{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e6ef4a92-399a-4213-88f5-8d4ea89987bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: namex, libclang, flatbuffers, wrapt, werkzeug, typing-extensions, termcolor, tensorflow-io-gcs-filesystem, tensorboard-data-server, protobuf, opt-einsum, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-nvcc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, mdurl, markdown, grpcio, google-pasta, gast, astunparse, absl-py, tensorboard, optree, nvidia-cusparse-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, ml-dtypes, markdown-it-py, h5py, rich, nvidia-cusolver-cu12, keras, tensorflow\n",
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      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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      "\u001b[?25hDownloading kiwisolver-1.4.8-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB)\n",
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      "\u001b[?25hInstalling collected packages: kiwisolver, fonttools, cycler, contourpy, matplotlib\n",
      "Successfully installed contourpy-1.3.2 cycler-0.12.1 fonttools-4.57.0 kiwisolver-1.4.8 matplotlib-3.10.1\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
      "Collecting huggingface_hub\n",
      "  Downloading huggingface_hub-0.30.2-py3-none-any.whl.metadata (13 kB)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.9.0)\n",
      "Collecting fsspec>=2023.5.0 (from huggingface_hub)\n",
      "  Downloading fsspec-2025.3.2-py3-none-any.whl.metadata (11 kB)\n",
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      "\u001b[?25hDownloading tqdm-4.67.1-py3-none-any.whl (78 kB)\n",
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      "\u001b[?25hInstalling collected packages: tqdm, fsspec, huggingface_hub\n",
      "  Attempting uninstall: fsspec\n",
      "    Found existing installation: fsspec 2023.4.0\n",
      "    Uninstalling fsspec-2023.4.0:\n",
      "      Successfully uninstalled fsspec-2023.4.0\n",
      "Successfully installed fsspec-2025.3.2 huggingface_hub-0.30.2 tqdm-4.67.1\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow[and-cuda]\n",
    "!pip install scikit_learn\n",
    "!pip install matplotlib\n",
    "!pip install huggingface_hub\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "faa5e1a8-6293-4605-a4b8-b648c451881a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xgboost\n",
      "  Downloading xgboost-3.0.0-py3-none-manylinux_2_28_x86_64.whl.metadata (2.1 kB)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xgboost) (2.1.3)\n",
      "Requirement already satisfied: nvidia-nccl-cu12 in /usr/local/lib/python3.10/dist-packages (from xgboost) (2.23.4)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.15.2)\n",
      "Downloading xgboost-3.0.0-py3-none-manylinux_2_28_x86_64.whl (253.9 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m253.9/253.9 MB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: xgboost\n",
      "Successfully installed xgboost-3.0.0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "de80513a-10fe-400f-8d58-a56e3d288ad2",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-04-19 16:33:02.534945: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "E0000 00:00:1745080382.556127     375 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "E0000 00:00:1745080382.562634     375 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "W0000 00:00:1745080382.579592     375 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
      "W0000 00:00:1745080382.579620     375 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
      "W0000 00:00:1745080382.579622     375 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
      "W0000 00:00:1745080382.579624     375 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
      "2025-04-19 16:33:02.584946: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow\n",
    "from tensorflow import keras\n",
    "from keras import layers\n",
    "from huggingface_hub import notebook_login, HfApi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e555780e-3948-4105-895f-5d6c470b6f7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "\n",
    "zf = zipfile.ZipFile('/workspace/train.zip', 'r')\n",
    "zf.extractall('/workspace/extractedData/')\n",
    "zf.close()\n",
    "\n",
    "zf = zipfile.ZipFile('/workspace/test.zip', 'r')\n",
    "zf.extractall('/workspace/extractedData/')\n",
    "zf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "79f9b2c2-f024-4372-87d2-87b2713ece67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 10129 images belonging to 8 classes.\n",
      "Found 1600 images belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "dataGen = ImageDataGenerator(\n",
    "    rescale=1./255\n",
    ")\n",
    "\n",
    "train_data = dataGen.flow_from_directory(\n",
    "    '/workspace/extractedData/train',\n",
    "    target_size = (224, 224),\n",
    "    batch_size = 32,\n",
    "    class_mode = 'categorical',\n",
    "    shuffle=False\n",
    ")\n",
    "\n",
    "test_data = dataGen.flow_from_directory(\n",
    "    '/workspace/extractedData/test',\n",
    "    target_size = (224, 224),\n",
    "    batch_size = 32,\n",
    "    class_mode = 'categorical',\n",
    "    shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f86b0c2-e30a-4a11-81ea-dbb3356bf3a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "703e3bc3bde944f89378fd82817190dc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "6d844388-7c59-46d0-aeca-dde00936f80d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d23882dc51ad4285837a054cb994104f",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "api = HfApi()\n",
    "vggDir2 = api.snapshot_download('madhavsinghabcde/final_trained_Vgg16_all_Layers', repo_type = 'model')\n",
    "vgg_model2 = keras.models.load_model(vggDir2+'/final_trained_Vgg16_all_Layers.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "711d0417-3776-4ab7-b9d3-26ab090b2207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a3cb9f6aea114744b6d880ceea496665",
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "57444e798b1544818e51956b950c4c01",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "038c1143b82f4a0f863c9964a41a40a3",
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     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0768fe673b374c3bb60e31b0213c8bd2",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resNetDir = api.snapshot_download('madhavsinghabcde/ResNet_checkpoint_train_conv3_Unfrozen', repo_type='model')\n",
    "resNet_model = keras.models.load_model(resNetDir+'/checkpoint_train_conv3_Unfrozen.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "732e110a-819a-46ac-8b36-648d5ee5c3ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4bca69c6385545c4aff648377ced4b20",
       "version_major": 2,
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      ]
     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "57c09ae4a32d4c6ca2504de31e22b017",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "78a38b2ad46c4dcbb15273a499c2da69",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e7da5b9e02f47ba93288789e327ff6d",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "denseNetDir = api.snapshot_download('madhavsinghabcde/checkPointedModel_allLayersUnfrozen_denseNet', repo_type='model')\n",
    "denseNet_model = keras.models.load_model(denseNetDir+'/checkPointedModel_allLayersUnfrozen.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a4fc387a-ef25-4420-b9e3-6efda7a5fd51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resnet50_resnet False\n",
      "global_average_pooling2d_resnet False\n",
      "batch_normalization_resnet False\n",
      "dropout_resnet False\n",
      "dense_resnet False\n",
      "batch_normalization_1_resnet False\n",
      "dropout_1_resnet False\n",
      "dense_1_resnet False\n"
     ]
    }
   ],
   "source": [
    "for layer in resNet_model.layers:\n",
    "    layer.trainable=False\n",
    "    layer.name = layer.name + '_resnet'\n",
    "    print(layer.name, layer.trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "acf61fea-bc63-43c1-8208-0a6ecd5c521a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "densenet121_densenet False\n",
      "global_average_pooling2d_2_densenet False\n",
      "batch_normalization_4_densenet False\n",
      "dropout_4_densenet False\n",
      "dense_4_densenet False\n",
      "batch_normalization_5_densenet False\n",
      "dropout_5_densenet False\n",
      "dense_5_densenet False\n"
     ]
    }
   ],
   "source": [
    "for layer in denseNet_model.layers:\n",
    "    layer.trainable=False\n",
    "    layer.name = layer.name + '_densenet'\n",
    "    print(layer.name, layer.trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "30f33333-412f-4687-901b-4cbf68ac9b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vgg16_vggnet False\n",
      "global_average_pooling2d_2_vggnet False\n",
      "batch_normalization_4_vggnet False\n",
      "dropout_4_vggnet False\n",
      "dense_4_vggnet False\n",
      "batch_normalization_5_vggnet False\n",
      "dropout_5_vggnet False\n",
      "dense_5_vggnet False\n"
     ]
    }
   ],
   "source": [
    "for layer in vgg_model2.layers:\n",
    "    layer.trainable=False\n",
    "    layer.name = layer.name + '_vggnet'\n",
    "    print(layer.name, layer.trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "299cedd6-15b4-4123-9bb4-671baab4a044",
   "metadata": {},
   "outputs": [],
   "source": [
    "intermediate_layer_name = 'batch_normalization_4_vggnet'\n",
    "vgg_input_layer = layers.Input(shape=vgg_model.input_shape[1:], name='vgg_input')\n",
    "vgg_inp = vgg_input_layer\n",
    "\n",
    "for layer in vgg_model2.layers:\n",
    "    vgg_inp = layer(vgg_inp)\n",
    "        \n",
    "vgg_branch = Model(inputs=vgg_input_layer, outputs=vgg_inp, name='vgg_branch')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "intermediate_layer_name = 'batch_normalization_4_densenet'\n",
    "denseNet_input_layer = layers.Input(shape=denseNet_model.input_shape[1:], name='denseNet_input')\n",
    "denseNet_inp = denseNet_input_layer\n",
    "\n",
    "for layer in denseNet_model.layers:\n",
    "    denseNet_inp = layer(denseNet_inp)\n",
    "        \n",
    "densenet_branch = Model(inputs=denseNet_input_layer, outputs=denseNet_inp, name='denseNet_branch')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "intermediate_layer_name = 'batch_normalization_4_resnet'\n",
    "resNet_input_layer = layers.Input(shape=resNet_model.input_shape[1:], name='resNet_input')\n",
    "resNet_inp = resNet_input_layer\n",
    "\n",
    "for layer in resNet_model.layers:\n",
    "    resNet_inp = layer(resNet_inp)\n",
    "        \n",
    "resnet_branch = Model(inputs=resNet_input_layer, outputs=resNet_inp, name='resNet_branch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6bf86bd3-6ed6-4e20-b045-6603c3ef3d60",
   "metadata": {},
   "outputs": [],
   "source": [
    "tensorflow.keras.models.save_model(ensemble, '/workspace/ensemble.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "39ec83fc-fd91-4026-a319-92a40e27eabb",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 340ms/step - accuracy: 0.9987 - loss: 0.0105 - val_accuracy: 0.5838 - val_loss: 1.6664\n",
      "Epoch 2/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 249ms/step - accuracy: 0.9980 - loss: 0.0096 - val_accuracy: 0.5838 - val_loss: 1.7475\n",
      "Epoch 3/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 244ms/step - accuracy: 0.9987 - loss: 0.0050 - val_accuracy: 0.5813 - val_loss: 1.8249\n",
      "Epoch 4/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 249ms/step - accuracy: 0.9982 - loss: 0.0058 - val_accuracy: 0.5825 - val_loss: 1.8485\n",
      "Epoch 5/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 246ms/step - accuracy: 0.9995 - loss: 0.0025 - val_accuracy: 0.5831 - val_loss: 1.8936\n",
      "Epoch 6/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 245ms/step - accuracy: 0.9985 - loss: 0.0068 - val_accuracy: 0.5825 - val_loss: 1.9156\n",
      "Epoch 7/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 248ms/step - accuracy: 0.9990 - loss: 0.0049 - val_accuracy: 0.5844 - val_loss: 1.9332\n",
      "Epoch 8/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 247ms/step - accuracy: 0.9988 - loss: 0.0047 - val_accuracy: 0.5838 - val_loss: 1.9370\n",
      "Epoch 9/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 248ms/step - accuracy: 0.9988 - loss: 0.0056 - val_accuracy: 0.5869 - val_loss: 1.9571\n",
      "Epoch 10/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 246ms/step - accuracy: 0.9986 - loss: 0.0056 - val_accuracy: 0.5850 - val_loss: 1.9702\n",
      "Epoch 11/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 246ms/step - accuracy: 0.9988 - loss: 0.0043 - val_accuracy: 0.5863 - val_loss: 2.0048\n",
      "Epoch 12/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 235ms/step - accuracy: 0.9985 - loss: 0.0060 - val_accuracy: 0.5863 - val_loss: 2.0171\n",
      "Epoch 13/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 248ms/step - accuracy: 0.9985 - loss: 0.0069 - val_accuracy: 0.5894 - val_loss: 2.0478\n",
      "Epoch 14/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 246ms/step - accuracy: 0.9988 - loss: 0.0036 - val_accuracy: 0.5894 - val_loss: 2.0545\n",
      "Epoch 15/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 250ms/step - accuracy: 0.9989 - loss: 0.0037 - val_accuracy: 0.5888 - val_loss: 2.0351\n"
     ]
    }
   ],
   "source": [
    "\n",
    "ensemble.compile(\n",
    "    optimizer=keras.optimizers.Adam(1e-3),\n",
    "    loss='categorical_crossentropy',\n",
    "    metrics=['accuracy']    \n",
    ")\n",
    "\n",
    "Ensemble_train_history2 = ensemble.fit(\n",
    "    train_data,\n",
    "    validation_data = test_data,\n",
    "    epochs=15\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c0049e8a-add4-4956-8ad8-ee8b41f1ee84",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 347ms/step - accuracy: 0.9990 - loss: 0.0052 - val_accuracy: 0.5869 - val_loss: 2.1157\n",
      "Epoch 2/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 235ms/step - accuracy: 0.9984 - loss: 0.0056 - val_accuracy: 0.5875 - val_loss: 2.1462\n",
      "Epoch 3/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 235ms/step - accuracy: 0.9988 - loss: 0.0047 - val_accuracy: 0.5881 - val_loss: 2.1866\n",
      "Epoch 4/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 246ms/step - accuracy: 0.9986 - loss: 0.0045 - val_accuracy: 0.5888 - val_loss: 2.2312\n",
      "Epoch 5/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 236ms/step - accuracy: 0.9988 - loss: 0.0039 - val_accuracy: 0.5881 - val_loss: 2.2479\n",
      "Epoch 6/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 234ms/step - accuracy: 0.9988 - loss: 0.0045 - val_accuracy: 0.5906 - val_loss: 2.2606\n",
      "Epoch 7/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 235ms/step - accuracy: 0.9990 - loss: 0.0029 - val_accuracy: 0.5838 - val_loss: 2.2446\n",
      "Epoch 8/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 237ms/step - accuracy: 0.9992 - loss: 0.0032 - val_accuracy: 0.5875 - val_loss: 2.2812\n",
      "Epoch 9/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 233ms/step - accuracy: 0.9988 - loss: 0.0061 - val_accuracy: 0.5875 - val_loss: 2.2809\n",
      "Epoch 10/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 235ms/step - accuracy: 0.9987 - loss: 0.0035 - val_accuracy: 0.5875 - val_loss: 2.2802\n",
      "Epoch 11/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 236ms/step - accuracy: 0.9986 - loss: 0.0057 - val_accuracy: 0.5856 - val_loss: 2.2694\n",
      "Epoch 12/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 238ms/step - accuracy: 0.9987 - loss: 0.0053 - val_accuracy: 0.5863 - val_loss: 2.3105\n",
      "Epoch 13/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 237ms/step - accuracy: 0.9986 - loss: 0.0044 - val_accuracy: 0.5888 - val_loss: 2.2751\n",
      "Epoch 14/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 234ms/step - accuracy: 0.9986 - loss: 0.0060 - val_accuracy: 0.5825 - val_loss: 2.2592\n",
      "Epoch 15/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 233ms/step - accuracy: 0.9988 - loss: 0.0042 - val_accuracy: 0.5894 - val_loss: 2.3089\n"
     ]
    }
   ],
   "source": [
    "ensemble.compile(\n",
    "    optimizer=keras.optimizers.Adam(1e-3),\n",
    "    loss='categorical_crossentropy',\n",
    "    metrics=['accuracy']    \n",
    ")\n",
    "\n",
    "Ensemble_train_history2 = ensemble.fit(\n",
    "    train_data,\n",
    "    validation_data = test_data,\n",
    "    epochs=15\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a5bd174e-3708-4d99-9195-fbbd6865b4b6",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 195ms/step - accuracy: 0.5854 - loss: 2.3234\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[2.3089075088500977, 0.5893750190734863]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "382eb326-f001-4161-aa0d-8607c38be13c",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 1])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "preds = ensemble.predict(test_data)\n",
