIiItVg8eLFAPTs2dPh/BtvvMGvf/1rAKZPn05RURETJkwgLy+PxMRENmzYQFhYmNl+3rx5+Pn5MXLkSIqKiujduzfp6en4+vp6NF6LYdTdmzPn5+djtVrpyTD8LP61HY5ItfDp2La2QxCpNuUVJbz/6RxsNpvDQj1PuvhZ0SfiAfx8Aq56nHJ7KZvOvFGtsdYmVQhERMQrGIYdw40nFrrTty5QQiAiIt7BMNx7QFHdLahXiXYZiIiIiCoEIiLiJQw3H39czysESghERMQ72O1gcWMdQD1fQ6ApAxEREVGFQEREvISmDJxSQiAiIl7BsNsx3JgyqO/bDjVlICIiIqoQiIiIl9CUgVNKCERExDvYDbAoIbgSTRmIiIiIKgQiIuIlDANw5z4E9btCoIRARES8gmE3MNyYMqjDDweuEiUEIiLiHQw77lUItO1QRERE6jlVCERExCtoysA5JQQiIuIdNGXgVJ1OCC5ma+WUuXWvCZFrmU9FSW2HIFJtyr//+10T377d/awop8xzwVyD6nRCcO7cOQC28m4tRyJSjT79R21HIFLtzp07h9VqrZaxAwICiI2NZWuO+58VsbGxBAQEeCCqa4/FqMOTIna7nW+++YawsDAsFktth+MV8vPziY+P5/jx44SHh9d2OCIepb/fNc8wDM6dO0dcXBw+PtW3zr24uJjS0lK3xwkICCAoKMgDEV176nSFwMfHhyZNmtR2GF4pPDxc/2BKvaW/3zWruioDPxYUFFRvP8g9RdsORURERAmBiIiIKCEQFwUGBvLMM88QGBhY26GIeJz+fos3q9OLCkVERMQzVCEQERERJQQiIiKihEBERERQQiAiIiIoIRAXLFq0iBYtWhAUFERCQgIfffRRbYck4hFbtmxhyJAhxMXFYbFYWLt2bW2HJFLjlBBIlbz11lukpKQwc+ZM9u7dy89+9jMGDhzIsWPHajs0EbcVFhbSoUMHFi5cWNuhiNQabTuUKklMTOS2225j8eLF5rk2bdowfPhw0tLSajEyEc+yWCysWbOG4cOH13YoIjVKFQL5SaWlpWRkZNCvXz+H8/369WPbtm21FJWIiHiSEgL5Sd9++y0VFRXExMQ4nI+JiSEnJ6eWohIREU9SQiBVdukjpg3D0GOnRUTqCSUE8pOioqLw9fWtVA3Izc2tVDUQEZG6SQmB/KSAgAASEhLYuHGjw/mNGzfSrVu3WopKREQ8ya+2A5C6YfLkySQnJ9O5c2eSkpJ4/fXXOXbsGA8//HBthybitoKCAo4cOWK+zsrKIjMzk4iICJo2bVqLkYnUHG07lCpbtGgRL730EtnZ2bRr14558+Zx55131nZYIm778MMP6dWrV6XzY8aMIT09veYDEqkFSghEREREawhERERECYGIiIighEBERERQQiAiIiIoIRARERGUEIiIiAhKCERERAQlBCIiIoISAhG3zZo1i44dO5qvf/3rXzN8+PAaj+Prr7/GYrGQmZl5xTbNmzdn/vz5VR4zPT2dhg0buh2bxWJh7dq1bo8jItVHCYHUS7/+9a+xWCxYLBb8/f1p2bIlU6dOpbCwsNp/9iuvvFLl291W5UNcRKQm6OFGUm8NGDCAN954g7KyMj766CMefPBBCgsLWbx4caW2ZWVl+Pv7e+TnWq1Wj4wjIlKTVCGQeiswMJDY2Fji4+MZPXo09913n1m2vljm/9///V9atmxJYGAghmFgs9kYP3480dHRhIeH8z//8z988sknDuO++OKLxMTEEBYWxtixYykuLna4fumUgd1uZ86cOdx4440EBgbStGlTZs+eDUCLFi0A6NSpExaLhZ49e5r93njjDdq0aUNQUBA333wzixYtcvg5u3btolOnTgQFBdG5c2f27t3r8u9o7ty5tG/fntDQUOLj45kwYQIFBQWV2q1du5abbrqJoKAg+vbty/Hjxx2ur1u3joSEBIKCgmjZsiXPPvss5eXlLscjIrVHCYF4jeDgYMrKyszXR44c4e233+Zvf/ubWbIfNGgQOTk5vPvuu2RkZHDbbbfRu3dvzpw5A8Dbb7/NM888w+zZs9m9ezeNGzeu9EF9qRkzZjBnzhyeeuopDhw4wKpVq4iJiQEufKgDbNq0iezsbP7+978DsHTpUmbOnMns2bM5ePAgqampPPXUUyxfvhyAwsJCBg8eTOvWrcnIyGDWrFlMnTrV5d+Jj48Pr776Kvv27WP58uW8//77TJ8+3aHN+fPnmT17NsuXL+e///0v+fn5jBo1yrz+3nvv8atf/YpJkyZx4MABlixZQnp6upn0iEgdYYjUQ2PGjDGGDRtmvt65c6cRGRlpjBw50jAMw3jmmWcMf39/Izc312zzn//8xwgPDzeKi4sdxrrhhhuMJUuWGIZhGElJScbDDz/scD0xMdHo0KHDZX92fn6+ERgYaCxduvSycWZlZRmAsXfvXofz8fHxxqpVqxzOPf/880ZSUpJhGIaxZMkSIyIiwigsLDSvL168+LJj/VizZs2MefPmXfH622+/bURGRpqv33jjDQMwduzYYZ47ePCgARg7d+40DMMwfvaznxmpqakO46xYscJo3Lix+Row1qxZc8WfKyK1T2sIpN765z//SYMGDSgvL6esrIxhw4axYMEC83qzZs247rrrzNcZGRkUFBQQGRnpME5RURFffvklAAcPHuThhx92uJ6UlMQHH3xw2RgOHjxISUkJvXv3rnLcp0+f5vjx44wdO5Zx48aZ58vLy831CQcPHqRDhw6EhIQ4xOGqDz74gNTUVA4cOEB+fj7l5eUUFxdTWFhIaGgoAH5+fnTu3Nnsc/PNN9OwYUMOHjzI7bffTkZGBh9//LFDRaCiooLi4mLOnz/vEKOIXLuUEEi91atXLxYvXoy/vz9xcXGVFg1e/MC7yG6307hxYz788MNKY13t1rvg4GCX+9jtduDCtEFiYqLDNV9fXwAMw7iqeH7s6NGj3HXXXTz88MM8//zzREREsHXrVsaOHeswtQIXtg1e6uI5u93Os88+y4gRIyq1CQoKcjtOEakZSgik3goNDeXGG2+scvvbbruNnJwc/Pz8aN68+WXbtGnThh07dnD//feb53bs2HHFMVu1akVwcDD/+c9/ePDBBytdDwgIAC58o74oJiaG66+/nq+++or77rvvsuO2bduWFStWUFRUZCYdzuK4nN27d1NeXs7LL7+Mj8+F5URvv/12pXbl5eXs3r2b22+/HYBDhw5x9uxZbr75ZuDC7+3QoUMu/a5F5NqjhEDke3369CEpKYnhw4czZ84cWrduzTfffMO7777L8OHD6dy5M48//jhjxoyhc+fOdO/enTfffJP9+/fTsmXLy44ZFBTEE088wfTp0wkICOCOO+7g9OnT7N+/n7FjxxIdHU1wcDDr16+nSZMmBAUFYbVamTVrFpMmTSI8PJyBAwdSUlLC7t27ycvLY/LkyYwePZqZM2cyduxYfv/73/P111/zxz/+0aX3e8MNN1BeXs6CBQsYMmQI//3vf3nttdcqtfP392fixIm8+uqr+Pv789hjj9G1a1czQXj66acZPHgw8fHx3HPPPfj4+PDpp5/y2Wef8cILL7j+H0JEaoV2GYh8z2Kx8O6773LnnXfym9/8hptuuolRo0bx9ddfm7sC7r33Xp5++mmeeOIJEhISOHr0KI888ojTcZ966immTJnC008/TZs2bbj33nvJzc0FLszPv/rqqyxZsoS4uDiGDRsGwIMPPsif//xn0tPTad++PT169CA9Pd3cptigQQPWrVvHgQMH6NSpEzNnzmTOnDkuvd+OHTsyd+5c5syZQ7t27XjzzTdJS0ur1C4kJIQnnniC0aNHk5SURHBwMKtXrzav9+/fn3/+859s3LiRLl260LVrV+bOnUuzZs1cikdEapfF8MRkpIiIiNRpqhCIiIiIEgIRERFRQiAiIiIoIRARERGUEIiIiAhKCERERAQlBCIiIoISAhEREUEJgYiIiKCEQERERFBCICIiIsD/B9c8x0kF21OfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a random forest classifier\n",