    "\n",
    "y_preds = np.argmax(preds, axis=1)\n",
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "43ab6eed-9637-40be-ba1f-9f8dba8934d3",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axis.YTick at 0x726f3560d120>]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cm = confusion_matrix(y_preds, test_data.classes)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "im = ax.imshow(cm, interpolation='nearest')\n",
    "fig.colorbar(im, ax=ax)\n",
    "\n",
    "classes = [test_data.class_indices]\n",
    "ax.set_xticks(np.arange(len(classes)))\n",
    "ax.set_yticks(np.arange(len(classes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a0aeafe9-4fc1-4e0f-9b0d-50dd83d981fe",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1) Suppose you used flow_from_directory:\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "class_indices = test_data.class_indices\n",
    "# e.g. {'angry':0, 'happy':1, …}\n",
    "\n",
    "# 2) Invert it to get a list of class names in index order:\n",
    "index_to_class = {v:k for k,v in class_indices.items()}\n",
    "class_names = [index_to_class[i] for i in range(len(index_to_class))]\n",
    "\n",
    "# 3) Compute your confusion matrix\n",
    "cm = confusion_matrix(test_data.classes, y_preds)\n",
    "\n",
    "# 4) Plot with labels\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap='Blues', xticks_rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9424543b-2d94-4183-8486-81345139c688",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5y4agP4mOS6SIlRmV/Dz5Yf5AbK2zn/VYEP4ODqHzV0tUt6cs/hWAj5tWYUiPpuz7X1dXs55z1I77ccHAQn86ATUf8Bl7FRkZGQXbT6Ah+/fv58svv+Sff/7B29ubhQsXUq9ePYKCgihfvjweHh5cuHCB8uXLAxAVFYW1tTWHDh2iXr16QOYU60mTJhEZGUlAQACVK1dm8eLFhIaGqh7n999/Z/LkyVy4cAF9fX18fHzo3bs3ffr0ATK7qYKCgmjTpk2Os8fExGBpaUmD2fvRM9ae5s6VnctrOsI7iUsqvM3fr+NirWVTQv/HyEA7Wnj+Cx4/T3z7ToWMmZH2/c6OjYmhRNEiREdHY2HxfovQF98NhvUmotB797E8GWlJJB+emOPMR48eZfbs2Zw/f57Q0NBsv9OuX7/O6NGjOXLkCGlpaZQqVYpffvkFN7fM000kJSUxfPhwfvzxR5KTkwkICGDp0qU4OORuAoT2vUPeUaNGjbh27Zraun/Xb6/WclZWVlnW9enTR1WMvLj9atdRQEAAAQEBr83xgdSMQgghCkoBt8TEx8dTrlw5evbsSbt2WVu/79y5Q61atejVqxeTJk3CwsKCq1evqo0NHTp0KLt37+bnn3/G0tKSQYMG0a5dO44fz90kiA+miMkPc+bMoXHjxpiamrJnzx7WrVvH0qVLNR1LCCGEKDDNmjWjWbNmr93+zTff0Lx5c2bNmqVaV7x4cdX/R0dHs3r1ajZt2kSDBg0AWLNmDb6+vpw6dYrq1XPeha29HWEacObMGRo3boyfnx/Lly9n4cKF9O7dW9OxhBBCfMjyaXZSTEyM2vLv85XllFKpZPfu3ZQsWZKAgADs7e2pVq0a27dvV+1z/vx5UlNTadSokWqdj48Pbm5uWc6U/zZSxOTCli1bCA8PJzExkatXr/LFF19oOpIQQogPXT6dJ8bV1VXtHGWBgYG5jhIeHk5cXBwzZsygadOm/PHHH7Rt25Z27dpx5MgRAMLCwjAwMMhy2R4HBwfCwsJy9XjSnSSEEEIIQkJC1Ab2vsupPl6cW+2jjz5i6NChAJQvX54TJ06wfPly6tatmz9h/0daYoQQQghtlk/dSRYWFmrLuxQxRYoUQU9Pj1Kl1E+Q6evrqzpDvqOjIykpKURFRant8+TJExwd1a/H9TZSxAghhBBaLa9dSflXChgYGFClShWCg4PV1t+8eZNixTJPRFqpUiX09fU5cODlObOCg4N58OAB/v65u8SJdCcJIYQQIsfi4uK4ffu26vbdu3e5ePEiNjY2uLm5MXLkSDp16kSdOnWoX78+e/fuZefOnRw+fBgAS0tLevXqxbBhw7CxscHCwoLBgwfj7++fq5lJIEWMEEIIod3yev2jXB577tw56tevr7o9bNgwALp168batWtp27Yty5cvJzAwkC+//BJvb29++eUXatWqpTpm3rx56Ojo0L59e7WT3eWWFDFCCCGENlMo8niyu9wVMfXq1XvriVt79uxJz549X7vdyMiIJUuWsGTJktfukxNSxAghhBDa7AO+dpL2JhdCCCHEB01aYoQQQghtVsBjYgoTKWKEEEIIbSbdSUIIIYQQ2kVaYoQQQghtJt1JQgghhNBKH3B3khQxWmRJh7KY/+viXIVdw5mHNR3hnRz9poGmI+Sajo72/pISBUP55tN6FErpWhhaI5k/4JYY7S2/hBBCCPFBk5YYIYQQQospFAoUH2hLjBQxQgghhBb7kIsY6U4SQgghhFaSlhghhBBCmyn+t+TleC0lRYwQQgihxaQ7SQghhBBCy0hLjBBCCKHFPuSWGClihBBCCC0mRYwQQgghtNKHXMTImBghhBBCaCVpiRFCCCG0mUyxFkIIIYQ2ku4kIYQQQggtIy0xQgghhBZTKMhjS0z+ZSloUsQIIYQQWkxBHruTtLiKkSLmA3Hm0h1W/XSYq7ceEh4Rw7LJ3Wlcy0+1vUSD4dkeN7pvS/p0rl8gGSu6W9Otlju+zhbYWxgx9IcLHLoeDoCejoKBjbyoVbIIRW2MiU1K4/SdCBb+cYunsckAVPaw5rteVbO97y7LTnL1Ucx7fw6nL95hxY8HuRyc+TqvnNaTgNqZr3NqWjpzVv3GoVPXeRAagbmpEbUql+Trfi1xKGL53rPlxuzvfmPO6r1q60q42XP8p7EaSpRzq7YcYdHGA4RHxFDGy4WZIztQqbS7pmO9VWHO/d2PB9l//DJ3Q55iZKBHuVLuDO3VHA9X+yz7ZmRk0H/sao6fC2b+hG40rFFGA4kznbl0h5U/HuLKzcy/x+VTetCk9svPvZGBm/nl97Nqx9Sp4s3a2f0KOmqefMhjYqSI+UAkJqXgW9yZDs2qMmDC2izbT26doHb7yOkbjJmzhYA6ZQsoIRjr63IzLJbt5x8xr0sFtW1G+rr4Opuz6vAdgsNisTDSZ1QLH+Z/VoEuy04BcPFBFA1nHFI7bmAjL6p62hRIAQOQkJSCb3EXOjavRr+xa9S2JSalcOXWQ77s1hjfEi5ExyYwaWEQvcZ8x65V2ReRmuTt6cTWhQNVt3V1C/8Qum1/nGfs/CDmft2JSmXcWb75EO0HL+Hs1vHY2ZhrOt5rFfbc5/6+Q+dWNShT0pX0dCUL1u6h3/+tYvuqkZgYGajtuyHozzy2CuSfhBefe82r0n/c2mz3qVvVh1mjO6tuGxjI16I2+U//a7m7uzNkyBCGDBmi6SgATJw4ke3bt3Px4sUCf+y61XypW833tdvtbCzUbu8/cYXq5Yvj5mz7vqOpHL/1jOO3nmW7LS45jS/WnldbN2PXdX7o74+jpRFh0UmkpWcQEZei2q6no6Cejx2bTz14r7n/rX51X+pXz/51tjAz5oe5/dXWTR7Sntb95vHoyXNcHKwLImKO6enqYG9r8fYdC5Glmw7StU0NurT2B2DumM78cfwqG3ecZGj3JhpO93qFPffy6X3Ubk8d3om6nSZx7dZDKvt5qtbfuPOIdb8c5adFX1L/kykFHTOLetV8qfeGzz0AA3097LTsfZ7FBzzFuvD/tBIF7llkLIdPXadD82qajvJGZkZ6KJUZxCalZru9ro89liYG/PrXowJOlnOx8YkoFAoszIw1HSWLf0KeUrbVWKq0n0T/Cet4GBap6UhvlJKaxsUbIdSr6q1ap6OjQ92q3py9fFeDyd5MG3PHxScBYGluolqXmJTC6Bmb+GZgG4rYaE9RcOribaq0GU/DzwMZO3crz6PjNR0p9/7XnfSuizZ3J2m0iFEqlcyaNYsSJUpgaGiIm5sb06ZNA+Dy5cs0aNAAY2NjbG1t6du3L3Fxcapju3fvTps2bZgzZw5OTk7Y2toycOBAUlMzv9Dq1avH/fv3GTp0aJb+wmPHjlG7dm2MjY1xdXXlyy+/JD7+5RvX3d2dqVOn0rVrV8zMzChWrBg7duzg6dOnfPTRR5iZmVG2bFnOnTunOmbt2rVYWVmxfft2vLy8MDIyIiAggJCQENX2SZMmcenSJVWetWvXvs+X951t++MspiaGqrEchZGBng5fNSnJ3suhxCenZ7tP20ounLz1jPCY5AJOlzNJyakELt9F64YVMDc10nQcNRVLu7NwbBc2z+vPrJEdefA4go/6L1B9eRVGEVFxpKcrs3S/2NlYEB5RMN2J70LbciuVSmYu30GF0u54uTuq1s9asYPypdxpoMExMLlVp6oP3/7fp2yY+wWj+7bkzKU79Bi9kvR0paajiRzSaBEzZswYZsyYwbhx47h27RqbNm3CwcGB+Ph4AgICsLa25uzZs/z888/s37+fQYMGqR1/6NAh7ty5w6FDh1i3bh1r165VFQbbtm2jaNGiTJ48mdDQUEJDQwG4c+cOTZs2pX379vz999/89NNPHDt2LMt9z5s3j5o1a3LhwgVatGjB559/TteuXfnss8/466+/KF68OF27diUjI0N1TEJCAtOmTWP9+vUcP36cqKgoOnfO7Gvt1KkTw4cPp3Tp0qo8nTp1yvZ1SU5OJiYmRm0pSFv3nKF1w4oYGugX6OPmlJ6OglmdyqFQKJi241q2+9hbGOLvVYSg84WzFSY1LZ2BE9aRkZHBtOEdNB0ni4b+pWjdsAKlS7hQv7ovm+Z+QXRsIr8euKDpaELDpi0O4vb9MGaN6aJad+jkVc5cvMPoL1prMFnutWpYgUY1y+Dj6UyT2n58F9ibv2+EcOribU1Hy5W8tMLkeVCwhmmsiImNjWXBggXMmjWLbt26Ubx4cWrVqkXv3r3ZtGkTSUlJrF+/njJlytCgQQMWL17Mhg0bePLkieo+rK2tWbx4MT4+PrRs2ZIWLVpw4MABAGxsbNDV1cXc3BxHR0ccHTN/MQQGBtKlSxeGDBmCl5cXNWrUYOHChaxfv56kpJe/Mps3b06/fv3w8vJi/PjxxMTEUKVKFTp06EDJkiUZPXo0169fV8uTmprK4sWL8ff3p1KlSqxbt44TJ05w5swZjI2NMTMzQ09PT5XH2Dj7LoTAwEAsLS1Vi6ur6/v4J8jW2b//4Z+Qp3RsUb3AHjM39HQUzOpcDicrY75Yc+61rTAfVXQhOiGFIzfCCzjh270oYB49ec4Pc/sXulaY7Fiam1DczZ67D59qOspr2VqZoaurw9PIWLX1TyNjCvXYHm3KPW1xEEdOX2f1rC9wtLNSrT9z8TYhoRHUaDee8s1GU77ZaACGTVlPj5HLNJQ299ycbbGxNOX+o+zH5hVWBV3EHD16lFatWuHs7IxCoWD79u2v3feLL75AoVAwf/58tfWRkZF06dIFCwsLrKys6NWrl1pvS05prIi5fv06ycnJNGzYMNtt5cqVw9TUVLWuZs2aKJVKgoODVetKly6Nrq6u6raTkxPh4W/+0rp06RJr167FzMxMtQQEBKBUKrl792X/c9myL2flODg4AODn55dl3b8fT09PjypVqqhu+/j4YGVlxfXr19+Y6VVjxowhOjpatbzokioIP+85TZmSRfEt7lxgj5lTLwoYN1sTvlhzlujE7MfCQGYRs/PiY9KUGa/dRxNeFDB3Hz7lh3n9sbY0fftBhUB8QjL3Hj4rdFPB/81AX4/yPq4cOfvyM0KpVHL07E2q+HloMNmbaUPujIwMpi0O4uCJK6ye1Y+ijjZq23t1qs8vy4fx87KhqgVgVL/WTBmefYtzYRQaHsXzmIRCVzwWNvHx8ZQrV44lS5a8cb+goCBOnTqFs3PW75MuXbpw9epV9u3bx65duzh69Ch9+/bNdRaNzU56XStEbujrq3d3KBQKlMo392XGxcXRr18/vvzyyyzb3Nzcsr3vF1Vqduve9njvwtDQEENDw3y9z/jEZLVfFyGhkVy7/QgrcxOc/zcrJjY+iT1H/mbMF63y9bFzythAFzeblwMFXayN8XY0JzoxlWexycz+pDy+zuZ8ueECOjoKbM0yp3ZGJ6aSlv6yWKnqaUNRGxOCzhV8V1J8QjL31F7nCK7eeoSVhQn2thb0H7eWKzcf8v3M3qSnK1VjHqwsTDDQLzyTBScu3E6TWqUp6mTDk6fRzPpuD7q6Cto2rqjpaG804NMGDJi0gQq+blQs7c6yzYeIT0ymS6vC2bL4QmHPPW1xEL8dusCCid0xNTbkWWTm+9bM1BgjQ32K2FhkO5jX0d4qS8FTkOITXvncC4vk2q1HWFqYYGVuwsJ1v9O0TlnsbCy4//gZM1fsophLEWpX8dFY5ndSwLOTmjVrRrNmzd64z6NHjxg8eDC///47LVq0UNt2/fp19u7dy9mzZ6lcuTIAixYtonnz5syZMyfboud1NPap6eXlhbGxMQcOHKB3795q23x9fVm7di3x8fGq1pjjx4+jo6ODt7d3dneXLQMDA9LT1bsbKlasyLVr1yhRokTen8Qr0tLSOHfuHFWrZp5wLTg4mKioKHx9fV+bp6BcDg7hs2Evm3WnL9sBQLuAyswa/QkAuw9dICMjg1YNKmR7H+9baRcLtZPVjWie+UGy469HLD94m/q+mSfW2jKohtpxvVef4dzd56rbbSsV5eL959x7VvCzDP4ODqHzVy9/nUxZ/CsAHzetwpAeTdl3/AoAzXrOUTvuxwUD8a+Q/+/Jd/X4aRRfTFjH8+h4bK3MqFquOL+tGkYRa82fs+RN2jWpxLOoOKav2E14RCx+JV3YunBgof9lXdhz/7TrJAA9Ry5XWz9leEfaNKmS3SGFwuXgED4dulR1e9qSzL/H9gFVmDKsPTf+CWXb7+eIiUvE3taC2lW8GdqzGYZadq6YvI5rye8xMUqlks8//5yRI0dSunTpLNtPnjyJlZWVqoABaNSoETo6Opw+fZq2bdvm+LE09i9lZGTE6NGjGTVqFAYGBtSsWZOnT59y9epVunTpwoQJE+jWrRsTJ07k6dOnDB48mM8//1zVjZMT7u7uHD16lM6dO2NoaEiRIkUYPXo01atXZ9CgQfTu3RtTU1OuXbvGvn37WLx4cZ6ek76+PoMHD2bhwoXo6ekxaNAgqlevripq3N3duXv3LhcvXqRo0aKYm5vne4vL61QvX4LbB7994z6dW/rTuaV/geTJzrm7zyk/9vfXbn/Ttn8b8/Pf+RUp1/wrlOD+0Xmv3f6mbYXJyindNR3hnfXtWJe+HetqOkauFebcl3+fXSDH5LfqFUrwz+G5r92+TsvOzPs6+VXEvDqJ5F17BWbOnImenl62PR4AYWFh2Nurn+1ZT08PGxsbwsLCcvVYGp2dNG7cOIYPH8748ePx9fWlU6dOhIeHY2Jiwu+//05kZCRVqlTh448/pmHDhrkuMiZPnsy9e/coXrw4dnZ2QOZYlyNHjnDz5k1q165NhQoVGD9+fK6ar17HxMSE0aNH8+mnn1KzZk3MzMz46aefVNvbt29P06ZNqV+/PnZ2dmzevDnPjymEEELkB1dXV7VJJYGBgbm+j/Pnz7NgwQLWrl1bILOeNNpmpqOjwzfffMM333yTZZufnx8HDx587bHZnWPl1dHP1atX59KlS1n2q1KlCn/88cdr7/vevXtZ1v17KjVktqq8ug6gXbt2tGvXLtv7NTQ0ZOvWra99XCGEECK38qslJiQkBAuLl12Y79IK8+effxIeHq42xjQ9PZ3hw4czf/587t27h6OjY5ZJOGlpaURGRqpmEueUdnX8CSGEEEJNfhUxFhYWakXMu/j8889p1KiR2rqAgAA+//xzevToAYC/vz9RUVGcP3+eSpUqAXDw4EGUSiXVquXuTPFSxAghhBAix+Li4rh9++UJAV+M9bSxscHNzQ1bW/Vr7unr6+Po6KiamOPr60vTpk3p06cPy5cvJzU1lUGDBtG5c+dcD+2Qayflk+7duxMVFaXpGEIIIT40inxYcuHcuXNUqFCBChUyZ7IOGzZMNb40p3744Qd8fHxo2LAhzZs3p1atWqxcuTJ3QZCWGCGEEEKrFfQU63r16mU7JvR1shtnamNjw6ZNm3L1uNmRlhghhBBCaCVpiRFCCCG0WGE72V1BkiJGCCGE0GJSxAghhBBCOxXwtZMKExkTI4QQQgitJC0xQgghhBaT7iQhhBBCaKUPuYiR7iQhhBBCaCVpiRFCCCG0mII8tsRo8cheKWKEEEIILfYhdydJESOEEEJoM5liLYQQQgihXaQlRos8fp6IaZq+pmPk2KEx9TUd4Z0EHrqj6Qi5NqKOp6YjvBNbMwNNR/hgGOhp32/WxJR0TUfItcTUgs8s3UlCCCGE0EofchGjfaW5EEIIIQTSEiOEEEJoNYUic8nL8dpKihghhBBCi2UWMXnpTsrHMAVMupOEEEIIoZWkJUYIIYTQZnnsTtLm88RIESOEEEJosQ95dpIUMUIIIYQW+5AH9sqYGCGEEEJoJWmJEUIIIbSYjo4CHZ13b07JyMOxmiZFjBBCCKHFpDtJCCGEEELLSEuMEEIIocVkdpIQQgghtJJ0JwkhhBBCaBlpiRFCCCG0mHQn/cfUq1eP8uXLM3/+fNzd3RkyZAhDhgzRdKxC44egI6z6YR/tW/gzuEcLYmITWLPlIOcu3ebJsyisLEypVcWXnp0bYWZqpLGcZy7dYeWPh7hy8yHhETEsn9KDJrX9VNtHBm7ml9/Pqh1Tp4o3a2f3K+ioKkqlknOHznDr72AS4hIwNTfFu7wvFetWVn1QHAzaz82LN9SOcy3hRovPW2siMpD5Wq/66RBX//daL5vSg8a1/NT2uX3/CbNW7uLMpTukpyspUcyBJZO64+xgraHU2Vu15QiLNh4gPCKGMl4uzBzZgUql3TUd660Kc+6zf99h9U+HuXLrIU8jYlgyqTuN/vX+eBYZy5xVuzh2/iaxcYlULuvJuEFtcS9qp7HMqzYfZN/xy9wNeYqRgR7lS7kzrHdzPFztVftMnL+VUxduER4Rg4mxIeVLFWNYrxZ4utm/4Z4LHyli/sPOnj2LqamppmMAcO/ePTw8PLhw4QLly5fXSIYbtx+yc99ZihdzVK179jyWiMgY+ndtSrGidjx5GsXclTt49jyWySM+0UhOgISkFHyLO9OheVX6j1ub7T51q/owa3Rn1W0DA82+pS8e+4tr565Qv20jrO1sePo4nMPbD2BgZIBf9XKq/VxLuFG/TUPVbV09XU3EVUl88Vo3q8qA8WuzbL//6Bmdv1xEh2bV+Kp7AGYmRty6F4ahhl/vV2374zxj5wcx9+tOVCrjzvLNh2g/eAlnt47HzsZc0/Feq7DnTkhMwbu4M+2bVWXQhLVq2zIyMhg4fg16erosndwDM1Mj1v58hB4jV7D7+5GYGBtqJPPZy3f4pHUN/Eq6kpauZMGaPfQZs4odq0ZiYmwAQCmvorRsUBEneyuiYxNYsmEffcas4o/1Y9DV1Z7RFjIm5j/Mzs4OExMTTccoFBISk5m64GdGfNFGrYXF082BySM/pUZlH1wcbanoV5zenzTm5LkbpKWnayxvvWq+DO/dnIDaZV+7j4G+Hna2FqrF0lyz/9ZhIaG4e3tQrKQ7FtYWFC9dgqLFXQl/9ERtP109XUzMTVWLobHmWrwA6lbzZViv5jR5zWs9d/Vv1K3my+gvWlHaqyjFXIrQqGYZbK01/wX7b0s3HaRrmxp0ae2Pj6cTc8d0xsTIgI07Tmo62hsV9tx1q/kytGezLK1zAPcePuPi9ftMHNKesj5ueLraM3FIe5JSUtl98IIG0mZaOb0PbZtUoYS7Iz7FnZk2ohOh4VFcu/VQtU/HFtWpXNYTF0cbSnkV5cvuAYQ9jeLRk0iN5dYGR48epVWrVjg7O6NQKNi+fbtqW2pqKqNHj8bPzw9TU1OcnZ3p2rUrjx8/VruPyMhIunTpgoWFBVZWVvTq1Yu4uLhcZ9H6IiY+Pp6uXbtiZmaGk5MT3377rdp2d3d35s+fD2T+Ypg4cSJubm4YGhri7OzMl19+qdo3NDSUFi1aYGxsjIeHB5s2bVI7/t69eygUCi5evKg6JioqCoVCweHDhwF4/vw5Xbp0wc7ODmNjY7y8vFizZg0AHh4eAFSoUAGFQkG9evXey2vyOgu+20n1it5ULlvirfvGJSRhYmKInq5mWwje5tTF21RpM56Gnwcydu5WnkfHazSPo6sTD+8+JOrZcwCehT0j7EEorl7F1PZ7fO8Ra2etZvPCjRzdeZikhEQNpM0ZpVLJ4VPX8ShqR/eRK6jadjzt+89n37HLmo6mJiU1jYs3QqhX1Vu1TkdHh7pVvTl7+a4Gk72ZtuZ+ISU1DUCtVU5HRwcDfV3OXyk8+WPjkwBe+0MnITGFoN/PUdTRBkc7qwJMlncKFKoupXdacnkZ6/j4eMqVK8eSJUuybEtISOCvv/5i3Lhx/PXXX2zbto3g4GBat1bvLu/SpQtXr15l37597Nq1i6NHj9K3b99cP/fC1Rb8DkaOHMmRI0f49ddfsbe35//+7//466+/su2u+eWXX5g3bx4//vgjpUuXJiwsjEuXLqm2d+3alWfPnnH48GH09fUZNmwY4eHhucozbtw4rl27xp49eyhSpAi3b98mMTHzC+rMmTNUrVqV/fv3U7p0aQwMDPL03HPjwLG/uXk3lOUzvnjrvlEx8WzYeohWjaoUQLJ3V6eqDwF1/CjqZMODRxHM+e43eoxeyS9LvtJYU3CFWpVISU7hx8U/oKPQQZmhpGqD6pQs+/ILyq2EG56+xTG3NicmMoYzB06ye+NO2vb+GB2dwve7IiIqjvjEZFZsPsjQns0Y1a8lR8/cYMD4tWyc259q5d9eFBeEiKg40tOVWbpf7GwsuHXvyWuO0jxtzf2Cp5s9zvbWfPvdb0we+jHGRgas3XqUsKfRPI2M0XQ8ILMQn7l8BxVKu+Pl4ai2bfOOE3z73W4Sk1LwKGrHqhl9MNDXrq/Ggu5OatasGc2aNct2m6WlJfv27VNbt3jxYqpWrcqDBw9wc3Pj+vXr7N27l7Nnz1K5cmUAFi1aRPPmzZkzZw7Ozs45zqJd/1KviIuLY/Xq1WzcuJGGDTPHF6xbt46iRYtmu/+DBw9wdHSkUaNG6Ovr4+bmRtWqVQG4ceMG+/fvV3tRv/vuO7y8vHKV6cGDB1SoUEF1H+7u7qptdnaZg9xsbW1xdHTM7nAAkpOTSU5OVt2OicnbB0H4sygWr9nNnHE9MDTQf+O+8QlJjJm+gWJF7enesUGeHvd9a9Wwgur/fTyd8SnuTL1Pp3Hq4m1qViqpkUx3rt7i1t83adS+Cdb2NkSEPeP4nj8xtcgc4AtQwu9lNluHItg62LJpwQYe33tEUU9XjeR+E6UyA4BGNUrTs0NdAEqVcOGvq/fYvPNkoSlihGbo6+myaFI3vpmzhaptxqGro4N/JS/qVPUhI0PT6TJNXRzErXthbJg7IMu2lg0rUKOSF08jYlmz9QjDp25k4/yBb/2s/C969bvG0NAQQ8O8j2mKjo5GoVBgZWUFwMmTJ7GyslJ9TwI0atQIHR0dTp8+Tdu2bXN834XvZ18u3Llzh5SUFKpVq6ZaZ2Njg7e3d7b7d+jQgcTERDw9PenTpw9BQUGkpWU2hQYHB6Onp0fFihVV+5coUQJr69zNvOjfvz8//vgj5cuXZ9SoUZw4cSLXzyswMBBLS0vV4uqaty+24H8e8zw6nj6jltKg43gadBzPpWv32PbbKRp0HE96uhLIHDMzauo6jI0NmDLqU/Q0PNg0t9ycbbGxNOX+o2cay3DyjxNUqFWREn4lsXUoQslyPpT1L8+FP8+/9hgLG0uMTIyIiYguwKQ5Z21pip6uDiXc1QvvEm72PH7yXEOpsrK1MkNXV4enkbFq659GxmBva6GhVG+nrbn/rUxJV35dOZxzv07l2M8TWD2jL1ExCbg62Wg6GlMXB3Hk1HXWzPoi224ic1NjirnYUbmsJ/PGfc7dkHD2H79S8EHzIE9dSf+a2eTq6qr23RMYGJjnbElJSYwePZpPPvkEC4vM93NYWBj29uozwPT09LCxsSEsLCxX96/VRUxuubq6EhwczNKlSzE2NmbAgAHUqVOH1NTUHB3/oqk/418/L149tlmzZty/f5+hQ4fy+PFjGjZsyIgRI3KVc8yYMURHR6uWkJCQXB3/qkp+xfl+7mC+mzNQtXgXd6FR7bJ8N2cguro6xCckMWLKWvT0dJn+9Wda+SskNDyK5zEJGv3gT0tNzTJdUaFQqL1nXhUXHUdSYhIm5oVjFt2rDPT18PNx458Q9a7Vuw+f4lKIplcb6OtR3seVI2eDVeuUSiVHz96kip+HBpO9mbbmzo65mTE2Vmbce/iUKzdDaFizjMayZGRkMHVxEAeOX+H72f0ompOCKgMyeDnOR1u86E7KywIQEhKi9t0zZsyYPOVKTU2lY8eOZGRksGzZsnx4pllpdXdS8eLF0dfX5/Tp07i5uQGZA2tv3rxJ3bp1sz3G2NiYVq1a0apVKwYOHIiPjw+XL1/G29ubtLQ0Lly4QKVKlQC4ffs2z5+//KX5ojsoNDSUChUyuzL+Pcj33/t169aNbt26Ubt2bUaOHMmcOXNUY2DS3zLjJ7+a8F4wMTbE081BbZ2RoT4W5iZ4ujmoCpjk5FS+GfUp8QnJxCdkdmdZWZhqbHxJfEKyWqtKSFgk1249wtLCBCtzExau+52mdcpiZ2PB/cfPmLliF8VcilC7io9G8gIU8/bgrz/PYWZljrWdDRFhT/n75EV8KpQCIDU5hXOHz+JZqjjGZibEPI/m1B8nsLSxxLWEm8Zyxye+8lqHRnLt9iOszE1wdrCmT6d6fDV5A1XKelK9QgmOnrnBwRPX+GF+1uZ5TRrwaQMGTNpABV83KpZ2Z9nmQ8QnJtOlVXVNR3ujwp47PjGZB/96fzwMi+T67UdY/u/9sefIJWwsTXG2tyb4bijTl2ynUc0y1Kqcfat4QZiyKIjfDl1g0aTumBgbqsbnmJsaY2SoT0hoBHsPX6JGpZJYW5ny5Gk03/10CEMDfepU8dVY7neRX+eJsbCwULWW5NWLAub+/fscPHhQ7X4dHR2zjDdNS0sjMjLyjUMtsqPVRYyZmRm9evVi5MiR2NraYm9vzzfffPPawZFr164lPT2datWqYWJiwsaNGzE2NqZYsWLY2trSqFEj+vbty7Jly9DX12f48OEYGxur/oGNjY2pXr06M2bMwMPDg/DwcMaOHav2GOPHj6dSpUqULl2a5ORkdu3aha9v5h+Evb09xsbG7N27l6JFi2JkZISlpeX7fZFy4OY/j7n+v2mHXQbNU9u2eelwnOw182v7cnAInw5dqro9bcmvALQPqMKUYe258U8o234/R0xcIva2FtSu4s3Qns00eu6SWs3rcPbgaf7cdYTE+MyT3ZWqXIZKdTMHSSt0dIh48ozgSzdISUrGxNwU1+KuVGlQXaPnirkcHMJn/3qtpy/NfK3bBVRh1tef0KR2WSYP/Zjlmw4wZVEQnq72LJ7Uncp+npqKnK12TSrxLCqO6St2Ex4Ri19JF7YuHFjou2UKe+4rwSF0Hf7yl3Tgsh0AtG1SmRmjP+FpRAwzlv1KxPM47Gws+KhJJQZ81lhTcQH4aVfm9PTuI5arrZ86oiNtm1TB0ECP81fusiHoT6LjEiliZUYlP09+mD8QW2szTUT+z3hRwNy6dYtDhw5ha2urtt3f35+oqCjOnz+vajQ4ePAgSqVSbXhITigy3tTOrQXi4uLo378/27Ztw9zcnOHDh7N79+5sz9i7fft2ZsyYwfXr10lPT8fPz4+pU6eqBgWHhobSq1cvDh48iKOjI4GBgQwZMoTJkyfTr1/mWWCvX79Or169uHjxIt7e3syaNYsmTZpw6NAh6tWrx9SpU9m0aRP37t3D2NiY2rVrM2/ePNX06u+++47Jkyfz6NEjateurZqa/SYxMTFYWlpy4MJ9TM0Lx4daTrgV0c7z88w68o+mI+TaiDqFq5jIKVuzgpuh96ELj0l++06FTNr/xutpk9jYGMoXdyQ6OjrfWjVe58V3Q8Vxu9A1evfu6PSkeP6a0jLHmePi4rh9+zaQecqQuXPnUr9+fWxsbHBycuLjjz/mr7/+YteuXTg4vOwFsLGxUfVINGvWjCdPnrB8+XJSU1Pp0aMHlStXZtOmTbnKrvVFzPv08OFDXF1d2b9/v6rQ0QQpYgqWFDEFR4qYgiNFTMHQRBFTafzuPBcx5ye3yHHmw4cPU79+/Szru3XrxsSJE1U/2l/14sc+ZJ7sbtCgQezcuRMdHR3at2/PwoULMTPLXSuYVncn5beDBw8SFxeHn58foaGhjBo1Cnd3d+rUqaPpaEIIIUShUK9evTdOVshJ24iNjU2uW12yI0XMv6SmpvJ///d//PPPP5ibm1OjRg1++OEH9PW1b6aOEEKID0QeT3aXyxP2FipSxPxLQEAAAQEBmo4hhBBC5NiHfBXrD+o8MUIIIYT475CWGCGEEEKLFfS1kwoTKWKEEEIILfYhdydJESOEEEJosQ+5JUbGxAghhBBCK0lLjBBCCKHFpDtJCCGEEFrpQy5ipDtJCCGEEFpJWmKEEEIILfYhD+yVIkYIIYTQYtKdJIQQQgihZaQlRgghhNBi0p0khBBCCK30IXcnSRGjRTwdzDC3MNN0jBzT09HOP4wx9YtrOkKufbHlb01HeCebulXSdIQPRnxSmqYj5Fp0QqqmI+RafGxygT+mgjy2xORbkoInY2KEEEIIoZWkJUYIIYTQYjoKBTp5aIrJy7GaJkWMEEIIocU+5IG90p0khBBCCK0kLTFCCCGEFpPZSUIIIYTQSjqKzCUvx2srKWKEEEIIbabIY2uKFhcxMiZGCCGEEFpJWmKEEEIILfYhz06SIkYIIYTQYor//ZeX47WVdCcJIYQQQitJS4wQQgihxWR2khBCCCG0kpwn5i127NiR4zts3br1O4cRQgghhMipHBUxbdq0ydGdKRQK0tPT85JHCCGEELlQ0LOTjh49yuzZszl//jyhoaEEBQWp1QkZGRlMmDCBVatWERUVRc2aNVm2bBleXl6qfSIjIxk8eDA7d+5ER0eH9u3bs2DBAszMzHKVJUcDe5VKZY4WKWCEEEKIgvXiKtZ5WXIjPj6ecuXKsWTJkmy3z5o1i4ULF7J8+XJOnz6NqakpAQEBJCUlqfbp0qULV69eZd++fezatYujR4/St2/fXD/3PI2JSUpKwsjIKC93ITQkLiGJ2at+Y+/Ryzx7HkeZki5M+qod5X3dNB3tjUKfRjF1yQ4OnrpOYlIq7kWLMP+bTwtN7tMX77Dix4NcDn5IeEQMK6f1JKC2HwCpaenMWfUbh05d50FoBOamRtSqXJKv+7XEoYhlgWX0cTCjZWkHPGxNsDYxYO7B25wLiVbb5+PyTtT3ssPUQJeb4XF8f+oBYbHJqu0f+TlSoaglxWxMSFMq6bP5UoHlf5tVW46waOMBwiNiKOPlwsyRHahU2l3Tsd6qMOf+fsshDp64wr2H4Rga6FPOtxhf9miOe1E71T4hoRHMX72bC1fvkZqaRo1KJRn1xUfYWptrMPlLm4KOsGrTPto392dQjxYAfLtiO39dvsOzyFiMjQwo7e1Gv88CcHOxe8u9FS4F3RLTrFkzmjVrlu22jIwM5s+fz9ixY/noo48AWL9+PQ4ODmzfvp3OnTtz/fp19u7dy9mzZ6lcuTIAixYtonnz5syZMwdnZ+ccZ8n1FOv09HSmTJmCi4sLZmZm/PPPPwCMGzeO1atX5/buCq2MjAz69u2LjY0NCoWCixcvajpSvho540f+PHuTBeM+Y//6UdSp4s0nQ5YS+jRK09FeKyomgVb9FqCnp8sPc7/gyKYxTBzcBitzE01HU0lISsG3uAtThrbPsi0xKYUrtx7yZbfG7P5uOCum9uCfB+H0GvNdgWY01NPh/vNE1pwOyXZ7qzIOBPja8/2p+4z77QZJaUq+buyF/r+mMOjpKDh9/zn7g58WVOwc2fbHecbOD2J072Yc3jCaMl4utB+8hKeRsZqO9kaFPff5y//QsYU/674dyLKpvUlLUzJg7HckJqUAme/tgWMz38crAvvw/Zz+pKalM2TyWpRKpSajA3Dj9kN27juLZzFHtfUlPV0YNaAd6+Z/xayx3SEDRk5ZS3q65jNrq7t37xIWFkajRo1U6ywtLalWrRonT54E4OTJk1hZWakKGIBGjRqho6PD6dOnc/V4uS5ipk2bxtq1a5k1axYGBgaq9WXKlOG77wr2w/h92rt3L2vXrmXXrl2EhoZSpkwZTUfKN4nJKfx25G++GdCK6uWL41HUjuG9muHuUoQNQcc1He+1Fm/cj4uDFQvGdqFiqWIUc7alXjUf3IsW0XQ0lfrVfRnZpzlN65TNss3CzJgf5vanZYMKFHezp2JpdyYPac/l4Ic8evK8wDJeehTDzxcec+5BVLbbm/o6sP3vMM6HRBPyPJFlx+5iZaJPZTcr1T6/XAplz7VwQp4nFkzoHFq66SBd29SgS2t/fDydmDumMyZGBmzccVLT0d6osOdeMqUXrRtXpngxR0p6OjNpWAfCnkZx7fZDAC5eu8fj8OdMGtYRL3cnvNydmDSsI9duPeLspTsazZ6YmMy0hT8z4os2mJuq9xy0alyFcqU8cLS3pqSnMz0/aUR4RDRhTwvu7zE/vJidlJcFICYmRm1JTk5+yyNnFRYWBoCDg4PaegcHB9W2sLAw7O3t1bbr6elhY2Oj2iencl3ErF+/npUrV9KlSxd0dXVV68uVK8eNGzdye3eF1p07d3BycqJGjRo4Ojqip5f/s9FTUlLy/T5zIj1dSXq6EkMDfbX1Rob6nPn7H41kyonfj12hnI8rvb9ZQ+nm39Co2yw2/npC07HyJDY+EYVCgYWZsaajAGBvZoC1iT5XHseo1iWmKrnzNB4vO1MNJnu7lNQ0Lt4IoV5Vb9U6HR0d6lb15uzluxpM9mbamDs2PnNsg6VZZitoSmoaChQY6L/8nDQ00EdHoeDCtXuaiKgyf/VOqlf0plLZEm/cLzEphb2H/sLJ3hp724Lr3s0PL7qT8rIAuLq6YmlpqVoCAwM1+8RyINdFzKNHjyhRIuubQalUkpqami+hNK179+4MHjyYBw8eoFAocHd3R6lUEhgYiIeHB8bGxpQrV46tW7eqjklPT6dXr16q7d7e3ixYsCDL/bZp04Zp06bh7OyMt7f3qw9dIMxMjKhUxp35a38n7Fk06elKfvn9HOev3iM8Iubtd6AhDx5HsC7oOJ6uRfhxXn+6ta3F2Hnb+Om3M5qO9k6SklMJXL6L1g0rZPmFqCmWxpmFbXSS+t9ydFKqalthFREVR3q6Ejsb9TEYdjYWhfp9rW25lUolc1bupHwpd0q4Z3bPlPVxw9hInwVrfiMxKYXEpBTmfbebdKWSZ5Gaew4Hj//NrX9C6fNp49fus/330zT7bDLNP5/M6Qs3mT2uO/r6H+Yp1EJCQoiOjlYtY8aMyfV9ODpmvieePHmitv7JkyeqbY6OjoSHh6ttT0tLIzIyUrVPTuX6X6pUqVL8+eefFCtWTG391q1bqVChQm7vrlBasGABxYsXZ+XKlZw9exZdXV0CAwPZuHEjy5cvx8vLi6NHj/LZZ59hZ2dH3bp1USqVFC1alJ9//hlbW1tOnDhB3759cXJyomPHjqr7PnDgABYWFuzbt++1j5+cnKzWjBcTk/8fAgvGfcbwwM1UbjMBXV0dypQsykeNKnI5OPtxEoWBUplBOR9X/u+LVgD4eRflxj+hrA86TqfmVTWcLndS09IZOGEdGRkZTBveQdNxhMixGct+5c79J3w/+wvVOmtLM2aO+YzAJUH8uOMEOgoFAXXL4VPcBR0dzVzdJvxZFIvX7Gb2uB4YGLy+AG9UqxyVyxYn4nksW3YcZ9Lcn1g8tc8bjyls3mWG0avHA1hYWGBhYZGnLB4eHjg6OnLgwAHKly8PZH6HnT59mv79+wPg7+9PVFQU58+fp1KlSgAcPHgQpVJJtWrVcvV4uS5ixo8fT7du3Xj06BFKpZJt27YRHBzM+vXr2bVrV27vrlCytLTE3NwcXV1dHB0dSU5OZvr06ezfvx9/f38APD09OXbsGCtWrKBu3bro6+szadIk1X14eHhw8uRJtmzZolbEmJqa8t1336mNJ3pVYGCg2n29D+4uRfhl8WASEpOJjU/CoYgl/cevxc258IwveZW9rQUlPdSrdC93B3YfLjwzY3LiRQHz6MlzNs8fUGhaYQCiEzNbYCyN9IlKTFOttzTS535kgqZi5YitlRm6ujpZBsM+jYzB3jZvH8zvkzblnrFsO3+euc53M7/AoYiV2jb/iiXZsXo0z6Pj0dPVwdzMmMZdpuDiWE4jWW/+85jn0fH0HbVUtU6pVPL39fsE7T3NH5smoqurg5mpEWamRhR1KkIpL1da95jGn2eu0bCWZnK/C8X/lrwcnxtxcXHcvn1bdfvu3btcvHgRGxsb3NzcGDJkCFOnTsXLywsPDw/GjRuHs7Oz6lwyvr6+NG3alD59+rB8+XJSU1MZNGgQnTt3ztXMJHiHIuajjz5i586dTJ48GVNTU8aPH0/FihXZuXMnjRu/vslOm92+fZuEhIQszy8lJUWt9WnJkiV8//33PHjwgMTERFJSUlSV6At+fn5vLGAAxowZw7Bhw1S3Y2JicHV1zfsTyYaJsSEmxoZExSRw5MwN/q9/4T3jctWyHtx5oN4E+U9IOEUdrTWUKPdeFDB3Hz7lxwUDsbYsXONMwuNSeJ6QSmknc+7/b9Cusb4Oxe1MC91MpFcZ6OtR3seVI2eDaVEv8wtIqVRy9OxNeneoo+F0r6cNuTMyMpi5/FcOnbzKqsB+uDjavHbfF+/pM5duExkdT91qpQoqppqKfsX5/tvBautmLt2Gm3MRPmlTB13drC1EGUBGBqSmyjnP3uTcuXPUr19fdfvF91W3bt1Yu3Yto0aNIj4+nr59+xIVFUWtWrXYu3ev2ilZfvjhBwYNGkTDhg1VJ7tbuHBhrrO8U8df7dq139gd8l8TFxcHwO7du3FxcVHbZmhoCMCPP/7IiBEj+Pbbb/H398fc3JzZs2dnmS5mavr2Ly1DQ0PV/b4vh09fJyMDirvZc+/RM6Yu+ZXibg50apG7pryC1LdTPVr1m8+CdX/QumEFLly7z4ZfTzJndCdNR1OJT0jm3qNnqtshoRFcvfUIKwsT7G0t6D9uLVduPuT7mb1JT1eqxjxYWZioDYp8nwz1dHA0f/n+sjM3pJi1MXEpaUTEp7L3+hPalnUiLDaZp7HJdKjgQlRCqtpsJltTfcwM9LA1NUBHoaCYdebA5LDYZJLTNDc9dcCnDRgwaQMVfN2oWNqdZZsPEZ+YTJdW1TWWKScKe+4ZS7ez58hF5o3rhomxIc/+12pkZmqEkWFmt8uv+87i4WqPtaUZf1+/z5yVO+nSppbauWQKkomxIR5u6jNkjAz1sTA3wcPNgcdPIjl04jKVy5bAysKUp5ExbA46iqGBHtUqltRI5ndV0NdOqlevHhkZGW+8v8mTJzN58uTX7mNjY8OmTZty9bjZeedPzXPnznH9+nUgc5zMi36t/6JSpUphaGjIgwcPqFu3brb7HD9+nBo1ajBgwADVujt3NDu18E1i45KYsWIXoU+jsLIwpVndsozu2wJ9Pd23H6whFUoV4/sZvZi+bBdz1/yOm5MtU75qS/uAym8/uID8HRxC569ensVyyuJfAfi4aRWG9GjKvuNXAGjWc47acT8uGIh/hTfPnsgvnrYmjGv6clD551UyW/mO3H7GiuP32XnlCYZ6OvT2L4aJgS43n8QxY/8tUpUvP7Q+Lu9M3RIvux4DW2f+2p6yN5jrT+IK5Hlkp12TSjyLimP6it2ER8TiV9KFrQsHFrpumVcV9tw//3YKgD5fr1BbP3FIB1o3zvz7u//wGYvX7iU6LhFne2t6dapPlza1CzxrThno63H5+n1+2X2C2LgkrK1MKevrzqKpfbG2zN2p7zXtQ76KtSLjTeVUNh4+fMgnn3zC8ePHsbKyAiAqKooaNWrw448/UrRo0feRs8DNnz+f+fPnc+/ePQDGjh3L8uXL+fbbb6lVqxbR0dEcP34cCwsLunXrxsKFCxk3bhxbtmzBw8ODDRs2sHDhQjw8PFQnyuvevTtRUVFs3749V1liYmKwtLTk7uMIzPM46Kog6WnpX4YmWxLe1Rdb/tZ0hHeyqdt/98dPYXM3PF7TEXItOkH7ZrzGx8bQqGIxoqOj8zxI9m1efDd0XHkMfeN3L7xSE+PY0rdWgWTOb7keNt67d29SU1O5fv06kZGRREZGcv36dZRKJb17934fGQuFKVOmMG7cOAIDA1WDknbv3o2HhwcA/fr1o127dnTq1Ilq1aoRERGh1iojhBBCiPyV65YYY2NjTpw4kWU69fnz56lduzYJCYV7BoM2kpaYgiUtMQVHWmIKjrTEFAxNtcQYmLx7S0xKgva2xOR6TIyrq2u2J7VLT0/P9dQoIYQQQuRNQQ/sLUxy3Z00e/ZsBg8ezLlz51Trzp07x1dffcWcOXPecKQQQgghRP7JUUuMtbW1WqUWHx9PtWrVVNcTSktLQ09Pj549e6pOZiOEEEKI9+9Dnp2UoyJm/vz57zmGEEIIId7Fh9ydlKMiplu3bu87hxBCCCHeQUFfdqAwydMpQpOSkkhJSVFbp20jm4UQQgihnXJdxMTHxzN69Gi2bNlCRERElu3p6XLNCSGEEKKg5NdVrLVRrmcnjRo1ioMHD7Js2TIMDQ357rvvmDRpEs7Ozqxfv/59ZBRCCCHEaygUeV+0Va5bYnbu3Mn69eupV68ePXr0oHbt2pQoUYJixYrxww8/0KVLl/eRUwghhBBCTa5bYiIjI/H09AQyx79ERkYCUKtWLY4ePZq/6YQQQgjxRi9mJ+Vl0Va5LmI8PT25e/cuAD4+PmzZsgXIbKF5cUFIIYQQQhSMD7k7KddFTI8ePbh06RIAX3/9NUuWLMHIyIihQ4cycuTIfA8ohBBCCJGdXI+JGTp0qOr/GzVqxI0bNzh//jwlSpSgbNmy+RpOCCGEEG/2Ic9OytN5YgCKFStGsWLF8iOLEEIIIXIpr11CWlzD5KyIWbhwYY7v8Msvv3znMEIIIYTIHbnswFvMmzcvR3emUCikiBFCCCFEgchREfNiNpLQLBNDPUwN89wDKN7CUF9X0xFybVO3SpqO8E6sqwzSdIRcu/z7bE1HeCce9qaajpBrGRkZmo6QazExBd+qocM7zNJ55XhtJd+IQgghhBb7kLuTtLkAE0IIIcQHTFpihBBCCC2mUICOzE4SQgghhLbRyWMRk5djNU2KGCGEEEKLyZiYXPrzzz/57LPP8Pf359GjRwBs2LCBY8eO5Ws4IYQQQojXyXUR88svvxAQEICxsTEXLlwgOTkZgOjoaKZPn57vAYUQQgjxei+6k/KyaKtcFzFTp05l+fLlrFq1Cn19fdX6mjVr8tdff+VrOCGEEEK8mVzFOheCg4OpU6dOlvWWlpZERUXlRyYhhBBCiLfKdRHj6OjI7du3s6w/duwYnp6e+RJKCCGEEDnz4irWeVm0Va6LmD59+vDVV19x+vRpFAoFjx8/5ocffmDEiBH079//fWQUQgghxGvo5MOSG+np6YwbNw4PDw+MjY0pXrw4U6ZMUbtMREZGBuPHj8fJyQljY2MaNWrErVu38vZEs5HrKdZff/01SqWShg0bkpCQQJ06dTA0NGTEiBEMHjw43wMKIYQQovCYOXMmy5YtY926dZQuXZpz587Ro0cPLC0tVReBnjVrFgsXLmTdunV4eHgwbtw4AgICuHbtGkZGRvmWJddFjEKh4JtvvmHkyJHcvn2buLg4SpUqhZmZWb6FEkIIIUTO5HVwbm6PPXHiBB999BEtWrQAwN3dnc2bN3PmzBkgsxVm/vz5jB07lo8++giA9evX4+DgwPbt2+ncufO7h33FO187ycDAgFKlSlG1alUpYIQQQggN0SGPY2LIrGJiYmLUlhenUHlVjRo1OHDgADdv3gTg0qVLHDt2jGbNmgFw9+5dwsLCaNSokeoYS0tLqlWrxsmTJ/P1uee6JaZ+/fpvPLvfwYMH8xRICCGEEDmXXy0xrq6uausnTJjAxIkTs+z/9ddfExMTg4+PD7q6uqSnpzNt2jS6dOkCQFhYGAAODg5qxzk4OKi25ZdcFzHly5dXu52amsrFixe5cuUK3bp1y69cQgghhChAISEhWFhYqG4bGhpmu9+WLVv44Ycf2LRpE6VLl+bixYsMGTIEZ2fnAq8Dcl3EzJs3L9v1EydOJC4uLs+B8ku9evUoX7488+fP13SUQmvVliMs2niA8IgYyni5MHNkByqVdtd0rLfSxtzamBkKV+4aFYoz+PNGlPNxw8nOki4jVvLbkb9V25+fXZztceMXBLFo4wEALv06CTdnW7Xtkxb/yvx1+95f8H/57seD7D9+mbshTzEy0KNcKXeG9mqOh6t9ln0zMjLoP3Y1x88FM39CNxrWKFMgGXOjML0/cuLEX7dZtPEAl248IOxZDBtm9aZFvXKajpVn+XUBSAsLC7Ui5nVGjhzJ119/rRrb4ufnx/379wkMDKRbt244OjoC8OTJE5ycnFTHPXnyJEtDSF6985iYV3322Wd8//33+XV34j3b9sd5xs4PYnTvZhzeMJoyXi60H7yEp5Gxmo72RtqYWxszQ+HLbWJsyJWbjxg566dst3s3HaO2DJy8EaVSyY5DF9X2m7Z8l9p+K386UgDpM537+w6dW9Xgh/mDWBnYl7T0dPr93yoSklKy7Lsh6M9CfWG+wvb+yIn4pGTKeLkwa2RHTUfJVwpF3s4Vk9u3WUJCAjo66uWDrq4uSqUSAA8PDxwdHTlw4IBqe0xMDKdPn8bf3z/Pz/ff8q2IOXnyZL5OmxLv19JNB+napgZdWvvj4+nE3DGdMTEyYOOO/B10ld+0Mbc2ZobCl3v/iWtMW76L3Yf/znZ7eESs2tK8jh9/nr/F/UcRavvFJSSp7ZddAfG+LJ/ehzZNqlDC3RHv4s5MHd6J0PAort16qLbfjTuPWPfLUaYM61Bg2XKrsL0/cqJxjdJ8078lLetrf+uLJrVq1Ypp06axe/du7t27R1BQEHPnzqVt27ZA5izmIUOGMHXqVHbs2MHly5fp2rUrzs7OtGnTJl+z5LqIadeundrStm1bqlevTo8ePejXr1++hssrpVLJqFGjsLGxwdHRUW2A0ty5c/Hz88PU1BRXV1cGDBig1h22du1arKys2L59O15eXhgZGREQEEBISIhqn4kTJ1K+fHlWrFiBq6srJiYmdOzYkejoaACOHj2Kvr5+loFMQ4YMoXbt2u/3yb9BSmoaF2+EUK+qt2qdjo4Odat6c/byXY3lehttzK2NmUF7c79gZ2NOk1pl2Phr1i/UId2acGffTI5sHM3gzxqiq5tvv+VyLS4+CQBLcxPVusSkFEbP2MQ3A9tQxObtTfuaoO3vj/+agr520qJFi/j4448ZMGAAvr6+jBgxgn79+jFlyhTVPqNGjWLw4MH07duXKlWqEBcXx969e/O9sSPXf72WlpZqi42NDfXq1eO3335jwoQJ+Rour9atW4epqSmnT59m1qxZTJ48mX37Mvu+dXR0WLhwIVevXmXdunUcPHiQUaNGqR2fkJDAtGnTWL9+PcePHycqKirL/Pbbt2+zZcsWdu7cyd69e7lw4QIDBgwAoE6dOnh6erJhwwbV/qmpqfzwww/07NnzPT/714uIiiM9XYmdjbnaejsbC8IjYjSU6u20Mbc2Zgbtzf3CJy2qERefxM5XupJW/HSEXv+3htb9F7B223GG9Qhg0uA2GsmoVCqZuXwHFUq74+XuqFo/a8UOypdyp0EhHAPzgra/P/5rCvoq1ubm5syfP5/79++TmJjInTt3mDp1KgYGBqp9FAoFkydPJiwsjKSkJPbv30/JkiXz+ZnncmBveno6PXr0wM/PD2tr63wPk9/Kli2rKqy8vLxYvHgxBw4coHHjxgwZMkS1n7u7O1OnTuWLL75g6dKlqvWpqaksXryYatWqAZlFka+vL2fOnKFq1aoAJCUlsX79elxcXIDMCrVFixZ8++23ODo60qtXL9asWcPIkSMB2LlzJ0lJSXTs+Po+2eTkZLX5+TEx8qEgRG50aV2dn/eeIzklTW390k0vTwFx9fZjUlLTmPd/nzB5yQ5SUtNevZv3atriIG7fD2PdtwNU6w6dvMqZi3f4eemQAs0ihLbKVUuMrq4uTZo00ZqrVZctW1bttpOTE+Hh4QDs37+fhg0b4uLigrm5OZ9//jkREREkJCSo9tfT06NKlSqq2z4+PlhZWXH9+nXVOjc3N1UBA+Dv749SqSQ4OBiA7t27c/v2bU6dOgVkdlN17NgRU1PT1+YODAxUa+16de5+XtlamaGrq5NlAN7TyBjsbQtn8zVoZ25tzAzamxvAv3xxSro7suHXE2/d9/zVe+jr6eLmbFMAyV6atjiII6evs3rWFzjaWanWn7l4m5DQCGq0G0/5ZqMp32w0AMOmrKfHyGUFmvFNtPn98V+kyIf/tFWuu5PKlCnDP//88z6y5Dt9fX212wqFAqVSyb1792jZsiVly5bll19+4fz58yxZsgSAlJT8HeRnb29Pq1atWLNmDU+ePGHPnj1v7UoaM2YM0dHRquXf43Dyg4G+HuV9XDlyNli1TqlUcvTsTar4eeTrY+UnbcytjZlBe3MDfPaRPxeuPeDKrUdv3devZFHS05UFNqMmIyODaYuDOHjiCqtn9aOoo3rx1KtTfX5ZPoyflw1VLQCj+rVmyvBOBZIxJ7T5/fFfVNDdSYVJrs8TM3XqVEaMGMGUKVOoVKlSlhaFnMwx17Tz58+jVCr59ttvVdPEtmzZkmW/tLQ0zp07p+o6Cg4OJioqCl9fX9U+Dx484PHjxzg7OwNw6tQpdHR08PZ+OeCtd+/efPLJJxQtWpTixYtTs2bNN+YzNDR87UmG8suATxswYNIGKvi6UbG0O8s2HyI+MZkuraq/18fNK23MrY2ZofDlNjU2wMPVTnW7mLMtZUq6EBWdwMMnzwEwNzXio4YVGDc/KMvxVfw8qFSmGMfO3SI2IYmqfh5MG9qeLXvOEh2bWCDPYdriIH47dIEFE7tjamzIs8jMrmIzU2OMDPUpYmOR7WBeR3urLAWPphW290dOxCUkc/fhU9Xt+48juHzzIdYWJoXu9c2N/DpPjDbKcREzefJkhg8fTvPmzQFo3bq12jkMMjIyUCgUpKen53/KfFaiRAlSU1NZtGgRrVq14vjx4yxfvjzLfvr6+gwePJiFCxeip6fHoEGDqF69uqqoATAyMqJbt27MmTOHmJgYvvzySzp27Kg62Q9AQEAAFhYWTJ06lcmTJxfIc3ybdk0q8SwqjukrdhMeEYtfSRe2LhxY6JuCtTG3NmaGwpe7vG8xdq34SnV7+rD2AGzadYqBkzaqMisUCn75/VyW45NTUmnXuBJf92mOgb4e9x9HsGzzIZb8UHCXSvlpV+ZsqZ4j1T9vpgzvSJsmVbI7pNAqbO+PnLh4/QGt+y9U3R77v2L3kxZVWTLhc03FEnmgyMjIyMjJjrq6uoSGhqqNB8lO3bp18yVYXmV3xt42bdpgZWXF2rVrmTdvHrNnzyYqKoo6derQpUsXunbtyvPnz1X7DBkyhO+//56RI0fy6NEjateuzerVq3FzcwMyp1hv376dfv36MXXqVCIjI2nZsiUrV67MMvB5/PjxTJ8+nZCQELUzGOZETEwMlpaWPImI1oqWLiFyyrrKIE1HyLXLv8/WdIR3UtTGWNMRci2HX0+FSkxMDI5FrIiOfv+f1y++GybvuoiRqfnbD3iNpPhYxrcsXyCZ81uOW2JevJkKS5HyNocPH86ybvv27ar/Hzp0KEOHDlXb/vnnWSvxF+fDeZP+/fvTv3//N+7z6NEjmjdvnusCRgghhHgT6U7KocJ8CuzCKjo6msuXL7Np0yZ27Nih6ThCCCHEf0auipiSJUu+tZCJjIzMU6D/mo8++ogzZ87wxRdf0LhxY03HEUII8R/zLmfdffV4bZWrImbSpElYWlq+ryyFSvfu3enevfsb95k4caLapQyyk123lhBCCJFfXlzIMS/Ha6tcFTGdO3fG3j7rJeOFEEIIoRkf8piYHJ/sTsbDCCGEEKIwyfXsJCGEEEIUInkcE6PFVx3IeRGjVCrfZw4hhBBCvAMdFOjkoRLJy7GalutrJwkhhBBCFAa5vnaSEEIIIQoPmWIthBBCCK0ks5OEEEIIIbSMtMQIIYQQWkxOdieEEEIIrSRjYoQQQgihlXTIY0uMTLEWQgghhChY0hIjhBBCaDHpThJaISMjQy7/UAC08Tph2vq+uLRnlqYj5JrfxzM0HeGdROyfqOkIuabUwvd1urLgM+uQt24Vbe6S0ebsQgghhPiASUuMEEIIocUUCkWeWpC1sfX5BSlihBBCCC2mIG8XotbeEka6k4QQQgihpaQlRgghhNBicsZeIYQQQmgt7S1D8ka6k4QQQggt9uI8MXlZcuvRo0d89tln2NraYmxsjJ+fH+fOnVNtz8jIYPz48Tg5OWFsbEyjRo24detWPj7rTFLECCGEECLHnj9/Ts2aNdHX12fPnj1cu3aNb7/9Fmtra9U+s2bNYuHChSxfvpzTp09jampKQEAASUlJ+ZpFupOEEEIILVbQU6xnzpyJq6sra9asUa3z8PBQ/X9GRgbz589n7NixfPTRRwCsX78eBwcHtm/fTufOnd8566ukJUYIIYTQYjr5sOTGjh07qFy5Mh06dMDe3p4KFSqwatUq1fa7d+8SFhZGo0aNVOssLS2pVq0aJ0+efMdnmT0pYoQQQghBTEyM2pKcnJztfv/88w/Lli3Dy8uL33//nf79+/Pll1+ybt06AMLCwgBwcHBQO87BwUG1Lb9IESOEEEJosRfdSXlZAFxdXbG0tFQtgYGB2T6eUqmkYsWKTJ8+nQoVKtC3b1/69OnD8uXLC/JpAzImRgghhNBq+XXG3pCQECwsLFTrDQ0Ns93fycmJUqVKqa3z9fXll19+AcDR0RGAJ0+e4OTkpNrnyZMnlC9fPg9Js5KWGCGEEEKL5VdLjIWFhdryuiKmZs2aBAcHq627efMmxYoVAzIH+To6OnLgwAHV9piYGE6fPo2/v3++PndpiRFCCCFEjg0dOpQaNWowffp0OnbsyJkzZ1i5ciUrV64EMouqIUOGMHXqVLy8vPDw8GDcuHE4OzvTpk2bfM0iRYwQQgihxd5lhtGrx+dGlSpVCAoKYsyYMUyePBkPDw/mz59Ply5dVPuMGjWK+Ph4+vbtS1RUFLVq1WLv3r0YGRnlIWlWUsRogLu7O0OGDGHIkCEay3Dir9ss2niASzceEPYshg2zetOiXjmN5ckJbcz8wqotR1i08QDhETGU8XJh5sgOVCrtrulYr6UNr/Xqnw5y4PgV7j4Mx9BAn/Kl3BnSsxnuRe3V9rt0/T6L1u3l8o0H6Oro4F3cmWVTe2NkqP/eM9YoW4zBnWpSzssJpyIWdBm3md+O31Dbp6RbESb2bUzNsu7o6uoQfP8p3Sb+xMPwaNU+VUoVZWyvhlTyKUq6UsmVO2G0H7WBpJS09/4csjNv7R/sOnyJW/efYGyoTxU/DyYM+givYg5vP1iD1vzyJ2u3HedBaAQAPp5ODO/ZlEY1Sr3lyMKtoM8TA9CyZUtatmz5xvucPHkykydPfudcOSFjYnKgXr16Gi043of4pGTKeLkwa2RHTUfJMW3MDLDtj/OMnR/E6N7NOLxhNGW8XGg/eAlPI2M1He21tOG1Pnf5Hzq1qsGGeYNYMb0PaWnpfPHNdyQkpaj2uXT9PgPGrsa/Ykl+WDCYTQsH07lVjQK74J2JkT5X7oQxcuHubLe7O1uzZ0Evbj14Rstha6jVZylzNh5RK06qlCrK1hmfc+jcHRoNXEnDAStZFXQGZUZGgTyH7Jy4cJteH9fmj9XD+WXhQNLS0vn4yyXEJ2Y/JbewcLa3YuzAVuxfO5L9a0dSq1JJuo5axY1/QjUdTbwjaYnJJxkZGaSnp6Onpx0vaeMapWlco7SmY+SKNmYGWLrpIF3b1KBL68wBbXPHdOaP41fZuOMkQ7s30XC67GnDa71sam+125OHdaT+J5O5fushlfw8AZi9YieffFSTXh3rq/Z7taXmfdp/5jb7z9x+7fZxPRuy78wtJqzcp1p37/FztX2mDWjKiqDTzN98TLXudkhE/ofNhZ8XDFC7vXj8Z3g3/T8u3QihRoUSGkr1dgG1/dRuf9O/JWuDjnHuyj18PJ1ec1Thl1+zk7SR1rfE1KtXjy+//JJRo0ZhY2ODo6MjEydOVG2Pioqid+/e2NnZYWFhQYMGDbh06ZJqe/fu3bMMNBoyZAj16tVTbT9y5AgLFixQNdndu3ePw4cPo1Ao2LNnD5UqVcLQ0JBjx45x584dPvroIxwcHDAzM6NKlSrs37+/AF4JURilpKZx8UYI9ap6q9bp6OhQt6o3Zy/f1WCy/564hMxrsliYmwAQERXH5eAH2Fia0XXYEup/MpmeI5fx15XC8borFAoaVy/J7ZAIts78nJu/jGTfkj40r+mj2qeIlSlVSrnyNCqe3xf1InjrSHbN60H1Mm4aTJ5VTFzma29tYaLhJDmXnq4kaN95EhKTqeLnruk4eaKJC0AWFlpfxACsW7cOU1NTTp8+zaxZs5g8eTL79mX+sunQoQPh4eHs2bOH8+fPU7FiRRo2bEhkZGSO7nvBggX4+/vTp08fQkNDCQ0NxdXVVbX966+/ZsaMGVy/fp2yZcsSFxdH8+bNOXDgABcuXKBp06a0atWKBw8evJfnLgq3iKg40tOV2NmYq623s7EgPCJGQ6n+e5RKJbNW7KB8KXe83DPPUfHof+Melv+wj3ZNq7J0Si98S7jQd8xK7j96qsm4ANhZmWJuYsiQT2px4Oxt2o3awO5j19kwqRM1ymZOVXV3yryg3tdd67Fu93k+/noDl26Fsn1ONzxdbDQZX0WpVPLNvF+oVtYT3+LOmo7zVtduP6ZY/RG41BnGiJlbWDuzN94e2tsK86HTjr6PtyhbtiwTJkwAwMvLi8WLF3PgwAGMjY05c+YM4eHhqvnuc+bMYfv27WzdupW+ffu+9b4tLS0xMDDAxMREdQKff5s8eTKNGzdW3baxsaFcuZcDIKdMmUJQUBA7duxg0KBBOXo+ycnJaqd7jomRLzsh3mT6ku3cufeEtXP6q9a9GDPycfNqtGlSBQDfEi6cvnib7X+c46sezTSS9QUdncyfv3tO3GDZ1szryVy5E0bV0q70bF2FE3/fV+2zdtc5Nu29CMDl23upW8GDz5pVZPJ3mm/lHTn7Z67/E8ruFUM0HSVHShSz59D60cTGJ7Lj4EUGT97Ir8u+1OpCRgcFOnnoFMrLsZr2n2iJKVu2rNptJycnwsPDuXTpEnFxcdja2mJmZqZa7t69y507d/LlsStXrqx2Oy4ujhEjRuDr64uVlRVmZmZcv349Vy0xgYGBaqd+/nfLj9AutlZm6OrqZBnE+zQyBntbi9ccJXJj+tLtHD1znVUz++FgZ6VaX8Qm8/X1dFOfMePhZk9YuPq4E02IiE4gNS2dG/fVW4Vu3n9GUXtLAMIiMt83wa/sE/zg5T6aNGr2Fv44doVflw7GxcFa03FyxEBfD09XO8r5uDFuQGtKl3Bh5U9HNB0rTz7k7qT/REuMvr76VEmFQoFSqSQuLg4nJycOHz6c5RgrKysgc3xCxiuj/FNTU3P82Kampmq3R4wYwb59+5gzZw4lSpTA2NiYjz/+mJSUlNfcQ1Zjxoxh2LBhqtsxMTFSyGgpA309yvu4cuRssGqKslKp5OjZm/TuUEfD6bRbRkYGgct+5eCJK6ye2Y+ijurdKy4O1tjZWnDvoXoBcP/hM2pV8UbTUtPSuRD8CC/XImrri7vaEvIkCoAHYVE8fhZDiVf2KVHUlv1nbhVU1CwyMjIYPedndh/5mx1Lv6SYc5G3H1RIKTMySNbQVPX8ovjff3k5Xlv9J4qY16lYsSJhYWHo6enh7u6e7T52dnZcuXJFbd3FixfVCiMDAwPS09Nz9JjHjx+ne/futG3bFshsmbl3716uchsaGr72dM/5JS4hmbv/+nC//ziCyzcfYm1hkuXLoLDQxswAAz5twIBJG6jg60bF0u4s23yI+MRkurSqrulor6UNr/X0JdvZc/gC88d3w9TYiGf/a+0yMzXCyFAfhUJB9/Z1WbZxH94eTngXd2bH/vPcexjOt998XiAZTY0M8PjX2JViTtaUKe5IVGwiD8OjWfjTcb4f14ETf9/nzwt3aVS1BE39S9Jq6FrVMYt+Os6YbvW5cieMy7fD+CSgPF5uReg26acCeQ7ZGTl7C7/8fp6Ns/tgZmrEk/+N77IwNcLYyEBjud5mytIdNPQvRVEHa+ISkvnlj3Mc/+s2W+b3f/vBolD6TxcxjRo1wt/fnzZt2jBr1ixKlizJ48eP2b17N23btqVy5co0aNCA2bNns379evz9/dm4cSNXrlyhQoUKqvtxd3fn9OnT3Lt3DzMzM2xsXv8h7uXlxbZt22jVqhUKhYJx48ahVCoL4unmysXrD2jdf6Hq9tj5QQB80qIqSyYUzAd8bmljZoB2TSrxLCqO6St2Ex4Ri19JF7YuHFiou5O04bXesjtzHEmv0SvU1k8e1pGPGmd2837WtjbJqWnMXrmT6NgEvD2dWT6tD67OtgWSsby3M7vm9VDdnj6gKQCb9l5g4Kzt7D52g2HzdjH009rMGNSM2yHP6DrhJ05dedn9vPyXUxgZ6DF9QFOszI25+k8Y7UauzzIVuyCt+SVzuve/3yMAi8Z14dOWhbc4f/Y8jkGTNvIkIhoLM2NKFXdmy/z+1Kvm8/aDC7G8dglpc3eSIuPVvhQtU69ePcqXL8/8+fNV69q0aYOVlRVr164lNjaWb775hl9++YWnT5/i6OhInTp1CAwMVHXRTJgwgRUrVpCUlETPnj1JTU3l8uXLqm6omzdv0q1bNy5dukRiYiJ3797l3r171K9fn+fPn6u6pgDu3btHz549OXXqFEWKFGH06NH8/PPPahlze8bemJgYLC0tCXsWpXaFUfF+5OXMl5qirX/GIRGJmo6Qa+U6ztR0hHcSsX+ipiPkmiZP6PeuYmJicLG3Jjo6+r1/Xr/4bth66g6mZuZvP+A14uNi+bh68QLJnN+0voj5EEgRU7CkiCk4UsQUHCliCoYUMQXrP92dJIQQQvzXfcjdSVLECCGEEFrsQy5i/hPniRFCCCHEh0daYoQQQggtJueJEUIIIYRW0lFkLnk5XltJESOEEEJosQ+5JUbGxAghhBBCK0lLjBBCCKHFPuTZSVLECCGEEFpMQd66hLS4hpHuJCGEEEJoJ2mJEUIIIbSYzE4SQgghhFb6kGcnSREjhBBCaLEPeWCvjIkRQgghhFaSlhghhBBCiynI2wwjLW6IkSJGCCGE0GY6KNDJQ5+QjhaXMdKdJIQQQgitJC0xWiT4cSxmsdpTMTtZG2k6wjtJSVNqOkKuGehp5+8RtyImmo6Qa88PTtJ0hHfy6brzmo6Qa8s7ltV0hFxLTEkv8MeU7iQhhBBCaKcPuIrRzp9vQgghhPjgSREjhBBCaDFFPvz3rmbMmIFCoWDIkCGqdUlJSQwcOBBbW1vMzMxo3749T548yYdnmpUUMUIIIYQ2U7w84d27LO9aw5w9e5YVK1ZQtqz62KWhQ4eyc+dOfv75Z44cOcLjx49p165d3p9nNqSIEUIIIbSYIh+W3IqLi6NLly6sWrUKa2tr1fro6GhWr17N3LlzadCgAZUqVWLNmjWcOHGCU6dOvfuTfA0pYoQQQgiRKwMHDqRFixY0atRIbf358+dJTU1VW+/j44ObmxsnT57M9xwyO0kIIYTQZvk0OykmJkZttaGhIYaGhll2//HHH/nrr784e/Zslm1hYWEYGBhgZWWltt7BwYGwsLA8hMyetMQIIYQQWiy/Bva6urpiaWmpWgIDA7M8VkhICF999RU//PADRkaaPxeYtMQIIYQQgpCQECwsLFS3s2uFOX/+POHh4VSsWFG1Lj09naNHj7J48WJ+//13UlJSiIqKUmuNefLkCY6OjvmeWYoYIYQQQoupZhnl4XgACwsLtSImOw0bNuTy5ctq63r06IGPjw+jR4/G1dUVfX19Dhw4QPv27QEIDg7mwYMH+Pv7v3vI15AiRgghhNBiBXnCXnNzc8qUKaO2ztTUFFtbW9X6Xr16MWzYMGxsbLCwsGDw4MH4+/tTvXr1PKTMnhQxQgghhMg38+bNQ0dHh/bt25OcnExAQABLly59L48lRYwQQgihzTR87aTDhw+r3TYyMmLJkiUsWbIkb3ecA1LECCGEEFosr5cOyMuxmiZFjBBCCKHF8mtgrzaSIuYDELT3FEF7zxAa/hwAD1d7enRsgH8lb0LDn/Nxv9nZHjdlxCc0qOlXkFHVnLl0h5U/HuLKzYeER8SwfEoPmtR+mWdk4GZ++V39ZEt1qnizdna/go6qsmLTAfYdu8w/IU8xMtSjQil3hvdpgaervWqf5JRUZi7fye5DF0lNTaNmZW8mfNWOItbmGsutja/166zacoRFGw8QHhFDGS8XZo7sQKXS7pqO9VaFKbePgxktSzvgYWuCtYkBcw/e5lxItNo+H5d3or6XHaYGutwMj+P7Uw8Ii01Wbf/Iz5EKRS0pZmNCmlJJn82XCvppcPriHVb8eJDLwZnv65XTehLwv/d1alo6c1b9xqFT13kQGoG5qRG1Kpfk634tcShiWeBZxbuRIkYDFAoFQUFBtGnTpkAez87Wki8+D8DVyZaMDNhz6C++nrGRNd8OopiLHTu+H6O2/69/nGHT9j+pXrFkgeR7nYSkFHyLO9OheVX6j1ub7T51q/owa3Rn1W0DA82+pc/+/Q+fflQTP29X0tOVzFv9G71Hr2TX6pGYGGeecyFw6Q6OnL7OgvGfY2ZqzJRFQQyeuI7NCwZpLLc2vtbZ2fbHecbOD2Lu152oVMad5ZsP0X7wEs5uHY+djeaKxLcpbLkN9XS4/zyRw7cjGFa/eJbtrco4EOBrz/Jj9wiPS6FDeWe+buzFyO1XSVVmAKCno+D0/efcehpPPS/bgn4KwIv3tQsdm1ej39g1atsSk1K4cushX3ZrjG8JF6JjE5i0MIheY75j16rhGsn7rjQ8JEajCt+nkMh3tar4qt3u91kTgn4/zdWbIXi6OWD7SgvA0dPXaFjTT/Wlqyn1qvlSr5rvG/cx0NfDzvbN5zUoSN/N6KN2O3BUZ2p8PJGrtx5SpWxxYuMS+WXvGWb/36dUr+CVuc/ITjTvOYuL1+