    "rf_recall = RandomForestClassifier(random_state=42, class_weight='balanced')\n",
    "\n",
    "# Define the parameter grid\n",
    "param_grid = {'n_estimators': [230, 235, 238, 240, 245], 'max_depth': [None, 6, 8, 10, 12], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4]}\n",
    "\n",
    "# Create a GridSearchCV object with 'recall' as the scoring metric\n",
    "rf_grid_search = GridSearchCV(estimator=rf_recall, param_grid=param_grid, cv=5, scoring='recall', n_jobs=-1, verbose=2)\n",
    "\n",
    "# Fit the GridSearchCV object to the training data\n",
    "rf_grid_search.fit(X_train, y_train)\n",
    "\n",
    "save_dir = \"../src/model/\" \n",
    "\n",
    "# Save the trained model to the specified directory\n",
    "dump(rf_grid_search, os.path.join(save_dir, \"lead-scoring-rf-gridrecall-v01.joblib\")) \n",
    "\n",
    "y_pred = rf_grid_search.predict(X_test)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "\n",
    "# Calculate precision \n",
    "precision = precision_score(y_test, y_pred) \n",
    "print(f\"Precision: {precision:.2f}\")\n",
    "\n",
    "# Calculate recall\n",
    "recall = recall_score(y_test, y_pred)\n",
    "print(f\"Recall: {recall:.2f}\")\n",
    "\n",
    "# Generate a comprehensive classification report\n",
    "report = classification_report(y_test, y_pred)\n",
    "print(report) \n",
    "\n",
    "# Create the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "ConfusionMatrixDisplay(confusion_matrix=cm).plot()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comment: This model is having the highest recall score, which focus on identifying maximum amount of true positives -- identify as much true positive potential customers as possible. The trade of is lowered the precision score, where more false positive is identified. \n",
    "\n",
    "Considering the business case of retargeting customers, not missing true positives are the most important, and considering acceptable with some marketing efforts were spent on customers who did not convert in the end. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make predictions \n",
    "\n",
    "### Load the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the saved model \n",
    "rf = load(os.path.join(save_dir, \"lead-scoring-rf-gridrecall-v01.joblib\")) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### New data point -- Eve  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the new data point \n",
    "data = '../src/data/new_data_point_eve.csv'\n",
    "X_test = pd.read_csv(data)\n",
    "\n",
    "# One-hot encoding for Month\n",
    "X_test = pd.get_dummies(X_test, columns=['Month'], dummy_na=False) \n",
    "\n",
    "# Define all possible months\n",
    "all_months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "\n",
    "# Add columns for missing months with initial values of False\n",
    "for month in all_months:\n",
    "    if f'Month_{month}' not in X_test.columns:\n",
    "        X_test[f'Month_{month}'] = False \n",
    "\n",
    "# One-hot encoding for VisitorType\n",