5TvlQxTcTWytc6O0s3HaRrmxp0aZ15Poq5Yzrzx/GrbNxxkqHdm2g43esVttyXHsVw6VHMa7c39XVg+99hnP9f68yyY3dZ1qkcld2sOHkvs8X3l0uhANQprpkCBqB+dV/qV8/+fW1hZswPc/urrZs8pD2t+83j0ZPnuDhYZ3tcofQBVzFy2YEPTHq6kv1/XiIpKYUy3q5Ztt+484hbd0Np2aiyBtLl3qmLt6nSZjwNPw9k7NytPI+O13QkNbHxSQBYmpsAcPXWQ1LT0qnxr1YuTzd7nO2tuHjtvkYy5lRhf61TUtO4eCOEelW9Vet0dHSoW9Wbs5fvajDZm2lbbnszA6xN9Lny+GWRk5iq5M7TeLzsTDWYLO9i4xNRKBRYmBlrOorIISlicmDr1q34+flhbGyMra0tjRo1Ij4+nrNnz9K4cWOKFCmCpaUldevW5a+//lI79tatW9SpUwcjIyNKlSrFvn37NPIc7twPo9EnE6nfcTyzl//K9K8/w8PVIct+u/afw72oHX4+mmkRyI06VX349v8+ZcPcLxjdtyVnLt2hx+iVpKcrNR0NAKVSyfSlv1KxtDslPZwAeBoZi76+bpYPSVtrc549f/0vX00r7K81QERUHOnpyizdL3Y2FoRHFN7XVttyWxrrAxCdlKq2PjopVbVNGyUlpxK4fBetG1bA3FTz1wTKjfy6dpI2ku6ktwgNDeWTTz5h1qxZtG3bltjYWP78808yMjKIjY2lW7duLFq0iIyMDL799luaN2/OrVu3MDc3R6lU0q5dOxwcHDh9+jTR0dEMGTLkrY+ZnJxMcvLLAXKvXln0Xbg5F2Ht3MHEJSRx6MQVpi38mcVT+6gVMsnJqew7eonuHevn+fEKQquGFVT/7+PpjE9xZ+p9Oo1TF29Ts5Jmx/MATF4YxK17YWyaP1DTUfKssL/WQuRFalo6AyesIyMjg2nDO2g6Tq7J7CTxWqGhoaSlpdGuXTuKFctsnfDzyxzd3qBBA7V9V65ciZWVFUeOHKFly5bs37+fGzdu8Pvvv+Ps7AzA9OnTadas2RsfMzAwkEmTJuXr89DX16OoU2bftE9xF27cfsjPu04wqn9b1T6HTl4hKSWVpvUqvO5uCjU3Z1tsLE25/+iZxr9YJy/axuHT19g4dwCOdlaq9XY25qSmphMTl6jWGhPxPJYi1oV7vMm/FabX+gVbKzN0dXV4Ghmrtv5pZAz2hXgsj7bljk7MbIGxNNInKjFNtd7SSJ/7kQmaivXOXhQwj548Z/P8AVrXCgMf9JAY6U56m3LlytGwYUP8/Pzo0KEDq1at4vnzzIFrT548oU+fPnh5eWFpaYmFhQVxcXE8ePAAgOvXr+Pq6qoqYIAcXQBrzJgxREdHq5aQkJB8f15KZQYpqelq63btP0etKj5YW5rl++MVhNDwKJ7HJGj0gz8jI4PJi7ax/9gV1s7+QlU4vlDaqyj6erqc/OuWat0/IeE8Do/S2KDed1EYXutXGejrUd7HlSNng1XrlEolR8/epIqfhwaTvZm25Q6PS+F5QiqlnV52fxnr61DczpRbTwvXOKm3eVHA3H34lB/m9cfaUrvH9HyIpCXmLXR1ddm3bx8nTpzgjz/+YNGiRXzzzTecPn2a/v37ExERwYIFCyhWrBiGhob4+/uTkpKSp8c0NDTM9hLo72rZht/xr1gSBzsrEhKT+ePoJS5cvcvc8d1V+zwMjeDitXvMGdst3x43r+ITkrn/6JnqdkhYJNduPcLSwgQrcxMWrvudpnXKYmdjwf3Hz5i5YhfFXIpQu4qPxjJPXriNXQcvsGRyD0xNDHkamdkVaG5qjJGhPuZmxrRvWpWZy3dgaWGCmYkRUxcHUb5UMY0WMdr4WmdnwKcNGDBpAxV83ahY2p1lmw8Rn5hMl1b5f+G5/FTYchvq6eBo/vIzyM7ckGLWxsSlpBERn8re609oW9aJsNhknsYm06GCC1EJqZx7EKU6xtZUHzMDPWxNDdBRKChmndnyGBabTHJawYylik9I5t6/39ehEVy99QgrCxPsbS3oP24tV24+5PuZvUlPV6rGIFlZmGCgr0Vfjx9wU4wW/StpjkKhoGbNmtSsWZPx48dTrFgxgoKCOH78OEuXLqV58+YAhISE8OzZyz8YX19fQkJCCA0Nxckpc2DnqVOnCjx/VHQcUxb8TMTzWExNjCjh7sjc8d2pWt5Ltc+uA+ewt7WgavkSBZ7vdS4Hh/Dp0JcXDZu25FcA2gdUYcqw9tz4J5Rtv58jJi4Re1sLalfxZmjPZhhq8Pwlm3eeBKDr8GVq66eP7ES7gCoAjBnQGh0dBV9NWkdKahq1Knsz/st2BZ7137Txtc5OuyaVeBYVx/QVuwmPiMWvpAtbFw4sVC1G2SlsuT1tTRjX9OVsqc+rZM5kPHL7GSuO32fnlScY6unQ278YJga63HwSx4z9t1TniAH4uLwzdUsUUd0ObF0KgCl7g7n+JK5AnsffwSF0/url9XumLM58X3/ctApDejRl3/ErADTrOUftuB8XDMS/QuH5LHybD/myA4qMjIyMt+/24Tp9+jQHDhygSZMm2Nvbc/r0aT777DO2b9/ON998Q5EiRViwYAExMTGMHDmSc+fOMX36dIYMGYJSqcTPzw8XFxdmz55NTEwMQ4cO5fz587k62V1MTAyWlpYc+TsEM/PC/WH8b07W2te3DJBSQL8S85OBnnb2DFubGmg6wgfj03XnNR0h15Z3LKvpCLkWGxNDiaJFiI6OxsLi/X5ev/huOHX9cZ6+G+JiY6ju61wgmfObdn7yFSALCwuOHj1K8+bNKVmyJGPHjuXbb7+lWbNmrF69mufPn1OxYkU+//xzvvzyS+ztX55eXkdHh6CgIBITE6latSq9e/dm2rRpGnw2Qggh/mtezE7Ky6KtCldbcCHk6+vL3r17s91WoUIFzp5Vv57Mxx9/rHa7ZMmS/Pnnn2rrpPFLCCFEfvmAh8RIS4wQQgghtJO0xAghhBDa7ANuipEiRgghhNBiH/LsJClihBBCCC32IV92QMbECCGEEEIrSUuMEEIIocU+4CExUsQIIYQQWu0DrmKkO0kIIYQQWklaYoQQQggtJrOThBBCCKGd8nrpAO2tYaSIEUIIIbTZBzwkRsbECCGEEEI7SUuMEEIIoc0+4KYYKWKEEEIILfYhD+yV7iQhhBBCaCVpiRFCCCG02Id87SQpYrSIrbkh5haGmo6RY8oMTSd4NyYGupqOkGuPnidpOsI7sTY10HSED8asVqU0HSHXRuy4pukIuZaSEFfgj1nQQ2ICAwPZtm0bN27cwNjYmBo1ajBz5ky8vb1V+yQlJTF8+HB+/PFHkpOTCQgIYOnSpTg4OOQhaVbSnSSEEEKIHDty5AgDBw7k1KlT7Nu3j9TUVJo0aUJ8fLxqn6FDh7Jz505+/vlnjhw5wuPHj2nXrl2+Z5GWGCGEEEKbFXBTzN69e9Vur127Fnt7e86fP0+dOnWIjo5m9erVbNq0iQYNGgCwZs0afH19OXXqFNWrV89DWHXSEiOEEEJoMUU+/AcQExOjtiQnJ+fo8aOjowGwsbEB4Pz586SmptKoUSPVPj4+Pri5uXHy5Ml8fe5SxAghhBBaTMHLwb3vtPzvflxdXbG0tFQtgYGBb31spVLJkCFDqFmzJmXKlAEgLCwMAwMDrKys1PZ1cHAgLCwsX5+7dCcJIYQQgpCQECwsLFS3DQ3fPpFk4MCBXLlyhWPHjr3PaK8lRYwQQgihxfJrSIyFhYVaEfM2gwYNYteuXRw9epSiRYuq1js6OpKSkkJUVJRaa8yTJ09wdHTMQ9KspDtJCCGE0GJ56kp6h3PMZGRkMGjQIIKCgjh48CAeHh5q2ytVqoS+vj4HDhxQrQsODubBgwf4+/vnx1NWkZYYIYQQQuTYwIED2bRpE7/++ivm5uaqcS6WlpYYGxtjaWlJr169GDZsGDY2NlhYWDB48GD8/f3zdWYSSBEjhBBCaLmCnWO9bNkyAOrVq6e2fs2aNXTv3h2AefPmoaOjQ/v27dVOdpffpIgRQgghtFhBX3YgI+Ptp2M3MjJiyZIlLFmy5B1T5YyMiRFCCCGEVpKWGCGEEEKLFfS1kwoTKWKEEEIILSZXsRZCCCGEVvr3pQPe9XhtJWNihBBCCKGVpCVGCCGE0GYf8KAYKWLewcSJE9m+fTsXL17UdJQcOfv3HVb/dJgrtx7yNCKGJZO606iWn2r7s8hY5qzaxbHzN4mNS6RyWU/GDWqLe1E7DaaGM5fusOqnQ1y9+ZDwiBiWTelB43/lBrh9/wmzVu7izKU7pKcrKVHMgSWTuuPsYK2RzKcv3mH5jwe5HJyZedW0ngTUfpl5z5G/2fjrcS7ffEhUTAJ7Vo+gtJeLRrK+sG3PKbbtOU1o+HMAPN3s6dmpIf6VvAEY8M1KLly5q3ZMm4CqjB7QtsCz5sSqLUdYtPEA4RExlPFyYebIDlQq7a7pWG9VmHN/9+NB9h+/zN2QpxgZ6FGulDtDezXHw9U+y74ZGRn0H7ua4+eCmT+hGw1rlNFA4kyGejp85OdIBRdLzA31CIlK5McLj7gfmQhA96qu1PCwUTvmSmgMC4/eze7uCq0PuIaRIuZdjBgxgsGDB2s6Ro4lJKbgXdyZ9s2qMmjCWrVtGRkZDBy/Bj09XZZO7oGZqRFrfz5Cj5Er2P39SEyM334BsPclMSkF3+LOdGhWlQHj12bZfv/RMzp/uYgOzarxVfcAzEyMuHUvDEMDzb2tE5JSKFXchU7Nq9F37JpstidTpawnLRtUYPSsnzSQMCs7W0sGdA3A1bkIGRkZ/HbwL0ZN38C6eYPxdHMA4KMmVejzaWPVMUaG+pqK+0bb/jjP2PlBzP26E5XKuLN88yHaD17C2a3jsbMx13S81yrsuc/9fYfOrWpQpqQr6elKFqzdQ7//W8X2VSMxMTJQ23dD0J8oCslI0a5VXHGxNOL70w+ISkylejFrhtUtzoS9N4hKTAMyi5a1Z0JUx6Slv/0cKKLw+CCLmJSUFAwMDN6+4ysyMjJIT0/HzMwMMzOz95Ds/ahbzZe61Xyz3Xbv4TMuXr/PrtUj8XLPvDDXxCHtqdlhErsPXqBDi/w9RXRuvCk3wNzVv1G3mi+jv2ilWlfMpUhBRHut+tV9qV/99ZnbB1QBICQ0sqAivVXtqup5v/g8gG17T3Ml+IGqiDE01MfWWvNfpm+zdNNBurapQZfWmddnmTumM38cv8rGHScZ2r2JhtO9XmHPvXx6H7XbU4d3om6nSVy79ZDKfp6q9TfuPGLdL0f5adGX1P9kSkHHVKOvq6BiUUuWHrvLrafxAOy8+oSyzhbULV6EX69knio/LT2DmKQ0TUbNsw95dpLWDOzdunUrfn5+GBsbY2trS6NGjYiPj6devXoMGTJEbd82bdqoTn0M4O7uzpQpU+jatSsWFhb07duXe/fuoVAo+PHHH6lRowZGRkaUKVOGI0eOqI47fPgwCoWCPXv2UKlSJQwNDTl27BgTJ06kfPnyavtVrVoVU1NTrKysqFmzJvfv31dt//XXX6lYsSJGRkZ4enoyadIk0tIKxx9NSmpmjn+3Xujo6GCgr8v5K4W3SVWpVHL41HU8itrRfeQKqrYdT/v+89l37LKmo2m19HQl+45eIikpBT9vN9X6P45coulnU+gyeD5L1+8lKTlFgymzl5KaxsUbIdSr6q1ap6OjQ92q3py9XHjfy9qYOy4+CQBLcxPVusSkFEbP2MQ3A9tQxCbnV0J+X3QUCnR1FKS+0rKSmp5BCTtT1e2S9mbM+agUk5t582klF0wNdAs6ap4p8uE/baUVLTGhoaF88sknzJo1i7Zt2xIbG8uff/6Zo1MfvzBnzhzGjx/PhAkT1NaPHDmS+fPnU6pUKebOnUurVq24e/cutra2qn2+/vpr5syZg6enJ9bW1hw+fFi1LS0tjTZt2tCnTx82b95MSkoKZ86cUTWn/vnnn3Tt2pWFCxdSu3Zt7ty5Q9++fQGyZNEETzd7nO2t+fa735g89GOMjQxYu/UoYU+jeRoZo+l4rxURFUd8YjIrNh9kaM9mjOrXkqNnbjBg/Fo2zu1PtfIlNB1Rq9y+F0bf0ctISUnD2NiAGWM+w+N/rTBN6pTH0c6KIjYW3LkXypL1e3nw6Bkzxnym4dTqIqLiSE9XZul+sbOx4Na9JxpK9XballupVDJz+Q4qlHZXtd4CzFqxg/Kl3GmgwTEw/5acpuTOs3halHYgNCaJmOQ0qrpZ4WlrQnhcMgBXQ2O58DCaZ/Ep2JkZ0MbPiS/reDLjwC1y8fWieR/woBitKWLS0tJo164dxYoVA8DPz+8tR6lr0KABw4cPV92+d+8eAIMGDaJ9+/ZA5kWt9u7dy+rVqxk1apRq38mTJ9O4cWOyExMTQ3R0NC1btqR48eIA+Pq+bJ6fNGkSX3/9Nd26dQPA09OTKVOmMGrUqNcWMcnJySQnJ6s9xvuir6fLoknd+GbOFqq2GYeujg7+lbyoU9WnUP8RK5WZ4RrVKE3PDnUBKFXChb+u3mPzzpNSxORSMZcirJs/mPj4ZA6euMyUBVtZOq0PHm4OtAmoqtqvhLsjtjYWDB73HQ9DIyjqZPuGexX/RdMWB3H7fhjrvh2gWnfo5FXOXLzDz0uHaC5YNr4/9YBuVV2Z/VFp0pUZPHieyJkHURSzMQbgbEiUat9H0Uk8jEpiektfvO3MuBEep6HUIje0oogpV64cDRs2xM/Pj4CAAJo0acLHH3+MtXXOZ6BUrlw52/X+/v6q/9fT06Ny5cpcv349R8cC2NjY0L17dwICAmjcuDGNGjWiY8eOODk5AXDp0iWOHz/OtGnTVMekp6eTlJREQkICJiYmWe4zMDCQSZMm5fi55VWZkq78unI4sXGJpKalY2NlRoeBCyhTsmiBZcgta0tT9HR1KPGvX4IAJdzsOVdIm+ALM319PVydMscT+ZRw4fqth/y06wRfZzMDqXRJV4BCV8TYWpmhq6vD08hYtfVPI2Owt9V898braFPuaYuDOHL6Omu/HYCjnZVq/ZmLtwkJjaBGu/Fq+w+bsp6KZTxYM7t/ASfN9DQ+hTmH7mCgq4Oxvg7RSWn08S/Gs7jsu0OfxacQm5SGvbkBN8ILOGwefMANMdoxJkZXV5d9+/axZ88eSpUqxaJFi/D29ubu3bvo6Ohk6VZKTU3Nch+mpqZZ1uXU245ds2YNJ0+epEaNGvz000+ULFmSU6dOARAXF8ekSZO4ePGiarl8+TK3bt3CyMgo2/sbM2YM0dHRqiUkJCTb/fKbuZkxNlZm3Hv4lCs3Q2hYs3A0C2fHQF8PPx83/glR/6S5+/ApLhqaXv1fkpGRQWpq9uO2bt59DECRQjBr5t8M9PUo7+PKkbPBqnVKpZKjZ29Sxc9Dg8neTBtyZ2RkMG1xEAdPXGH1rH4UdVSfltyrU31+WT6Mn5cNVS0Ao/q1ZsrwTpqIrCYlXUl0Uhom+rqUdjTn4qPobPezMtbH1FCX6MTCMWYxp14M7M3Loq20oiUGQKFQULNmTWrWrMn48eMpVqwYQUFB2NnZERoaqtovPT2dK1euUL9+/Rzd76lTp6hTpw6QOb7l/PnzDBo0KNf5KlSoQIUKFRgzZgz+/v5s2rSJ6tWrU7FiRYKDgylRIufdG4aGhhga5t/U5vjEZB48eqa6/TAskuu3H2FpboKzgzV7jlzCxtIUZ3trgu+GMn3JdhrVLEOtyt5vuNf3Lz4xmfv/yh0SGsm124+w+l/uPp3q8dXkDVQp60n1CiU4euYGB09c44f5A95wr+85c0Iy99QyR3D11iOsLExwcbAmKiaeR0+iePIs80P0zoPMIszOxlxjv7qXrt+LfyVvHItYEZ+YzB9HL/LXlbvMn9iDh6ER/HH0IjUq+WBpbsLte6Es+H435Ut7UMLdSSN532TApw0YMGkDFXzdqFjanWWbDxGfmEyXVpqbZZcThT33tMVB/HboAgsmdsfU2JBn/xsvZ2ZqjJGhPkVsLLIdzOtob5Wl4ClIpRzNUQBhscnYmxnwcTlnwmKTOHE3EkM9HVqWduCvh9HEJKZiZ2ZI+3JOPI1L4WpY7FvvWxQOWlHEnD59mgMHDtCkSRPs7e05ffo0T58+xdfXF1NTU4YNG8bu3bspXrw4c+fOJSoqKsf3vWTJEry8vPD19WXevHk8f/6cnj175vj4u3fvsnLlSlq3bo2zszPBwcHcunWLrl27AjB+/HhatmyJm5sbH3/8MTo6Oly6dIkrV64wderU3L4U7+RKcAhdhy9T3Q5ctgOAtk0qM2P0JzyNiGHGsl+JeB6HnY0FHzWpxIDPsh8DVJAuB4fw2dClqtvTl/4KQLuAKsz6+hOa1C7L5KEfs3zTAaYsCsLz/9u787ia8v8P4O/THqUitIhQWkRU6hpLRJbM2JcxWUfZRyMSmRBGMfYtY8k2GYylmsZYvplBlgYRE7KFREMoKm231++PfvdMV8wwqnsv7+c85jFzzzn33ve9nfs57/NZLerQ6pARckM+q9qllDQa5LdGfDx3dWnM/bu1oqVBX9CRk8k0JfRHcf/EkG1ERPT1iK7k/2W3qg32/z3LzqW5y3fTk6cvSK+6DjVuYELL54wk1xbW9NfjLDqbdIt2/XyS8vOLqI6xAXVo7UAjB77dTUJV69vFmTKzcmjB97/QoycvqFkTc9qzcoLSNcu8Stnj3hV7moiIvgxYJ7d93pSB1LtLK0WE9FZ0NdWob3NTMtTVpLxCKSXez6aoyw9JCiI1gOoZ6FJrSyOqpqlOWfnFdCXjBUVfzqDiEiXuEPha7zvCSHWrYgS8yxAfBbl69SpNnjyZEhMT6fnz59SgQQP66quvaOLEiVRUVER+fn60a9cu0tDQoMmTJ9OZM2fI0NCQtmzZQkSlQ6y//vpruaHYd+7coYYNG9KOHTto+fLldPHiRbKysqLVq1eLtTi///47dezYkZ49e0aGhobic8vO2PvXX3/R2LFjKSEhgZ48eUKmpqY0fPhwmj17NqmplbbWHTp0iObOnUsXLlwgTU1NsrW1JR8fH/L1lZ974U2eP39OBgYG9GfqI9KvoRyF2ttQV1PNH4aWuurFnf4sX9Eh/Ce2ZsrVJPUhu///s9SqkrlHris6hHdWmJdDu0a3pezsbKpRyeW17Npw5+HT93qv58+fk6VpzSqJuaKpRBJTGWRJzIULF+TmfFFGnMRULU5iqg4nMVWHk5iqwUlM1VKJjr2MMcYYY69SiT4xjDHGGHu9j3nZgY82ibG0tHynGX8ZY4wxZfS+Sweo8rID3JzEGGOMMZX00dbEMMYYYx8Cbk5ijDHGmEriZQcYY4wxxlQM18QwxhhjquwjrorhJIYxxhhTYTw6iTHGGGNMxXBNDGOMMabCeHQSY4wxxlTSR9wlhpuTGGOMMZUmVMC/72jNmjVkaWlJOjo65ObmRn/88cf7f47/gJMYxhhjjL21Xbt2kb+/P82ePZsSExPJ0dGRunbtSo8eParyWDiJYYwxxlSYUAH/vIulS5eSr68vjRw5kuzt7WndunVUrVo1ioiIqKRP+GacxDDGGGMqTNax933+fVuFhYV0/vx56ty5s7hNTU2NOnfuTKdPn66ET/fPuGOvCpCttp3z4oWCI3k36mqq2V1MS1314s55ka/oEP6T5895Jfmq8uLFS0WH8M4K83IUHcI7K3qZS0R/l9tV4fnz5xXy/FdfR1tbm7S1teW2ZWZmklQqpbp168ptr1u3Ll27du294vgvOIlRAS/+P3mRNG+s4EgYY4y9jRcvXpCBgUGlvoeWlhaZmJiQdUOL934tPT09srCQf53Zs2fTnDlz3vu1KxMnMSrAzMyM0tLSSF9fn4QKHtD//PlzsrCwoLS0NKpRo0aFvnZlUcWYiVQzblWMmUg14+aYq05lxg2AXrx4QWZmZhX6uq+jo6NDqampVFhY+N6vBaDc9eXVWhgiImNjY1JXV6e//vpLbvtff/1FJiYm7x3Hu+IkRgWoqalRvXr1KvU9atSooVKFEJFqxkykmnGrYsxEqhk3x1x1Kivuyq6BKUtHR4d0dHSq7P20tLTI2dmZ4uLiqHfv3kREVFJSQnFxcTRx4sQqi0OGkxjGGGOMvTV/f38aPnw4ubi4kKurKy1fvpxyc3Np5MiRVR4LJzGMMcYYe2uDBg2ix48f06xZsygjI4NatGhBBw8eLNfZtypwEvOR09bWptmzZ7+27VNZqWLMRKoZtyrGTKSacXPMVUdV41YmEydOVEjz0asEVOU4MMYYY4yxCsKT3THGGGNMJXESwxhjjDGVxEkMY4wxxlQSJzGMMabCuFsj+5hxEsOYEit7gVLGi1VJSYmiQ/hopaSkUGFhIQmCoBTnxoMHD/h8YFWOkxj21o4dOyau46TqkpOTxf/ftGkTnT17VoHRvF5JSYncNOAVveTEfyG7WF64cIGISmeTVlWvXnCVIRF4Wzt37qTu3btTdHQ0FRUVKTyRiYiIoJYtW1JCQoJKfY9M9aluCcSq1MyZM8nf37/cehmq6PLly/Tpp5/S4sWLKSAggCZMmEC1atVSdFhyjh07RllZWURU+t3PnTtXsQH9P0EQ6MCBA+Ts7ExHjx5VdDjvRZaAJSYmEpFyJIlvq3fv3tSoUSNavHgxxcTEKDyRGTlyJNWtW5dGjx5NCQkJH2SNzJs+04f4WVUJzxPD/tXt27dp0qRJNHXqVOrQoYOiw3lvDx48oM2bN9PSpUtJKpVSYmIiNWrUiIqLi0lDQ/HzP2ZlZZGVlRW1bNmSGjVqRDt37qTTp0+Tvb29okOje/fu0cqVK6lx48Y0btw4RYfz3uLi4mjChAn0888/k7W1taLDeSuy87SgoIB69epFjx8/pqCgIOrZsydpamq+diG/ylRYWEhaWlpEROTs7EyFhYX0/fffk0QiUemaurJKSkrEz3LixAl6+vQpaWhoUNeuXUlDQ0NuP6ta/K2zf7R06VLq0aMHZWdnk5WVlaLDqRBmZmZkbm5OOTk5ZGhoSPv27SMiIg0NDZJKpQqOjsjQ0JCuXbtGp06dosjISIqOjlaKBCYpKYl8fHzo0KFD1Lx5cyJSrSaY19HT06Nnz57RtWvXiEg1Po/sPNXW1qbo6GgyNjamBQsWKKxGRlNTk4iI7ty5QwsWLKDk5GQKDAz8oJqWZAlKYGAg+fr60vTp0yksLIyaNWtGz5494wRGgfibZ/+oZ8+elJWVRSdPnqTr168rOpz/TFblK/tv27Zt6cSJE+Tr60sbNmyg+fPnExGRurq6wmMEQM+ePaPi4mLS0dGhRYsWyTXjKaqzb1ZWFgGgmzdvUkpKChGRwvtivIuy368sZjc3Nxo8eDDNnDmTMjMzVaZJSXaeyhKZWrVqKSyREQSBoqKiyM7OjuLj42nQoEGUnp5Oo0aN+qASmTVr1lBERARt376drl69Sv3796eUlBQ6ffq0eMyH8llVChh7g5KSEgBAamoqjI2N0aFDB6SkpCg4qncnlUrF/79x4wbu3r2LvLw8AMCDBw/wzTffoEmTJliwYIF43LfffosLFy4oJMazZ8+K3/29e/dgbm6OLl264K+//qqyeN7kzJkz8PLyQosWLRAdHS1ul8WrCp48eSL3+OjRo2jVqhWOHj0KACguLlZEWP9K9h3fvXsXly5dwoMHD/Dy5UsAwMuXL+Hp6QknJyfs2bMHhYWFcs+pTI8fP4atrS3mz58vbnvy5AkcHR1hb2+PU6dOyZ3fqqikpATjx4/H0qVLAQD79++Hvr4+1q9fDwDIyclR2vPmQ8dJDCsnOjoay5cvx+rVq5GYmAig9OJfs2ZNdOvWDdevX1dwhP/NjBkzYGFhAXNzc9SvXx/btm1Dfn4+MjMzMWvWLDRu3BhffPEFvLy8YGFhUWWFUtkCPigoCBKJBDt37sSLFy8AAFeuXIG5uTm6d++O9PR0FBUVwdvbG0uWLKm0mGQXvwcPHuDmzZvIyMgQ9x07dgy9e/dGhw4d8PPPP5d7jjLbtWsXBEHAN998g4MHD4rbvby84OHhocDI/pnsu92/fz8aN26Mxo0bw9TUFCEhIbh69SqAvxMZNzc3REZGiolMZXv27BlsbGywa9cuABDf9/Hjx7CwsICHhwd+//13lUpkXncuf/bZZ1i4cCEOHDgAPT09rF27FkDp73flypUIDw+v6jAZOIlhrwgICEDDhg3h4eGBvn37QhAEHDp0CABw69YtGBsbw8vLC1euXFFwpP+ubKEZExMDY2NjREVFIS4uDn5+fjA0NERoaCgAICMjA99//z26dOmCL774QiyIq7Lg/eabb1C7dm0cOnQI2dnZcvuSk5NhZmaGxo0bo2XLlrCxsam0i1TZC6aLiwvq1q0LT09PzJw5Uzzmt99+Q+/evdG5c2fs3bu3UuKoCLLPIvvv06dPsXjxYvTs2RPGxsb4/PPPceTIEZw5cwatW7fGr7/+qshw/9Gvv/4KAwMDLFu2DAUFBZgzZw6MjY0xZswYXL58GUBpIuPq6ooOHTrg+fPnVRabnZ0dRo8eLT4uKiqCVCqFl5cXBEGARCIRa42UXdnf/J07d8TH8+fPh0QiQY0aNbBmzRrxmEePHsHLywuLFi2q8lgZJzGsjB07dsDExAQJCQkAgG3btkEQBGzfvl085ubNmxAEAf7+/ooK851t2bIFS5YswbJly+S2f/vtt6hWrRr+97//yW2XXfCKioqqKkRcunQJNjY2+O233wCU3t1evnwZa9euRVxcHIDSC3BQUBDCwsLE2CorxgMHDqB69epYunQpkpOTERAQgJo1a2Ls2LHiMceOHYOHhwc+++wzsdZImZS9GD19+hT5+fni4ydPnuDMmTPo3r07PvnkE5iYmKBWrVqYM2eOIkL9V8+ePUPv3r3F+NLT09GoUSNIJBI0bNgQo0aNEm8s8vPzcffu3UqJ4021bZGRkTA3N5drkgUAf39/nDx5EqmpqZUST0Ure87Mnj0b7du3F8vDu3fvomnTprC2tsaZM2eQm5uLu3fvonv37nBzc6vS8oL9jZMYJpo7dy4mTJgAANi7dy/09PTENt/s7GyxILp//77KtP+mpqbC1tYWgiAgKCgIAOQuZj179kSXLl0AyPeFqOymkVdreG7fvg0HBwfs3r0bCQkJGD16NGxtbWFnZwctLS3s37+/3GtUVqGZnp6O9u3bY/ny5QBKEwBzc3O0adMGTZo0kUtk4uPjkZaWVilxVJSQkBC0bNkSLi4u6NWrF+7evSt+/zk5OUhJSUFAQACsra1hZGSE8+fPKzjiUrJz8M6dO8jKykJMTAxu3LiBzMxM2Nvbw8fHB0BpM6mhoSG++OILsUamMuM5duwYQkNDMW7cOJw/fx4FBQXIzs5GSEgITExMMGzYMKxbtw5jxoyBnp4e7t+/X2kxVaSyv/np06fDxMQEu3fvxoMHD8TtN27cgLW1NZo2bYo6deqgdevWcHNzE2tFVaVc/JBwEvORK/vDnTVrFsaOHYt9+/ZBT09Pro1327ZtCAoKkmvmUMY7j1eTj8LCQhw8eBCffPIJrKyskJubC+Dv2L/++mv07NmzyuOUuXTpEoqKipCRkYFu3brBxcUFGhoamDBhAqKjo5GRkYG2bduWq0WqbMuWLcPly5eRkZEBW1tbjBs3Djk5OfD29oa2tja8vb2rNJ53UTZBDA8PF5tgFi5cCCcnJ1hYWOD48ePlnnfu3Dl06dJF7OugDH18du3aBVNTU1y5cgVPnz4FAKxYsQKdOnUSOyivXbsW1tbW6NatGx4+fFip8ezbtw+Ghobo0aMHOnXqhNq1a2PJkiXIzs5GTk4O9uzZgxYtWsDZ2Rlubm5V2jn+v7p48aLc49OnT6N+/friOZKfn4+HDx/iwIEDePHiBV68eIG4uDiEh4cjLi5OTFyUsTz8GHAS85E7efKk+P9bt25FkyZNUL16daxatUrcnp2dje7du2PatGmKCPGtlb145eXliU0chYWFiIuLg52dHZo3b44nT54gLy8PxcXFaNeuHYYMGaKQeI8ePQpBELBp0yYApSOR4uLiEB8fLx5TUlICV1dXhXUaDAsLQ8+ePZGZmQkAWLx4MZo1a4YuXbogPT1dITG9rUOHDmHWrFnYuXOn3Pbu3bujYcOG4vlR9uLj6+uLjh07Vmmcr5IlTy9fvoSPj484IkYmJCQEbm5u4vc/bdo0hIeHlxt1VdFOnz4NMzMzREREACj93jQ0NGBmZob58+fLvX9eXh5ycnIqNZ6KMHPmTAwYMADA39/7wYMHYW1tjadPnyIhIQHTpk1DkyZNYGBggM6dOyM5Obnc63ANjOJwEvMRu3DhAgRBwOrVq8Vt3t7eqF69OiIjI5GSkoLLly+ja9eucHJyEgt7ZbhD/SchISHo2LEjJBKJOGJCKpXi6NGjsLOzQ506dSCRSDBixAjY2dlV6XDUV02dOhW6urrYvHmz3Pbc3Fykpqaie/fuct99RSopKRE/c3JyMn799VccOnQIN27cEI/58ssv0bp1a/Gxv78/5s2bh6ysrAqPpyKdOnUKlpaWqF69Ovbt2wcAKCgoAFB6gW3cuDFCQkLE42UJsL+/Pzp16iQOwVeU48ePw87ODp07d8a5c+fk9kVERKBJkybo06cPevfujWrVqokjlCrTDz/8gMDAQAClzZ+WlpaYNGkSZsyYAXV1dYSFheHOnTuVHkdFSkxMFH9bsn5Ejx49gq6uLlxcXKCvrw9fX1/s3r0bZ86cQa1ateRG5DHF4yTmI7VmzRp89dVX0NXVhZqaGr777jtxX8+ePdGsWTNoaGhAIpHA3d1dZdp816xZAzMzM8yePRtDhw6FIAgICwsDUHqhiouLQ4cOHWBsbCx3cajsquB/SpCmTZsGTU1NbN26VbzQLl++HF26dEG7du0q/Lt/ddTK3r17YWpqik8++QS2trZo06aNeLe9ceNGODk5YfDgwfDx8YG+vr5KDLF/+PAh5s+fD2NjYwwePFjcXlRUhIKCAnh4eJSrWbx+/TocHR3FaQWqwutGv5WUlCApKQmOjo5QU1PD6dOnAcifo0uWLMGwYcPQr1+/SusHIztnL168iPT0dNy/fx/JycniUO5Ro0aJx5qbm8PQ0BBLly5V+jLidfbt2wcLCwuxk/+tW7cwf/58xMbGir+X4uJiuLq6ikkxUw6cxHyEZs6ciTp16iAyMhIbNmyAt7c39PT05EYWXL58GYcPH0ZycrJY0Cpjm++rF4ENGzbgp59+Eh+vXbsWampq4mcrLi7GkSNHxDZ72WeqqoJ3yZIlrx3GO23aNGhra+OHH34AUHpXuGPHjgpvb/f19cWXX34pvm5CQgJq1qwpDhk9cOAANDQ0xInLMjIy8O2338LDwwNdunRBUlJShcRRkV49B2QX38zMTISFhaF+/fr46quv5I5p0aIFZsyYUe61Xh3aXhXS0tLEiQN37NgBPz8/FBUV4cKFC3B0dESLFi3EphlZkitTWb/JssPsTU1NERwcLPYnu337Npo1a4YDBw4AKO3oP2TIEAQEBMjV4imzsjcVSUlJiI2NRb9+/eDk5CSOEJQdI5tLStZnTRWTtA8ZJzEfmYyMDDg7O2PLli3itrS0NMyaNQu6urpv7ECqjBNVlS2I9uzZg/Xr18Pd3R2RkZFyx61du1as7gZKE5a4uDi0atUKTZo0kRutVJkxAkCPHj1QvXp1cXbYsrp06YK6deti3bp1ctsrqtD88ccfUbt2bbmaho0bN6J79+4ASkdyWVpayo0+kvWFASBexJRJ2e937dq1mDRpEkaOHCleiJ4/f47Q0FDUqlUL7dq1w4gRIzBgwAA0btxYLgF4dT6Zqoq9oKAA/fr1g7u7O6ZNmwZBELBhwwbxmIsXL8LOzg6tWrUSm7iq6mYiNjYWurq62LBhg1z/p0uXLsHMzAxbt27FnTt3MGfOHLRv317hTXBvq2xZ5ufnB1tbWzx+/BjHjx9H//794ejoiGPHjgEoTRpXrlwJiUQCiUSiMjXSHxNOYj4yjx8/hrGxMRYvXiy3/d69e5BIJBAEQRxaCyhv/5eycQUFBUFDQwNubm4QBAHDhg0rN0X/unXrIAgCtm3bBqC0EPr111/h7u5eJXNYlB1mOmTIEBgaGorzvwCln2f06NGwtrZG+/btK+V7X7RoEWxtbQEAUVFRWLZsGdavX4/Ro0fj4cOHMDc3x5gxY8RC/vDhw1i0aJE4KkbZlL0YTZs2DUZGRujVqxc6dOgADQ0NBAcHIysrC8+fP0dYWBgaNGgAR0dHHD58WHyeMtQupqenw8nJCYIgYNKkSeX2yxKZ1q1bV1ki+fLlSwwYMECcliA3Nxe3bt1CWFgY4uLi0LlzZ9SqVQtWVlaoXbu20gxLfxdPnz7FsGHD5OaJOnHiBAYMGABHR0dxdNLFixflmsmU4Zxhf+Mk5iNTWFiIkSNHYsCAAeX6NowfPx6dO3eGhYUFduzYoaAI38358+fRvXt3nDlzBllZWdi+fTsEQcCMGTPw+PFjuWP3798vVwBJpdJKuyiUvcCuW7cOXl5eciPBBg8eDCMjI/zvf/8T29wHDRqEpKSkSqsV+OOPP2BjYwMPDw8IgoB9+/Zh37590NHRQa1atco1uYwePRpDhw5V+lEm6enp8PX1xR9//CFuW716NYyMjLBw4UIApTWQoaGhcHJywpQpU8TjFFnDKOtYnZ+fD4lEAgcHB3h5eWHPnj3ljk1KSkLdunWrbORUXl4eXFxc8NVXX+HJkyeYOHEi3N3dYWJiAktLS6xatQoxMTGIjo5WmYnsylq3bh2MjIzg6uqKW7duye2TJTJOTk7lJsLkGhjlw0nMRyAlJUVuWOCuXbtgY2ODgIAAXLt2DUBptXufPn2wfv16DBw4EN7e3sjPz1famhig9ELVq1cv9O7dW66vgCyRmT59ulxziExl30mVvTDGx8dj8uTJ0NLSQt++fXH27Flx37Bhw6ClpYWOHTvC0dERDg4OYiFZWRfX8ePHQxAEuRFHkyZNgpqaGo4cOYKsrCxkZmYiMDAQtWvXVvrlJbZv345q1arBxsYG165dkztfFy9eDF1dXfEi9ejRI4SGhqJ58+YYM2aMokKWc/HiRTGJvXHjBjw9PeHp6SnXrwsovXgmJyfj5s2bVRbb1q1boaurixo1aqBPnz7YunUrAGDixInw9PRUyibmt3X27Fm0adMG1atXF8vGsst4xMfHw8PDA8OHD1dQhOxtcRLzgZs+fTrMzMxQt25dSCQSsePdhg0b4ODgAGdnZ/Tq1QvOzs5wdHQEUDrs19XVVenvOjZt2oQaNWrAwsKi3KRaP/zwA9TV1TFu3DiFDQeeOnUq6tWrh2+++QajR4+Grq4uPvvsM3EacwBYuXIlAgICEBAQUOmdjPPy8uDh4QEfHx/Y29vj888/B1DaVDBo0CBoa2vDysoKEokEDRo0qNJROv/V0aNH0b17d+jq6oqdjmV9MzIzM2Fubi63tlNmZiaCg4MhkUgUvir4/fv3IZFI4OXlJTY3JiUlwdPTE926dcPu3bsBlDaXlq09qkrJycli85ssaZkwYQKGDh1aqX3JKtLrkq3i4mJcvHgRTZs2RcuWLctNggmU/i1UOVH7WHAS8wHbt28fGjZsiKioKBw4cACtW7eGpaWl2H59/PhxLFu2DAMHDsSMGTPEQmnYsGEYMWJEuZEQivSmwmT37t0wMTHB2LFjkZKSIrdv/fr1+OSTTxRSm/THH3+gdu3aYgdBoHSyMFNTU3h5eeHMmTOvfV5l1xLJCutNmzbBxsYGQ4cOFfdFR0dj8+bNiI6OVsqlBF53DkilUsTHx8PNzQ0NGjTAo0ePxH33799HvXr1EBMTA+Dv5rknT568toZOEdatW4eOHTuiT58+YiJz6dIl9OjRA82aNUPr1q2hp6f3xvOlKl29ehVBQUEwMDCo1OUNKlLZc+Z///sffvrpJ/zxxx/iKLTLly+jSZMmch2nX11YlRMZ5cZJzAfqxx9/xJo1a7By5UpxW2FhIdq1a4cGDRq8tiNeWlqauA7Ln3/+WZXh/qOyhcjBgwfFoeGyGgvZ4nMTJ0584xwmVZ3IJCYmwtzcXPyeZcnJyZMnoa6ujs8//1yc/0MRXrx4gYiICNjY2MjNo6Ksyp4Df/75J65fvy7+raVSKU6ePAlXV1eYm5tj06ZNiIyMRI8ePeDo6Kg0NYqyc/DVeCIiItCuXTu5ROb69esIDw9HUFBQlUxk92/OnTuHwYMHw87Ortw0/apg2rRp0NfXR+PGjaGpqYl+/frh4MGDAEqTRltbW0gkEqUcgcf+GScxH6Dnz5/D1NQUgiCIE3rJCtDCwkK0b98eVlZWOHnypLj9xYsXGD9+PBwcHJR2vZOAgABYWVmhVatWaNWqFUxMTMRkKzIyEvXq1YOfn1+V9+Moe4GVXaCuXLkCfX19sR9BYWEhpFIpXr58CXt7e9SpUwfe3t4KrRHIyclBREQEHBwc8Nlnnyksjn9TNgGdPXs2mjZtioYNG8LGxkYcbVZSUoKTJ0+iXbt2EAQBQ4YMwapVq8SLkrIkMmfOnMH48ePLzUcTEREBZ2dnDBgwABkZGQCUa2RgXl4ejh8/jnv37ik6lLdS9rtLSEiAjY0NTpw4gdzcXMTFxaF79+7o2rUrfv/9dwClTUc1a9aUm8CPqQZOYj5QsiHT9vb2uH37NoC/f9hFRUWwtbUV1wyRyczMlFuxVZmsX79ebihnZGQkBEEQmwqA0k6e6urqVbpYYtkEZu3atQgJCRFH88yePRtaWlpyQ3pzcnIwZswY7N69GxoaGnJzgihCTk4O1q5dC1dXV6VfC2n27NmoXbs2Dh8+jOvXr8Pb2xuCIMgt2Hj8+HF069YNtra2Yp8XZZq/ZN68eXBwcMCkSZPKzZw8ZcoU6OjooGvXrpW+kOPHYuHChZg8eXK5jtyyJkjZiDypVIobN24oTbLL3h4nMR+QI0eOYP/+/eLsn2lpaXBwcECrVq3EO6iyVdplf7DKdNcHlI8nMDAQc+fOBQD89NNP0NfXx/fffw8AyMrKklu8raoKorIxTp06FWZmZli7dq2YND58+BC+vr4QBAGBgYFYuHAhPDw84OzsDADo2LEjvvzyyyqJ9Z/k5uYq/VpI586dQ4cOHcS5dWJjY2FoaIhPP/0UgiCIEwRKpVKcOHEC7dq1Q/PmzZUuKS8oKEBYWBhcXV0xYcIEue99165dcHZ2xqBBg5SyT5IqKHtT8fTpU3ECwVatWpU7x8PDw1GtWjWx5kuGExnVwknMB2L69OkwNzdHy5YtoaOjg+HDhyMtLQ337t1D06ZN4erq+tqCURl/sK9LqPr16wd/f38cOnQI+vr6cnff3333ndySCUDlfq5XR2Vs3LgRdevWlZunBChtQioqKkJ4eDhatmwJiUSCXr16iR2m27Vrh3nz5lVanKrs1XMgLS0NYWFhyM/PR1xcHExNTREeHo6cnBx4enpCEAS59b9Onz6NZs2aQSKRQCqVKiRJl73nlStXcPr0abEPhuycdXNzkxs9N3PmTAQHB+PZs2dVHuuHZsaMGRgzZgxevHiBkJAQqKmpISIiQq5cOHDgABwcHLjWS8VxEvMBWLhwIUxNTcWhu6tWrYIgCOjbty/S0tKQlpaG5s2bw9LSUuHDSv9NfHy8mAz4+vri22+/BQBs2bIFbm5u0NHRERMYAHj27Bl69OiBWbNmVUl8gwcPRmxsLIC/L1ITJkwQ29KvXLmC9evXw8nJCfb29uKxr94FzpgxA2ZmZiqxmGJVK3uhuXnzpninLLvLHj58OMaNGyeOIhkzZgxcXFzQtm1b8bklJSVISEhQ2KrKsnNj7969qFevHiQSCYyMjODl5YVDhw5BKpVi4cKFkEgkqFOnjjhMXBk68aqisknqwYMHYWtrKzcnk7+/P7S0tLBixQpcuHABd+/eRZcuXdC2bVulq4Vm74aTGBWXnp6O4cOHY+fOnQBKC00jIyMEBwfDwMAAffv2RWpqKlJTUzFkyBClrHkBSguhx48fo169eujfvz+GDBkCPT09sZNxWloaPD090bRpU+zduxd5eXm4du0aunfvDhcXlyqbCjw4OBgvX74E8PdQzAULFsDExAQzZsyAs7Mz+vTpg2+++QbDhg1DzZo15e6sL1++jMmTJ8PU1FQl5mGpSmvXrpXrVD59+nQ0bdoUtWrVQkBAgJjctmjRAlOnTgVQ2t+lb9++YrIIKE/t4smTJ2FkZCT2ezp69CgEQRAX2ywuLsbp06cRFBSEadOmcQJTAXbu3Imvv/5aPD/KlgtTp06FIAioXr06fHx80KlTJ/E3zMOoVRcnMSru5cuX2LdvH549e4azZ8/C0tISK1asAFC6YrIgCOjYsaNcDYyyFPKvc/36ddSuXRsaGhrlFnK8ceMGOnToADs7OxgYGKBVq1Zo06ZNlSzKFhgYiM2bN4uP16xZg/Xr16OgoAA3btxAYGAg7O3tsWzZMnEG0Li4OLi7u8uNQMrKysLRo0cVVkOgrG7fvo169erB19cXN27cQHR0NMzNzbF//36EhITAzc0Nffr0wfnz57FixQpoampi9OjRcHV1RcuWLeVqYJTFsmXL0Lt3bwCl57WVlRV8fX3F/WU79vJF9L+R/b2lUimKiorg4uICQRDQrVs38Ziy3+3cuXMhCAJ+/PFHcRuvhaTaBAAgptKKiopIU1OTwsLCKD4+niIjI8nAwIBWr15NCQkJlJmZSb/88gupqakpOtR/VFxcTMnJyTR48GDKzc2lTz75hPz8/EgikYjHZGZm0oMHDygpKYlsbGzI2dmZ1NXVqbi4mDQ0NColrqysLOrTpw+VlJTQsGHDaNSoUdS7d2+6fPkyzZ8/nwYMGEAaGhr04sUL0tfXJyIiqVRKn376KWlpaVFUVBQJglApsX1ILl68SD4+PtSuXTtSU1Mje3t7GjVqFBERxcbG0pIlS8jIyIg+//xzyszMpJiYGDI3N6d169aRpqYmSaVSUldXV/Cn+Nu0adOoqKiIli1bRvXq1aMePXrQunXrSBAE+umnn+j58+c0dOhQ0tLSUnSoKi8jI4NMTEzo5cuX5O3tTWfPnqWwsDAaMGAAaWlpUUlJiVj+TZ48mcLDwykyMpL69eun4MjZe1N0FsXen+xuZOTIkWjbti2ys7Px8uVLfPrpp2IzE6Ccd3tviikpKQlWVlbo16/fv85WWpk1MLLv9q+//kL//v3h7u4urmszYsQINGnSBNu2bRPnI3n+/Dn2798PDw8PODo6irVEylRDoMzOnz8PFxcXGBkZlRsqHxMTg06dOqFfv36Ij4+X26fou+myswHLzoUDBw5AT08P+vr6+Prrr+XOdR8fH4wYMUKphn+rqm3btsHLy0tsbszLy4OnpyecnZ2xd+/e1zYZyZqWoqKiFBIzqzicxHxATp8+DU1NTTg4OMDa2hrNmjVTeOH+T8pe2Pfu3YsVK1bgyJEjePLkCYDSz2NlZYVBgwbhxIkTAAB3d3e5WYgrW9kE6dSpU3B3d4ezs7M4jH3o0KHipGsvX77ErVu3EBwcjFGjRonfvTL/DZTRpUuX0KhRI3h6euLSpUty+2JjY+Hg4IDAwEBxm7IkiPv370ebNm1gbW2NWbNmIS4uDtOnT0edOnVw6NAhAKXDfoOCglCnTh3uA1NBIiIiIJFI4O3tLXbmzc3NRadOndCqVSvs27ev3FICAJRmNmT2frg56QOTmJhI+/btoxo1apC/vz9paGhUalPLfwVAbGIJCAigbdu2UfXq1UlHR4datWpFCxYsIHNzc0pISKBRo0aRjo4O5efnk1QqpaSkpCqvgp8yZQrdunWLHj58SFevXqXatWvTd999R3379qVhw4bRuXPnKDg4mAYOHEh5eXmkp6dHgiAoXROHqkhKSqKRI0eSi4sL+fn5UdOmTcV9p06dIjc3N6X6XhMTE8nDw4OmTJlCT548ofj4eLKysiJnZ2e6c+cObdiwgezt7UlHR4cePnxIUVFR1LJlS0WHrXLKNguVtXPnTlqzZg3Vq1ePpkyZQi4uLpSXl0d9+vSha9eu0bZt28jd3V0BEbNKp+AkilUyZa8FSEpKwqefforExERx9thX15G5dOkSVq1ahYULFyqkdmPr1q0wMjLC+fPnkZmZifT0dHh6esLFxUWsjh4+fDgMDAzEO25AeWoIVFViYiKcnJzg6+srdpYuS1k6qN+8eRPz5s3D/PnzxW0xMTHw9PTEwIEDER0djfj4eISGhmLHjh24e/euAqP9MBw+fBg3b96U2xYZGYm2bdti0KBB4vpOOTk58PPzU5pzhVU8TmKYwvz444/o3Lkz+vfvL1fdK1sQr2/fvmIi87r1iarKrFmz0KZNG7lJ0+7fvw9XV1dxlXCgdEp5LiwrVmJiIlq1aoX+/fuLMyErk+zsbLi4uKBOnTqYPn263L7o6Gh07NgRffv2fe2Cq+ztlf39X7hwARYWFpg4cSJSU1Pljtu8eTP09fUxePBgnDx5Um4f/zY/TMo9XIV9sEpKSujSpUuUmppKly9flqsiHjlyJI0cOZKePn1KQ4YMoSdPnsjtr6pmBPx/S6uuri4VFBRQQUEBCYJARUVFZG5uTgsWLKBHjx5RYGAgHT16lL755htSV1cnqVRaJfF9DFq2bEmrV68mfX19atCggaLDKadGjRq0fv16MjQ0pBMnTlBycrK4r2fPnjR16lS6ffs2LV26lPLy8sRzir29sk1IMTExZGlpSVOnTqUzZ87QsmXL6M6dO+KxI0aMoEaNGtGJEyfoyJEjRPT371iZmh9ZBVJwEsU+Eq8bhVRYWIjFixejcePGGDNmTLmVfVetWoXx48crfFTVn3/+CQ0NDcyZM0du+y+//IKePXsiKChI4TF+6MrOB6KMkpKS0KJFC4wePVpcWV3m0KFDPC/Qf1S2SXbGjBmoW7cuwsPDAZTOg9WiRQv4+fmJNTIPHz6Ej48PtmzZorTnCqtY3LGXVbqyd1LJycninB52dnZUXFxMixcvpqioKHJxcaHQ0FBxrhWivzsAv6lDX1XZsmULjR49mvz8/GjgwIFUs2ZNmjRpEjVv3pxCQ0OJiLgTbyVDmc7gyujChQvk4+NDTk5ONHnyZLK3t1d0SB+MefPm0cqVK+nAgQNkbW1NhoaGREQUHh5O27dvJyMjI/Lw8KDDhw8TEdHBgweVotxglY+TGFapyl54goKCaM+ePZSbm0vFxcXk6+tLc+bMISKiRYsWUWxsLLm4uNC8efPIwMDgta+hSHv37qXx48eLI6Nq165NCQkJpKmpqTQxMsW6cOECjR07lho1akSzZ88mW1tbRYek8p4+fUqDBg2iESNGkLe3N6Wnp9P169dp586d1LlzZ7px4wZduXKFkpKSyMrKinbv3s2/yY+Ico27ZR8cWSGyePFiWr9+Pf30008kCAKlpqbS2LFjKSMjgzZu3EgBAQFERBQREUGWlpbk7+9f7jUUrV+/ftS6dWtKT0+n3NxcateuXaXPFsxUi6wPT0BAgFwizv47QRDoypUrdPXqVTp+/DitXbuWUlNTqaSkhGJiYig4OJi2bt1K2dnZZGRkRIIg8G/yI8I1MaxSlL0LKikpoX79+lHTpk1p/vz54jG//fYbderUiVauXEkTJ06kwsJC2rlzJ3l7e6tMsww3IbHXyc/PJx0dHUWH8cHYtGkTBQQEkFQqpbFjx5Knpyd17tyZhgwZQurq6rR161bxWG5C+rhwEsMqXNlCJDMzk4yNjalp06bUo0cPWrRoEQGg4uJi0tTUpMmTJ9OlS5coKipKri8MJweMsbLu3btHBQUFZG1tTUSl5UyXLl1IIpHI3Ryxjwunq6xClU1gli5dSrNmzaL09HTy9vamPXv20Llz50gQBLGqV09Pj9TU1OQSGCIeDskYk1e/fn2ytramnJwcio+Pp169etGjR4/EfnXs48RJDKtQsgQmMDCQwsLCqF27diSVSqlbt27k4OBAwcHBYiKTm5tLf/zxB9WrV0/BUTPGVAEAOnfuHC1cuJCKioro/PnzpKGhwXMzfcS4OYlVuLi4OPL19aXt27dTmzZtxO0xMTG0adMmiouLIzs7OyooKCAAlJiYyKMJGGNvpaCggK5cuUKOjo6kpqbGnXg/cvyXZxXu3r17VK1aNXHRPlkTU8+ePcnBwYGuX79OZ8+epdq1a5OPj4/SLlLJGFM+2tra4uKZJSUlXG585PivzyqMrCbl5cuXctW7ZVdzPn/+PDk5OVG3bt3E/VKplAsixtg741FIjM8AVmFkTUEdO3akGzdu0PLly8Xt6urqlJOTQz/88AMdPHhQ7nnciZcxxth/wX1iWKVYv349TZw4kcaNG0effvopaWlp0YIFCygjI0PsjMcYY4y9D05iWKUAQDExMTRp0iSSSqVkaGhI5ubmFBsbK66dxDUwjDHG3gcnMaxSZWZmUnZ2NpWUlFDjxo15NAFjjLEKw0kMq1I8JThjjLGKwkkMY4wxxlQS3xIzxhhjTCVxEsMYY4wxlcRJDGOMMcZUEicxjDHGGFNJnMQwxhhjTCVxEsMYY4wxlcRJDGOMMcZUEicxjLE3GjFiBPXu3Vt83KFDB/r666+rPI7ff/+dBEGgrKysNx4jCAJFRUW99WvOmTOHWrRo8V5x3blzhwRBoIsXL77X6zDG/htOYhhTMSNGjCBBEEgQBNLS0iIrKyuaO3cuFRcXV/p779u3j+bNm/dWx75N4sEYY++DF7BhTAV169aNNm/eTAUFBXTgwAGaMGECaWpq0owZM8odW1hYSFpaWhXyvjVr1qyQ12GMsYrANTGMqSBtbW0yMTGhBg0a0Lhx46hz584UExNDRH83AX377bdkZmZGNjY2RESUlpZGAwcOJENDQ6pZsyb16tWL7ty5I76mVColf39/MjQ0pFq1atG0adPo1VVJXm1OKigooMDAQLKwsCBtbW2ysrKiTZs20Z07d6hjx45ERGRkZESCINCIESOIqHT9rNDQUGrYsCHp6uqSo6Mj7dmzR+59Dhw4QE2aNCFdXV3q2LGjXJxvKzAwkJo0aULVqlWjRo0aUXBwMBUVFZU77vvvvycLCwuqVq0aDRw4kLKzs+X2b9y4kezs7EhHR4dsbW1p7dq17xwLY6xycBLD2AdAV1eXCgsLxcdxcXGUkpJCR44codjYWCoqKqKuXbuSvr4+nThxgk6ePEl6enrUrVs38XlLliyhLVu2UEREBMXHx9PTp09p//79//i+w4YNox9//JFWrlxJV69epe+//5709PTIwsKC9u7dS0REKSkp9PDhQ1qxYgUREYWGhtK2bdto3bp1lJycTJMnT6YhQ4bQsWPHiKg02erbty999tlndPHiRfLx8aHp06e/83eir69PW7ZsoStXrtCKFStow4YNtGzZMrljbt68Sbt376aff/6ZDh48SBcuXKDx48eL+yMjI2nWrFn07bff0tWrV2nBggUUHBxMW7dufed4GGOVAIwxlTJ8+HD06tULAFBSUoIjR45AW1sbU6dOFffXrVsXBQUF4nO2b98OGxsblJSUiNsKCgqgq6uLQ4cOAQBMTU2xaNEicX9RURHq1asnvhcAuLu7w8/PDwCQkpICIsKRI0deG+dvv/0GIsKzZ8/Ebfn5+ahWrRpOnTold+yoUaMwePBgAMCMGTNgb28vtz8wMLDca72KiLB///437v/uu+/g7OwsPp49ezbU1dVx//59cduvv/4KNTU1PHz4EADQuHFj7NixQ+515s2bh9atWwMAUlNTQUS4cOHCG9+XMVZ5uE8MYyooNjaW9PT0qKioiEpKSuiLL76gOXPmiPubNWsm1w8mKSmJbt68Sfr6+nKvk5+fT7du3aLs7Gx6+PAhubm5ifs0NDTIxcWlXJOSzMWLF0ldXZ3c3d3fOu6bN29SXl4eeXp6ym0vLCykli1bEhHR1atX5eIgImrduvVbv4fMrl27aOXKlXTr1i3Kycmh4uJiqlGjhtwx9evXJ3Nzc7n3KSkpoZSUFNLX16dbt27RqFGjyNfXVzymuLiYDAwM3jkexljF4ySGMRXUsWNHCg8PJy0tLTIzMyMNDfmfcvXq1eUe5+TkkLOzM0VGRpZ7rdq1a/+nGHR1dd/5OTk5OURE9Msvv8glD0Sl/XwqyunTp8nb25tCQkKoa9euZGBgQDt37qQlS5a8c6wbNmwol1Spq6tXWKyMsf+OkxjGVFD16tXJysrqrY93cnKiXbt2UZ06dcrVRsiYmppSQkICtW/fnohKaxzOnz9PTk5Orz2+WbNmVFJSQseOHaPOnTuX2y+rCZJKpeI2e3t70tbWpnv37r2xBsfOzk7spCxz5syZf/+QZZw6dYoaNGhAM2fOFLfdvXu33HH37t2jBw8ekJmZmfg+ampqZGNjQ3Xr1iUzMzO6ffs2eXt7v9P7M8aqBnfsZewj4O3tTcbGxtSrVy86ceIEpaam0u+//06TJk2i+/fvExGRn58fhYWFUVRUFF27do3Gjx//j3O8WFpa0vDhw+nLL7+kqKgo8TV3795NREQNGjQgQRAoNjaWHj9+TDk5OaSvr09Tp06lyZMn09atW+nWrVuUmJhIq1atEjvLjh07lm7cuEEBAQGUkpJCO3bsoC1btrzT57W2tqZ79+7Rzp076datW7Ry5crXdlLW0dGh4cOHU1JSEp04cYImTZpEAwcOJBMTEyIiCgkJodDQUFq5ciVdv36dLl++TJs3b6alS5e+UzyMscrBSQxjH4Fq1arR8ePHqX79+tS3b1+ys7OjUaNGUX5+vlgzM2XKFBo6dCgNHz6cWrduTfr6+tSnT59/fN3w8HDq378/jR8/nmxtbcnX15dyc3OJiMjc3JxCQkJo+vTpVLduXZo4cSIREc2bN4+Cg4MpNDSU7OzsqFu3bvTLL79Qw4YNiai0n8revXspKiqKHB0dad26dbRgwYJ3+rw9e/akyZMn08SJE6lFixZ06tQpCg4OLneclZUV9e3bl7y8vKhLly7UvHlzuSHUPj4+tHHjRtq8eTM1a9aM3N3dacuWLWKsjDHFEvCmXnuMMcYYY0qMa2IYY4wxppI4iWGMMcaYSuIkhjHGGGMqiZMYxhhjjKkkTmIYY4wxppI4iWGMMcaYSuIkhjHGGGMqiZMYxhhjjKkkTmIYY4wxppI4iWGMMcaYSuIkhjHGGGMqiZMYxhhjjKmk/wOE4u8Lmmty+QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "denseModel_preds = denseNet_model.predict(test_data)\n",
    "denseNetModel_Predictions = np.argmax(denseModel_preds, axis=1)\n",
    "\n",
    "class_indices = test_data.class_indices\n",
    "\n",
    "index_to_class = {v:k for k,v in class_indices.items()}\n",
    "class_names = [index_to_class[i] for i in range(len(index_to_class))]\n",
    "\n",
    "# 3) Compute your confusion matrix\n",
    "cm = confusion_matrix(test_data.classes, denseNetModel_Predictions)\n",
    "\n",
    "# 4) Plot with labels\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap='Blues', xticks_rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "78a2c70d-80ed-4cab-a4b2-ff202e0fd1f9",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 110ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resModel_preds = resNet_model.predict(test_data)\n",
    "resNetModel_Predictions = np.argmax(resModel_preds, axis=1)\n",
    "\n",
    "class_indices = test_data.class_indices\n",
    "\n",
    "index_to_class = {v:k for k,v in class_indices.items()}\n",
    "class_names = [index_to_class[i] for i in range(len(index_to_class))]\n",
    "\n",
    "# 3) Compute your confusion matrix\n",
    "cm = confusion_matrix(test_data.classes, resNetModel_Predictions)\n",
    "\n",
    "# 4) Plot with labels\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap='Blues', xticks_rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "cba7c9b6-ac65-44e6-b0a4-03b290ca3d35",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 114ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vggModel_preds = vgg_model2.predict(test_data)\n",
    "vggNetModel_Predictions = np.argmax(vggModel_preds, axis=1)\n",
    "\n",
    "class_indices = test_data.class_indices\n",
    "\n",
    "index_to_class = {v:k for k,v in class_indices.items()}\n",
    "class_names = [index_to_class[i] for i in range(len(index_to_class))]\n",
    "\n",
    "# 3) Compute your confusion matrix\n",
    "cm = confusion_matrix(test_data.classes, vggNetModel_Predictions)\n",
    "\n",
    "# 4) Plot with labels\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap='Blues', xticks_rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "fef6ab7b-f4ba-4913-a53e-813b2db9ff8f",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 116ms/step - accuracy: 0.5244 - loss: 2.3462\n"
     ]
    }
   ],
   "source": [
    "vggModel_preds = vgg_model2.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9a2153b-f108-40dd-8e30-7a39fcb988f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e00876eb-8f31-4b21-a0e7-3c3fcf508d66",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffd52f14-d510-4a99-9e78-592672bd761e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "24deb38c-c888-4d4c-aa32-77da0c8b83cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 344ms/step - accuracy: 0.1116 - loss: 2.0446 - val_accuracy: 0.3125 - val_loss: 2.0229\n",
      "Epoch 2/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 237ms/step - accuracy: 0.3981 - loss: 1.9369 - val_accuracy: 0.3456 - val_loss: 1.9689\n",
      "Epoch 3/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 248ms/step - accuracy: 0.5059 - loss: 1.8411 - val_accuracy: 0.3700 - val_loss: 1.9117\n",
      "Epoch 4/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 236ms/step - accuracy: 0.6474 - loss: 1.6814 - val_accuracy: 0.3738 - val_loss: 1.8498\n",
      "Epoch 5/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 234ms/step - accuracy: 0.6432 - loss: 1.5353 - val_accuracy: 0.3787 - val_loss: 1.7822\n",
      "Epoch 6/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 239ms/step - accuracy: 0.6501 - loss: 1.3644 - val_accuracy: 0.4344 - val_loss: 1.7125\n",
      "Epoch 7/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 248ms/step - accuracy: 0.7900 - loss: 1.1807 - val_accuracy: 0.4663 - val_loss: 1.6469\n",
      "Epoch 8/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 237ms/step - accuracy: 0.8459 - loss: 1.0331 - val_accuracy: 0.4794 - val_loss: 1.5856\n",
      "Epoch 9/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 241ms/step - accuracy: 0.8940 - loss: 0.8753 - val_accuracy: 0.5125 - val_loss: 1.5258\n",
      "Epoch 10/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 240ms/step - accuracy: 0.9984 - loss: 0.7077 - val_accuracy: 0.5481 - val_loss: 1.4574\n",
      "Epoch 11/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 240ms/step - accuracy: 0.9986 - loss: 0.5552 - val_accuracy: 0.5619 - val_loss: 1.4049\n",
      "Epoch 12/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 239ms/step - accuracy: 0.9983 - loss: 0.4270 - val_accuracy: 0.5725 - val_loss: 1.3666\n",
      "Epoch 13/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 244ms/step - accuracy: 0.9985 - loss: 0.3496 - val_accuracy: 0.5731 - val_loss: 1.3409\n",
      "Epoch 14/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 237ms/step - accuracy: 0.9986 - loss: 0.2682 - val_accuracy: 0.5731 - val_loss: 1.3241\n",
      "Epoch 15/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 238ms/step - accuracy: 0.9984 - loss: 0.2111 - val_accuracy: 0.5731 - val_loss: 1.3159\n",
      "Epoch 16/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 242ms/step - accuracy: 0.9986 - loss: 0.1604 - val_accuracy: 0.5725 - val_loss: 1.3136\n",
      "Epoch 17/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 238ms/step - accuracy: 0.9992 - loss: 0.1342 - val_accuracy: 0.5750 - val_loss: 1.3166\n",
      "Epoch 18/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 235ms/step - accuracy: 0.9985 - loss: 0.1057 - val_accuracy: 0.5750 - val_loss: 1.3228\n",
      "Epoch 19/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 238ms/step - accuracy: 0.9992 - loss: 0.0869 - val_accuracy: 0.5750 - val_loss: 1.3322\n",
      "Epoch 20/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 235ms/step - accuracy: 0.9987 - loss: 0.0731 - val_accuracy: 0.5781 - val_loss: 1.3441\n",
      "Epoch 21/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 236ms/step - accuracy: 0.9984 - loss: 0.0580 - val_accuracy: 0.5769 - val_loss: 1.3582\n",
      "Epoch 22/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 239ms/step - accuracy: 0.9986 - loss: 0.0487 - val_accuracy: 0.5756 - val_loss: 1.3738\n",
      "Epoch 23/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 237ms/step - accuracy: 0.9991 - loss: 0.0406 - val_accuracy: 0.5756 - val_loss: 1.3896\n",
      "Epoch 24/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 236ms/step - accuracy: 0.9985 - loss: 0.0366 - val_accuracy: 0.5738 - val_loss: 1.4067\n",
      "Epoch 25/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 235ms/step - accuracy: 0.9986 - loss: 0.0285 - val_accuracy: 0.5744 - val_loss: 1.4247\n",
      "Epoch 26/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 236ms/step - accuracy: 0.9981 - loss: 0.0272 - val_accuracy: 0.5750 - val_loss: 1.4448\n",
      "Epoch 27/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 237ms/step - accuracy: 0.9992 - loss: 0.0191 - val_accuracy: 0.5750 - val_loss: 1.4656\n",