    "X_test = pd.get_dummies(X_test, columns=['VisitorType']) \n",
    "\n",
    "# Define all possible visitor \n",
    "all_visitors = ['Returning_Visitor' 'New_Visitor' 'Other']\n",
    "\n",
    "# Add columns for missing visitor with initial values of False\n",
    "if 'VisitorType_Returning_Visitor' not in X_test.columns:\n",
    "    X_test['VisitorType_Returning_Visitor'] = False \n",
    "if 'VisitorType_New_Visitor' not in X_test.columns:\n",
    "    X_test['VisitorType_New_Visitor'] = False \n",
    "if 'VisitorType_Other' not in X_test.columns:\n",
    "    X_test['VisitorType_Other'] = False "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Administrative                     int64\n",
      "Administrative_Duration          float64\n",
      "Informational                      int64\n",
      "Informational_Duration           float64\n",
      "ProductRelated                     int64\n",
      "ProductRelated_Duration          float64\n",
      "BounceRates                      float64\n",
      "ExitRates                        float64\n",
      "PageValues                       float64\n",
      "SpecialDay                       float64\n",
      "OperatingSystems                   int64\n",
      "Browser                            int64\n",
      "Region                             int64\n",
      "TrafficType                        int64\n",
      "Weekend                             bool\n",
      "Revenue                             bool\n",
      "Month_Aug                           bool\n",
      "Month_Dec                           bool\n",
      "Month_Feb                           bool\n",
      "Month_Jul                           bool\n",
      "Month_Jun                           bool\n",
      "Month_Mar                           bool\n",
      "Month_May                           bool\n",
      "Month_Nov                           bool\n",
      "Month_Oct                           bool\n",
      "Month_Sep                           bool\n",
      "Month_Jan                           bool\n",
      "Month_Apr                           bool\n",
      "VisitorType_New_Visitor             bool\n",
      "VisitorType_Other                   bool\n",
      "VisitorType_Returning_Visitor       bool\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Fixing feature order\n",
    "# Get feature names from the trained model\n",
    "train_features = rf.feature_names_in_ \n",
    "\n",
    "# Reorder X_test features to match training order\n",
    "X_test = X_test[train_features]\n",
    "\n",
    "# Make predictions\n",
    "y_pred = rf.predict(X_test)\n",
    "\n",
    "print(df.dtypes) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_proba = rf.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prediction -- Eve "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False]\n",
      "[[0.88160444 0.11839556]]\n"
     ]
    }
   ],
   "source": [
    "print(y_pred)\n",
    "print(y_proba)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comment: The random forest classification model predits Revenue is False with a probability of 88% -- Eve is a cold lead. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### New data point -- Jezebel   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the new data point  \n",
    "data = '../src/data/new_data_point_jezebel.csv'\n",
    "X_test = pd.read_csv(data)\n",
    "\n",
    "# One-hot encoding for Month\n",
    "X_test = pd.get_dummies(X_test, columns=['Month'], dummy_na=False) \n",
    "\n",
    "# Define all possible months\n",