      "Epoch 28/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 236ms/step - accuracy: 0.9992 - loss: 0.0166 - val_accuracy: 0.5750 - val_loss: 1.4866\n",
      "Epoch 29/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 236ms/step - accuracy: 0.9989 - loss: 0.0146 - val_accuracy: 0.5750 - val_loss: 1.5069\n",
      "Epoch 30/30\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 235ms/step - accuracy: 0.9994 - loss: 0.0110 - val_accuracy: 0.5769 - val_loss: 1.5276\n"
     ]
    }
   ],
   "source": [
    "input_layer = layers.Input((224, 224, 3), name='img_input')\n",
    "\n",
    "x_vgg = vgg_branch(input_layer)\n",
    "x_resnet = resnet_branch(input_layer)\n",
    "x_densenet = densenet_branch(input_layer)\n",
    "\n",
    "\n",
    "x = layers.Concatenate()([x_vgg, x_resnet, x_densenet])\n",
    "x = layers.Dense(16, activation='relu', name='ensemble_activation_dense16Relu')(x)\n",
    "out = layers.Dense(8, activation='softmax', name='ensemble_activation_dense8Soft')(x)\n",
    "\n",
    "ensemble = tensorflow.keras.Model(input_layer, out, name='ANN_ensemble')\n",
    "\n",
    "ensemble.compile(\n",
    "    optimizer=keras.optimizers.Adam(2e-4),\n",
    "    loss='categorical_crossentropy',\n",
    "    metrics=['accuracy']    \n",
    ")\n",
    "\n",
    "Ensemble_train_history = ensemble.fit(\n",
    "    train_data,\n",
    "    validation_data = test_data,\n",
    "    epochs=30\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "507143c7-850f-4d15-95ed-28f1b6d7d3e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "tensorflow.keras.models.save_model(ensemble, '/workspace/ensemble2.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1cd8d5a5-aa6a-40b1-a511-515bfeaab2e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 346ms/step - accuracy: 0.9992 - loss: 0.0097 - val_accuracy: 0.5800 - val_loss: 1.7229\n",
      "Epoch 2/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 232ms/step - accuracy: 0.9985 - loss: 0.0058 - val_accuracy: 0.5819 - val_loss: 1.7940\n",
      "Epoch 3/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 238ms/step - accuracy: 0.9990 - loss: 0.0059 - val_accuracy: 0.5831 - val_loss: 1.8475\n",
      "Epoch 4/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 234ms/step - accuracy: 0.9990 - loss: 0.0034 - val_accuracy: 0.5838 - val_loss: 1.8993\n",
      "Epoch 5/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 233ms/step - accuracy: 0.9992 - loss: 0.0035 - val_accuracy: 0.5856 - val_loss: 1.9220\n",
      "Epoch 6/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 235ms/step - accuracy: 0.9983 - loss: 0.0069 - val_accuracy: 0.5881 - val_loss: 1.9413\n",
      "Epoch 7/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 235ms/step - accuracy: 0.9987 - loss: 0.0046 - val_accuracy: 0.5875 - val_loss: 1.9738\n",
      "Epoch 8/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 234ms/step - accuracy: 0.9984 - loss: 0.0068 - val_accuracy: 0.5831 - val_loss: 1.9645\n",
      "Epoch 9/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 238ms/step - accuracy: 0.9989 - loss: 0.0032 - val_accuracy: 0.5869 - val_loss: 1.9819\n",
      "Epoch 10/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 236ms/step - accuracy: 0.9985 - loss: 0.0065 - val_accuracy: 0.5894 - val_loss: 2.0023\n",
      "Epoch 11/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 235ms/step - accuracy: 0.9991 - loss: 0.0039 - val_accuracy: 0.5900 - val_loss: 2.0331\n",
      "Epoch 12/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 233ms/step - accuracy: 0.9985 - loss: 0.0069 - val_accuracy: 0.5900 - val_loss: 2.0572\n",
      "Epoch 13/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 238ms/step - accuracy: 0.9988 - loss: 0.0062 - val_accuracy: 0.5863 - val_loss: 2.0580\n",
      "Epoch 14/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 237ms/step - accuracy: 0.9992 - loss: 0.0035 - val_accuracy: 0.5856 - val_loss: 2.0710\n",
      "Epoch 15/15\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 235ms/step - accuracy: 0.9987 - loss: 0.0043 - val_accuracy: 0.5881 - val_loss: 2.0779\n"
     ]
    }
   ],
   "source": [
    "\n",
    "ensemble.compile(\n",
    "    optimizer=keras.optimizers.Adam(1e-3),\n",
    "    loss='categorical_crossentropy',\n",
    "    metrics=['accuracy']    \n",
    ")\n",
    "\n",
    "Ensemble_train_history2 = ensemble.fit(\n",
    "    train_data,\n",
    "    validation_data = test_data,\n",
    "    epochs=15\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ed2219a2-7151-43a4-87e8-41d7ef3545c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "tensorflow.keras.models.save_model(ensemble, '/workspace/final_ensemble_model.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1c86c96-ac4d-4f9c-a45c-e723569102b8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "419e0426-900b-48bf-8ed2-b58db29378a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b964cb0-216c-4b4b-bd15-724c94607d42",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "2cafdcf3-1df8-4b50-8039-a702a7f10ba0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 103ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ensemble_preds = ensemble.predict(test_data)\n",
    "EnsembleModel_Predictions = np.argmax(ensemble_preds, axis=1)\n",
    "\n",
    "class_indices = test_data.class_indices\n",
    "\n",
    "index_to_class = {v:k for k,v in class_indices.items()}\n",
    "class_names = [index_to_class[i] for i in range(len(index_to_class))]\n",
    "\n",
    "# 3) Compute your confusion matrix\n",
    "cm = confusion_matrix(test_data.classes, EnsembleModel_Predictions)\n",
    "\n",
    "# 4) Plot with labels\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap='Blues', xticks_rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "aaa91969-78f9-4ff1-a776-6fb363b59f23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 121ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "denseNet_preds = denseNet_model.predict(test_data)\n",
    "denseNet_Predictions = np.argmax(denseNet_preds, axis=1)\n",
    "\n",
    "class_indices = test_data.class_indices\n",
    "\n",
    "index_to_class = {v:k for k,v in class_indices.items()}\n",
    "class_names = [index_to_class[i] for i in range(len(index_to_class))]\n",
    "\n",
    "# 3) Compute your confusion matrix\n",
    "cm = confusion_matrix(test_data.classes, denseNet_Predictions)\n",
    "\n",
    "# 4) Plot with labels\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap='Blues', xticks_rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "6a66852e-5105-4838-bf92-6e71696103e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 141ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resNet_preds = resNet_model.predict(test_data)\n",
    "resNet_Predictions = np.argmax(resNet_preds, axis=1)\n",
    "\n",
    "class_indices = test_data.class_indices\n",
    "\n",
    "index_to_class = {v:k for k,v in class_indices.items()}\n",
    "class_names = [index_to_class[i] for i in range(len(index_to_class))]\n",
    "\n",
    "# 3) Compute your confusion matrix\n",
    "cm = confusion_matrix(test_data.classes, resNet_Predictions)\n",
    "\n",
    "# 4) Plot with labels\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap='Blues', xticks_rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "727971d9-e2fb-4618-8a94-401628e241f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 135ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vgg_preds = vgg_model.predict(test_data)\n",
    "vgg_Predictions = np.argmax(vgg_preds, axis=1)\n",
    "\n",
    "class_indices = test_data.class_indices\n",
    "\n",
    "index_to_class = {v:k for k,v in class_indices.items()}\n",
    "class_names = [index_to_class[i] for i in range(len(index_to_class))]\n",
    "\n",
    "# 3) Compute your confusion matrix\n",
    "cm = confusion_matrix(test_data.classes, vgg_Predictions)\n",
    "\n",
    "# 4) Plot with labels\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap='Blues', xticks_rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "82491e4f-0ee9-4fb6-8f58-40cf34e1aa01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 199ms/step\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 132ms/step\n",
      "\u001b[1m317/317\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 127ms/step\n"
     ]
    }
   ],
   "source": [
    "vgg_features = vgg_branch.predict(train_data)\n",
    "\n",
    "resnet_features = resnet_branch.predict(train_data)\n",
    "\n",
    "densenet_features = densenet_branch.predict(train_data)\n",
    "\n",
    "concatenated_preds = np.concatenate([vgg_features, resnet_features, densenet_features], axis=1)\n",
    "y_train = train_data.classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "f3c08b3f-6ff2-475f-8459-78c344a744fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "vgg_features_test = vgg_branch.predict(test_data)\n",
    "resnet_features_test = resnet_branch.predict(test_data)\n",
    "densenet_features_test = densenet_branch.predict(test_data)\n",
    "\n",
    "concatenated_preds_test = np.concatenate([vgg_features_test, resnet_features_test, densenet_features_test], axis=1)\n",
    "y_test = test_data.classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "723558d7-cfe1-452c-a651-c46d40a567e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10129,)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "e1c848e8-f1d8-4f7d-88b6-111aaf180224",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 58.88%\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.42      0.55      0.48       200\n",
      "           1       0.58      0.66      0.62       200\n",
      "           2       0.53      0.38      0.44       200\n",
      "           3       0.55      0.54      0.54       200\n",
      "           4       0.90      0.81      0.86       200\n",
      "           5       0.81      0.89      0.85       200\n",
      "           6       0.47      0.47      0.47       200\n",
      "           7       0.47      0.42      0.45       200\n",
      "\n",
      "    accuracy                           0.59      1600\n",
      "   macro avg       0.59      0.59      0.59      1600\n",
      "weighted avg       0.59      0.59      0.59      1600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "rf = RandomForestClassifier(\n",
    "    n_estimators=500,            # more trees for stability in high‑D\n",
    "    max_features='sqrt',         # √10 000 ≃ 100 features tried at each split\n",
    "    max_depth=30,              # let trees grow until other stopping criteria stop them\n",
    "    min_samples_split=10,        # each node must have ≥10 samples to consider splitting\n",
    "    min_samples_leaf=5,             # estimate generalization error from out‑of‑bag samples\n",
    "    n_jobs=-1,                   # use all cores\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "rf = rf.fit(concatenated_preds, y_train)\n",
    "y_pred = rf.predict(concatenated_preds_test)\n",
    "\n",
    "# 4) Evaluate performance\n",
    "print(f\"Test Accuracy: {accuracy_score(y_test, y_pred):.2%}\")\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "e58d78bf-cc50-43ff-8245-4e9ddbbb3926",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4530    0.5300    0.4885       200\n",
      "           1     0.5794    0.6750    0.6236       200\n",
      "           2     0.5312    0.4250    0.4722       200\n",
      "           3     0.5872    0.5050    0.5430       200\n",
      "           4     0.8396    0.8900    0.8641       200\n",
      "           5     0.8656    0.8050    0.8342       200\n",
      "           6     0.4822    0.4750    0.4786       200\n",
      "           7     0.4466    0.4600    0.4532       200\n",
      "\n",
      "    accuracy                         0.5956      1600\n",
      "   macro avg     0.5981    0.5956    0.5947      1600\n",
      "weighted avg     0.5981    0.5956    0.5947      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4309    0.5300    0.4753       200\n",
      "           1     0.5823    0.6900    0.6316       200\n",
      "           2     0.5385    0.3850    0.4490       200\n",
      "           3     0.5598    0.5150    0.5365       200\n",
      "           4     0.8357    0.8900    0.8620       200\n",
      "           5     0.8656    0.8050    0.8342       200\n",
      "           6     0.4798    0.4750    0.4774       200\n",
      "           7     0.4508    0.4350    0.4427       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5929    0.5906    0.5886      1600\n",
      "weighted avg     0.5929    0.5906    0.5886      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4467    0.5450    0.4910       200\n",
      "           1     0.5678    0.6700    0.6147       200\n",
      "           2     0.5493    0.3900    0.4561       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8757    0.8100    0.8416       200\n",
      "           6     0.4794    0.4650    0.4721       200\n",
      "           7     0.4635    0.4450    0.4541       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5959    0.5938    0.5917      1600\n",
      "weighted avg     0.5959    0.5938    0.5917      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4430    0.5250    0.4805       200\n",
      "           1     0.5672    0.6750    0.6164       200\n",
      "           2     0.5556    0.4000    0.4651       200\n",
      "           3     0.5608    0.5300    0.5450       200\n",
      "           4     0.8372    0.9000    0.8675       200\n",
      "           5     0.8703    0.8050    0.8364       200\n",
      "           6     0.4750    0.4750    0.4750       200\n",
      "           7     0.4583    0.4400    0.4490       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5959    0.5938    0.5919      1600\n",
      "weighted avg     0.5959    0.5938    0.5919      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4321    0.5250    0.4740       200\n",
      "           1     0.5656    0.6900    0.6216       200\n",
      "           2     0.5532    0.3900    0.4575       200\n",
      "           3     0.5508    0.5150    0.5323       200\n",
      "           4     0.8364    0.8950    0.8647       200\n",
      "           5     0.8703    0.8050    0.8364       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4607    0.4400    0.4501       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5933    0.5906    0.5884      1600\n",
      "weighted avg     0.5933    0.5906    0.5884      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4467    0.5450    0.4910       200\n",
      "           1     0.5678    0.6700    0.6147       200\n",
      "           2     0.5493    0.3900    0.4561       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8757    0.8100    0.8416       200\n",
      "           6     0.4794    0.4650    0.4721       200\n",
      "           7     0.4635    0.4450    0.4541       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5959    0.5938    0.5917      1600\n",
      "weighted avg     0.5959    0.5938    0.5917      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4403    0.5350    0.4831       200\n",
      "           1     0.5798    0.6900    0.6301       200\n",
      "           2     0.5517    0.4000    0.4638       200\n",
      "           3     0.5838    0.5050    0.5416       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4774    0.4750    0.4762       200\n",
      "           7     0.4433    0.4500    0.4467       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5985    0.5950    0.5934      1600\n",
      "weighted avg     0.5985    0.5950    0.5934      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4280    0.5350    0.4756       200\n",
      "           1     0.5798    0.6900    0.6301       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5574    0.5100    0.5326       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4819    0.4650    0.4733       200\n",
      "           7     0.4490    0.4400    0.4444       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5942    0.5913    0.5892      1600\n",
      "weighted avg     0.5942    0.5913    0.5892      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4426    0.5400    0.4865       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5310    0.3850    0.4464       200\n",
      "           3     0.5482    0.5400    0.5441       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4734    0.4450    0.4588       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5954    0.5944    0.5920      1600\n",
      "weighted avg     0.5954    0.5944    0.5920      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4487    0.5250    0.4839       200\n",
      "           1     0.5837    0.6800    0.6282       200\n",
      "           2     0.5290    0.4100    0.4620       200\n",
      "           3     0.5771    0.5050    0.5387       200\n",
      "           4     0.8357    0.8900    0.8620       200\n",
      "           5     0.8656    0.8050    0.8342       200\n",
      "           6     0.4774    0.4750    0.4762       200\n",
      "           7     0.4488    0.4600    0.4543       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5958    0.5938    0.5924      1600\n",
      "weighted avg     0.5958    0.5938    0.5924      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4327    0.5300    0.4764       200\n",
      "           1     0.5805    0.6850    0.6284       200\n",
      "           2     0.5486    0.3950    0.4593       200\n",
      "           3     0.5635    0.5100    0.5354       200\n",
      "           4     0.8357    0.8900    0.8620       200\n",
      "           5     0.8656    0.8050    0.8342       200\n",
      "           6     0.4703    0.4750    0.4726       200\n",
      "           7     0.4560    0.4400    0.4478       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5941    0.5913    0.5895      1600\n",
      "weighted avg     0.5941    0.5913    0.5895      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4449    0.5450    0.4899       200\n",
      "           1     0.5702    0.6700    0.6161       200\n",
      "           2     0.5493    0.3900    0.4561       200\n",
      "           3     0.5393    0.5150    0.5269       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8757    0.8100    0.8416       200\n",
      "           6     0.4819    0.4650    0.4733       200\n",
      "           7     0.4615    0.4500    0.4557       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.5955    0.5931    0.5911      1600\n",
      "weighted avg     0.5955    0.5931    0.5911      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4468    0.5250    0.4828       200\n",
      "           1     0.5690    0.6800    0.6196       200\n",
      "           2     0.5616    0.4100    0.4740       200\n",
      "           3     0.5579    0.5300    0.5436       200\n",
      "           4     0.8372    0.9000    0.8675       200\n",
      "           5     0.8703    0.8050    0.8364       200\n",
      "           6     0.4700    0.4700    0.4700       200\n",
      "           7     0.4632    0.4400    0.4513       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5970    0.5950    0.5931      1600\n",
      "weighted avg     0.5970    0.5950    0.5931      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4303    0.5250    0.4730       200\n",
      "           1     0.5656    0.6900    0.6216       200\n",
      "           2     0.5401    0.3700    0.4392       200\n",
      "           3     0.5604    0.5100    0.5340       200\n",
      "           4     0.8364    0.8950    0.8647       200\n",
      "           5     0.8656    0.8050    0.8342       200\n",
      "           6     0.4650    0.4650    0.4650       200\n",
      "           7     0.4611    0.4450    0.4529       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5906    0.5881    0.5856      1600\n",
      "weighted avg     0.5906    0.5881    0.5856      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4449    0.5450    0.4899       200\n",
      "           1     0.5702    0.6700    0.6161       200\n",
      "           2     0.5493    0.3900    0.4561       200\n",
      "           3     0.5393    0.5150    0.5269       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8757    0.8100    0.8416       200\n",
      "           6     0.4819    0.4650    0.4733       200\n",
      "           7     0.4615    0.4500    0.4557       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.5955    0.5931    0.5911      1600\n",
      "weighted avg     0.5955    0.5931    0.5911      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4403    0.5350    0.4831       200\n",
      "           1     0.5798    0.6900    0.6301       200\n",
      "           2     0.5517    0.4000    0.4638       200\n",
      "           3     0.5838    0.5050    0.5416       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4774    0.4750    0.4762       200\n",
      "           7     0.4433    0.4500    0.4467       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5985    0.5950    0.5934      1600\n",
      "weighted avg     0.5985    0.5950    0.5934      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4280    0.5350    0.4756       200\n",
      "           1     0.5798    0.6900    0.6301       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5574    0.5100    0.5326       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4819    0.4650    0.4733       200\n",
      "           7     0.4490    0.4400    0.4444       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5942    0.5913    0.5892      1600\n",
      "weighted avg     0.5942    0.5913    0.5892      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4426    0.5400    0.4865       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5310    0.3850    0.4464       200\n",
      "           3     0.5482    0.5400    0.5441       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4734    0.4450    0.4588       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5954    0.5944    0.5920      1600\n",
      "weighted avg     0.5954    0.5944    0.5920      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4298    0.5200    0.4706       200\n",
      "           1     0.5679    0.6900    0.6230       200\n",
      "           2     0.5357    0.3750    0.4412       200\n",
      "           3     0.5503    0.5200    0.5347       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8723    0.8200    0.8454       200\n",
      "           6     0.4792    0.4600    0.4694       200\n",
      "           7     0.4541    0.4450    0.4495       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5921    0.5900    0.5878      1600\n",
      "weighted avg     0.5921    0.5900    0.5878      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4408    0.5400    0.4854       200\n",
      "           1     0.5720    0.6750    0.6193       200\n",
      "           2     0.5379    0.3900    0.4522       200\n",
      "           3     0.5683    0.5200    0.5431       200\n",
      "           4     0.8443    0.8950    0.8689       200\n",
      "           5     0.8717    0.8150    0.8424       200\n",
      "           6     0.4750    0.4750    0.4750       200\n",
      "           7     0.4583    0.4400    0.4490       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5961    0.5938    0.5919      1600\n",
      "weighted avg     0.5961    0.5938    0.5919      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4362    0.5300    0.4786       200\n",
      "           1     0.5744    0.6950    0.6290       200\n",
      "           2     0.5435    0.3750    0.4438       200\n",
      "           3     0.5608    0.5300    0.5450       200\n",
      "           4     0.8419    0.9050    0.8723       200\n",
      "           5     0.8804    0.8100    0.8438       200\n",
      "           6     0.4897    0.4750    0.4822       200\n",
      "           7     0.4667    0.4550    0.4608       200\n",
      "\n",
      "    accuracy                         0.5969      1600\n",
      "   macro avg     0.5992    0.5969    0.5944      1600\n",
      "weighted avg     0.5992    0.5969    0.5944      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4362    0.5300    0.4786       200\n",
      "           1     0.5620    0.6800    0.6154       200\n",
      "           2     0.5429    0.3800    0.4471       200\n",
      "           3     0.5622    0.5200    0.5403       200\n",
      "           4     0.8443    0.8950    0.8689       200\n",
      "           5     0.8717    0.8150    0.8424       200\n",
      "           6     0.4821    0.4700    0.4759       200\n",
      "           7     0.4541    0.4450    0.4495       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5944    0.5919    0.5898      1600\n",
      "weighted avg     0.5944    0.5919    0.5898      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4398    0.5300    0.4807       200\n",
      "           1     0.5615    0.6850    0.6171       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5487    0.5350    0.5418       200\n",
      "           4     0.8443    0.8950    0.8689       200\n",
      "           5     0.8717    0.8150    0.8424       200\n",
      "           6     0.4718    0.4600    0.4658       200\n",
      "           7     0.4703    0.4350    0.4519       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5943    0.5925    0.5900      1600\n",
      "weighted avg     0.5943    0.5925    0.5900      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4362    0.5300    0.4786       200\n",
      "           1     0.5744    0.6950    0.6290       200\n",
      "           2     0.5435    0.3750    0.4438       200\n",
      "           3     0.5608    0.5300    0.5450       200\n",
      "           4     0.8419    0.9050    0.8723       200\n",
      "           5     0.8804    0.8100    0.8438       200\n",
      "           6     0.4897    0.4750    0.4822       200\n",
      "           7     0.4667    0.4550    0.4608       200\n",
      "\n",
      "    accuracy                         0.5969      1600\n",
      "   macro avg     0.5992    0.5969    0.5944      1600\n",
      "weighted avg     0.5992    0.5969    0.5944      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4309    0.5300    0.4753       200\n",
      "           1     0.5732    0.6850    0.6241       200\n",
      "           2     0.5347    0.3850    0.4477       200\n",
      "           3     0.5659    0.5150    0.5393       200\n",
      "           4     0.8451    0.9000    0.8717       200\n",
      "           5     0.8811    0.8150    0.8468       200\n",
      "           6     0.4872    0.4750    0.4810       200\n",
      "           7     0.4541    0.4450    0.4495       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5965    0.5938    0.5919      1600\n",
      "weighted avg     0.5965    0.5938    0.5919      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4426    0.5400    0.4865       200\n",
      "           1     0.5667    0.6800    0.6182       200\n",
      "           2     0.5479    0.4000    0.4624       200\n",
      "           3     0.5668    0.5300    0.5478       200\n",
      "           4     0.8458    0.9050    0.8744       200\n",
      "           5     0.8859    0.8150    0.8490       200\n",
      "           6     0.4923    0.4800    0.4861       200\n",
      "           7     0.4684    0.4450    0.4564       200\n",
      "\n",
      "    accuracy                         0.5994      1600\n",
      "   macro avg     0.6021    0.5994    0.5976      1600\n",
      "weighted avg     0.6021    0.5994    0.5976      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4385    0.5350    0.4820       200\n",
      "           1     0.5620    0.6800    0.6154       200\n",
      "           2     0.5319    0.3750    0.4399       200\n",
      "           3     0.5677    0.5450    0.5561       200\n",
      "           4     0.8498    0.9050    0.8765       200\n",
      "           5     0.8865    0.8200    0.8519       200\n",
      "           6     0.4948    0.4750    0.4847       200\n",
      "           7     0.4764    0.4550    0.4655       200\n",
      "\n",
      "    accuracy                         0.5988      1600\n",
      "   macro avg     0.6010    0.5988    0.5965      1600\n",
      "weighted avg     0.6010    0.5988    0.5965      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4368    0.5700    0.4946       200\n",
      "           1     0.5622    0.7000    0.6236       200\n",
      "           2     0.5714    0.3400    0.4263       200\n",
      "           3     0.5312    0.5100    0.5204       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4703    0.4750    0.4726       200\n",
      "           7     0.4663    0.4150    0.4392       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5939    0.5894    0.5855      1600\n",
      "weighted avg     0.5939    0.5894    0.5855      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4444    0.5800    0.5033       200\n",
      "           1     0.5560    0.6950    0.6178       200\n",
      "           2     0.5630    0.3350    0.4201       200\n",
      "           3     0.5357    0.5250    0.5303       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4650    0.4650    0.4650       200\n",
      "           7     0.4914    0.4300    0.4587       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5961    0.5919    0.5878      1600\n",
      "weighted avg     0.5961    0.5919    0.5878      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4392    0.5600    0.4923       200\n",
      "           1     0.5508    0.7050    0.6184       200\n",
      "           2     0.5690    0.3300    0.4177       200\n",
      "           3     0.5323    0.5350    0.5337       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4747    0.4700    0.4724       200\n",
      "           7     0.4857    0.4250    0.4533       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5956    0.5913    0.5869      1600\n",
      "weighted avg     0.5956    0.5913    0.5869      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4449    0.5850    0.5054       200\n",
      "           1     0.5628    0.6950    0.6219       200\n",
      "           2     0.5667    0.3400    0.4250       200\n",
      "           3     0.5377    0.5350    0.5363       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4650    0.4650    0.4650       200\n",
      "           7     0.5000    0.4300    0.4624       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5988    0.5944    0.5904      1600\n",
      "weighted avg     0.5988    0.5944    0.5904      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4411    0.5800    0.5011       200\n",
      "           1     0.5600    0.7000    0.6222       200\n",
      "           2     0.5678    0.3350    0.4214       200\n",
      "           3     0.5408    0.5300    0.5354       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4650    0.4650    0.4650       200\n",
      "           7     0.4885    0.4250    0.4545       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5970    0.5925    0.5884      1600\n",
      "weighted avg     0.5970    0.5925    0.5884      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4392    0.5600    0.4923       200\n",
      "           1     0.5508    0.7050    0.6184       200\n",
      "           2     0.5690    0.3300    0.4177       200\n",
      "           3     0.5323    0.5350    0.5337       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4747    0.4700    0.4724       200\n",
      "           7     0.4857    0.4250    0.4533       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5956    0.5913    0.5869      1600\n",
      "weighted avg     0.5956    0.5913    0.5869      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4406    0.5750    0.4989       200\n",
      "           1     0.5556    0.7000    0.6195       200\n",
      "           2     0.5593    0.3300    0.4151       200\n",
      "           3     0.5350    0.5350    0.5350       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4697    0.4650    0.4673       200\n",
      "           7     0.4942    0.4250    0.4570       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5959    0.5919    0.5875      1600\n",
      "weighted avg     0.5959    0.5919    0.5875      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4406    0.5750    0.4989       200\n",
      "           1     0.5578    0.7000    0.6208       200\n",
      "           2     0.5593    0.3300    0.4151       200\n",
      "           3     0.5327    0.5300    0.5313       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4697    0.4650    0.4673       200\n",
      "           7     0.4828    0.4200    0.4492       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5945    0.5906    0.5863      1600\n",
      "weighted avg     0.5945    0.5906    0.5863      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4392    0.5600    0.4923       200\n",
      "           1     0.5508    0.7050    0.6184       200\n",
      "           2     0.5690    0.3300    0.4177       200\n",
      "           3     0.5297    0.5350    0.5323       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4747    0.4700    0.4724       200\n",
      "           7     0.4885    0.4250    0.4545       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5956    0.5913    0.5869      1600\n",
      "weighted avg     0.5956    0.5913    0.5869      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4530    0.5300    0.4885       200\n",
      "           1     0.5794    0.6750    0.6236       200\n",
      "           2     0.5312    0.4250    0.4722       200\n",
      "           3     0.5872    0.5050    0.5430       200\n",
      "           4     0.8396    0.8900    0.8641       200\n",
      "           5     0.8656    0.8050    0.8342       200\n",
      "           6     0.4822    0.4750    0.4786       200\n",
      "           7     0.4466    0.4600    0.4532       200\n",
      "\n",
      "    accuracy                         0.5956      1600\n",
      "   macro avg     0.5981    0.5956    0.5947      1600\n",