    "all_months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "\n",
    "# Add columns for missing months with initial values of False\n",
    "for month in all_months:\n",
    "    if f'Month_{month}' not in X_test.columns:\n",
    "        X_test[f'Month_{month}'] = False \n",
    "\n",
    "# One-hot encoding for VisitorType\n",
    "X_test = pd.get_dummies(X_test, columns=['VisitorType']) \n",
    "\n",
    "# Define all possible visitor \n",
    "all_visitors = ['Returning_Visitor' 'New_Visitor' 'Other']\n",
    "\n",
    "# Add columns for missing visitor with initial values of False\n",
    "if 'VisitorType_Returning_Visitor' not in X_test.columns:\n",
    "    X_test['VisitorType_Returning_Visitor'] = False \n",
    "if 'VisitorType_New_Visitor' not in X_test.columns:\n",
    "    X_test['VisitorType_New_Visitor'] = False \n",
    "if 'VisitorType_Other' not in X_test.columns:\n",
    "    X_test['VisitorType_Other'] = False "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Administrative                     int64\n",
      "Administrative_Duration          float64\n",
      "Informational                      int64\n",
      "Informational_Duration           float64\n",
      "ProductRelated                     int64\n",
      "ProductRelated_Duration          float64\n",
      "BounceRates                      float64\n",
      "ExitRates                        float64\n",
      "PageValues                       float64\n",
      "SpecialDay                       float64\n",
      "OperatingSystems                   int64\n",
      "Browser                            int64\n",
      "Region                             int64\n",
      "TrafficType                        int64\n",
      "Weekend                             bool\n",
      "Revenue                             bool\n",
      "Month_Aug                           bool\n",
      "Month_Dec                           bool\n",
      "Month_Feb                           bool\n",
      "Month_Jul                           bool\n",
      "Month_Jun                           bool\n",
      "Month_Mar                           bool\n",
      "Month_May                           bool\n",
      "Month_Nov                           bool\n",
      "Month_Oct                           bool\n",
      "Month_Sep                           bool\n",
      "Month_Jan                           bool\n",
      "Month_Apr                           bool\n",
      "VisitorType_New_Visitor             bool\n",
      "VisitorType_Other                   bool\n",
      "VisitorType_Returning_Visitor       bool\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Fixing feature order\n",
    "# Get feature names from the trained model\n",
    "train_features = rf.feature_names_in_ \n",
    "\n",
    "# Reorder X_test features to match training order\n",
    "X_test = X_test[train_features]\n",
    "\n",
    "# Make predictions\n",
    "y_pred = rf.predict(X_test)\n",
    "\n",
    "print(df.dtypes) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_proba = rf.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prediction -- Jezebel "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True]\n",
      "[[0.27524375 0.72475625]]\n"
     ]
    }
   ],
   "source": [
    "print(y_pred)\n",
    "print(y_proba)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comment: The random forest classification model predits Revenue is True with a probability of 72% -- Jezebel is a hot lead. "
   ]
  }
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