      "weighted avg     0.5981    0.5956    0.5947      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4309    0.5300    0.4753       200\n",
      "           1     0.5823    0.6900    0.6316       200\n",
      "           2     0.5385    0.3850    0.4490       200\n",
      "           3     0.5598    0.5150    0.5365       200\n",
      "           4     0.8357    0.8900    0.8620       200\n",
      "           5     0.8656    0.8050    0.8342       200\n",
      "           6     0.4798    0.4750    0.4774       200\n",
      "           7     0.4508    0.4350    0.4427       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5929    0.5906    0.5886      1600\n",
      "weighted avg     0.5929    0.5906    0.5886      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4467    0.5450    0.4910       200\n",
      "           1     0.5678    0.6700    0.6147       200\n",
      "           2     0.5493    0.3900    0.4561       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8757    0.8100    0.8416       200\n",
      "           6     0.4794    0.4650    0.4721       200\n",
      "           7     0.4635    0.4450    0.4541       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5959    0.5938    0.5917      1600\n",
      "weighted avg     0.5959    0.5938    0.5917      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4430    0.5250    0.4805       200\n",
      "           1     0.5672    0.6750    0.6164       200\n",
      "           2     0.5556    0.4000    0.4651       200\n",
      "           3     0.5608    0.5300    0.5450       200\n",
      "           4     0.8372    0.9000    0.8675       200\n",
      "           5     0.8703    0.8050    0.8364       200\n",
      "           6     0.4750    0.4750    0.4750       200\n",
      "           7     0.4583    0.4400    0.4490       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5959    0.5938    0.5919      1600\n",
      "weighted avg     0.5959    0.5938    0.5919      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4321    0.5250    0.4740       200\n",
      "           1     0.5656    0.6900    0.6216       200\n",
      "           2     0.5532    0.3900    0.4575       200\n",
      "           3     0.5508    0.5150    0.5323       200\n",
      "           4     0.8364    0.8950    0.8647       200\n",
      "           5     0.8703    0.8050    0.8364       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4607    0.4400    0.4501       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5933    0.5906    0.5884      1600\n",
      "weighted avg     0.5933    0.5906    0.5884      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4467    0.5450    0.4910       200\n",
      "           1     0.5678    0.6700    0.6147       200\n",
      "           2     0.5493    0.3900    0.4561       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8757    0.8100    0.8416       200\n",
      "           6     0.4794    0.4650    0.4721       200\n",
      "           7     0.4635    0.4450    0.4541       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5959    0.5938    0.5917      1600\n",
      "weighted avg     0.5959    0.5938    0.5917      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4403    0.5350    0.4831       200\n",
      "           1     0.5798    0.6900    0.6301       200\n",
      "           2     0.5517    0.4000    0.4638       200\n",
      "           3     0.5838    0.5050    0.5416       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4774    0.4750    0.4762       200\n",
      "           7     0.4433    0.4500    0.4467       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5985    0.5950    0.5934      1600\n",
      "weighted avg     0.5985    0.5950    0.5934      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4280    0.5350    0.4756       200\n",
      "           1     0.5798    0.6900    0.6301       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5574    0.5100    0.5326       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4819    0.4650    0.4733       200\n",
      "           7     0.4490    0.4400    0.4444       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5942    0.5913    0.5892      1600\n",
      "weighted avg     0.5942    0.5913    0.5892      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=sqrt, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4426    0.5400    0.4865       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5310    0.3850    0.4464       200\n",
      "           3     0.5482    0.5400    0.5441       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4734    0.4450    0.4588       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5954    0.5944    0.5920      1600\n",
      "weighted avg     0.5954    0.5944    0.5920      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4530    0.5300    0.4885       200\n",
      "           1     0.5794    0.6750    0.6236       200\n",
      "           2     0.5312    0.4250    0.4722       200\n",
      "           3     0.5872    0.5050    0.5430       200\n",
      "           4     0.8396    0.8900    0.8641       200\n",
      "           5     0.8656    0.8050    0.8342       200\n",
      "           6     0.4822    0.4750    0.4786       200\n",
      "           7     0.4466    0.4600    0.4532       200\n",
      "\n",
      "    accuracy                         0.5956      1600\n",
      "   macro avg     0.5981    0.5956    0.5947      1600\n",
      "weighted avg     0.5981    0.5956    0.5947      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4309    0.5300    0.4753       200\n",
      "           1     0.5823    0.6900    0.6316       200\n",
      "           2     0.5385    0.3850    0.4490       200\n",
      "           3     0.5598    0.5150    0.5365       200\n",
      "           4     0.8357    0.8900    0.8620       200\n",
      "           5     0.8656    0.8050    0.8342       200\n",
      "           6     0.4798    0.4750    0.4774       200\n",
      "           7     0.4508    0.4350    0.4427       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5929    0.5906    0.5886      1600\n",
      "weighted avg     0.5929    0.5906    0.5886      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4467    0.5450    0.4910       200\n",
      "           1     0.5678    0.6700    0.6147       200\n",
      "           2     0.5493    0.3900    0.4561       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8757    0.8100    0.8416       200\n",
      "           6     0.4794    0.4650    0.4721       200\n",
      "           7     0.4635    0.4450    0.4541       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5959    0.5938    0.5917      1600\n",
      "weighted avg     0.5959    0.5938    0.5917      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4430    0.5250    0.4805       200\n",
      "           1     0.5672    0.6750    0.6164       200\n",
      "           2     0.5556    0.4000    0.4651       200\n",
      "           3     0.5608    0.5300    0.5450       200\n",
      "           4     0.8372    0.9000    0.8675       200\n",
      "           5     0.8703    0.8050    0.8364       200\n",
      "           6     0.4750    0.4750    0.4750       200\n",
      "           7     0.4583    0.4400    0.4490       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5959    0.5938    0.5919      1600\n",
      "weighted avg     0.5959    0.5938    0.5919      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4321    0.5250    0.4740       200\n",
      "           1     0.5656    0.6900    0.6216       200\n",
      "           2     0.5532    0.3900    0.4575       200\n",
      "           3     0.5508    0.5150    0.5323       200\n",
      "           4     0.8364    0.8950    0.8647       200\n",
      "           5     0.8703    0.8050    0.8364       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4607    0.4400    0.4501       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5933    0.5906    0.5884      1600\n",
      "weighted avg     0.5933    0.5906    0.5884      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4467    0.5450    0.4910       200\n",
      "           1     0.5678    0.6700    0.6147       200\n",
      "           2     0.5493    0.3900    0.4561       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8757    0.8100    0.8416       200\n",
      "           6     0.4794    0.4650    0.4721       200\n",
      "           7     0.4635    0.4450    0.4541       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5959    0.5938    0.5917      1600\n",
      "weighted avg     0.5959    0.5938    0.5917      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4403    0.5350    0.4831       200\n",
      "           1     0.5798    0.6900    0.6301       200\n",
      "           2     0.5517    0.4000    0.4638       200\n",
      "           3     0.5838    0.5050    0.5416       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4774    0.4750    0.4762       200\n",
      "           7     0.4433    0.4500    0.4467       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5985    0.5950    0.5934      1600\n",
      "weighted avg     0.5985    0.5950    0.5934      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4280    0.5350    0.4756       200\n",
      "           1     0.5798    0.6900    0.6301       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5574    0.5100    0.5326       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4819    0.4650    0.4733       200\n",
      "           7     0.4490    0.4400    0.4444       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5942    0.5913    0.5892      1600\n",
      "weighted avg     0.5942    0.5913    0.5892      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4426    0.5400    0.4865       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5310    0.3850    0.4464       200\n",
      "           3     0.5482    0.5400    0.5441       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4734    0.4450    0.4588       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5954    0.5944    0.5920      1600\n",
      "weighted avg     0.5954    0.5944    0.5920      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4487    0.5250    0.4839       200\n",
      "           1     0.5837    0.6800    0.6282       200\n",
      "           2     0.5290    0.4100    0.4620       200\n",
      "           3     0.5771    0.5050    0.5387       200\n",
      "           4     0.8357    0.8900    0.8620       200\n",
      "           5     0.8656    0.8050    0.8342       200\n",
      "           6     0.4774    0.4750    0.4762       200\n",
      "           7     0.4488    0.4600    0.4543       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5958    0.5938    0.5924      1600\n",
      "weighted avg     0.5958    0.5938    0.5924      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4327    0.5300    0.4764       200\n",
      "           1     0.5805    0.6850    0.6284       200\n",
      "           2     0.5486    0.3950    0.4593       200\n",
      "           3     0.5635    0.5100    0.5354       200\n",
      "           4     0.8357    0.8900    0.8620       200\n",
      "           5     0.8656    0.8050    0.8342       200\n",
      "           6     0.4703    0.4750    0.4726       200\n",
      "           7     0.4560    0.4400    0.4478       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5941    0.5913    0.5895      1600\n",
      "weighted avg     0.5941    0.5913    0.5895      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4449    0.5450    0.4899       200\n",
      "           1     0.5702    0.6700    0.6161       200\n",
      "           2     0.5493    0.3900    0.4561       200\n",
      "           3     0.5393    0.5150    0.5269       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8757    0.8100    0.8416       200\n",
      "           6     0.4819    0.4650    0.4733       200\n",
      "           7     0.4615    0.4500    0.4557       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.5955    0.5931    0.5911      1600\n",
      "weighted avg     0.5955    0.5931    0.5911      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4468    0.5250    0.4828       200\n",
      "           1     0.5690    0.6800    0.6196       200\n",
      "           2     0.5616    0.4100    0.4740       200\n",
      "           3     0.5579    0.5300    0.5436       200\n",
      "           4     0.8372    0.9000    0.8675       200\n",
      "           5     0.8703    0.8050    0.8364       200\n",
      "           6     0.4700    0.4700    0.4700       200\n",
      "           7     0.4632    0.4400    0.4513       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5970    0.5950    0.5931      1600\n",
      "weighted avg     0.5970    0.5950    0.5931      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4303    0.5250    0.4730       200\n",
      "           1     0.5656    0.6900    0.6216       200\n",
      "           2     0.5401    0.3700    0.4392       200\n",
      "           3     0.5604    0.5100    0.5340       200\n",
      "           4     0.8364    0.8950    0.8647       200\n",
      "           5     0.8656    0.8050    0.8342       200\n",
      "           6     0.4650    0.4650    0.4650       200\n",
      "           7     0.4611    0.4450    0.4529       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5906    0.5881    0.5856      1600\n",
      "weighted avg     0.5906    0.5881    0.5856      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4449    0.5450    0.4899       200\n",
      "           1     0.5702    0.6700    0.6161       200\n",
      "           2     0.5493    0.3900    0.4561       200\n",
      "           3     0.5393    0.5150    0.5269       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8757    0.8100    0.8416       200\n",
      "           6     0.4819    0.4650    0.4733       200\n",
      "           7     0.4615    0.4500    0.4557       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.5955    0.5931    0.5911      1600\n",
      "weighted avg     0.5955    0.5931    0.5911      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4403    0.5350    0.4831       200\n",
      "           1     0.5798    0.6900    0.6301       200\n",
      "           2     0.5517    0.4000    0.4638       200\n",
      "           3     0.5838    0.5050    0.5416       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4774    0.4750    0.4762       200\n",
      "           7     0.4433    0.4500    0.4467       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5985    0.5950    0.5934      1600\n",
      "weighted avg     0.5985    0.5950    0.5934      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4280    0.5350    0.4756       200\n",
      "           1     0.5798    0.6900    0.6301       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5574    0.5100    0.5326       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4819    0.4650    0.4733       200\n",
      "           7     0.4490    0.4400    0.4444       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5942    0.5913    0.5892      1600\n",
      "weighted avg     0.5942    0.5913    0.5892      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4426    0.5400    0.4865       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5310    0.3850    0.4464       200\n",
      "           3     0.5482    0.5400    0.5441       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4734    0.4450    0.4588       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5954    0.5944    0.5920      1600\n",
      "weighted avg     0.5954    0.5944    0.5920      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4298    0.5200    0.4706       200\n",
      "           1     0.5679    0.6900    0.6230       200\n",
      "           2     0.5357    0.3750    0.4412       200\n",
      "           3     0.5503    0.5200    0.5347       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8723    0.8200    0.8454       200\n",
      "           6     0.4792    0.4600    0.4694       200\n",
      "           7     0.4541    0.4450    0.4495       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5921    0.5900    0.5878      1600\n",
      "weighted avg     0.5921    0.5900    0.5878      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4408    0.5400    0.4854       200\n",
      "           1     0.5720    0.6750    0.6193       200\n",
      "           2     0.5379    0.3900    0.4522       200\n",
      "           3     0.5683    0.5200    0.5431       200\n",
      "           4     0.8443    0.8950    0.8689       200\n",
      "           5     0.8717    0.8150    0.8424       200\n",
      "           6     0.4750    0.4750    0.4750       200\n",
      "           7     0.4583    0.4400    0.4490       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5961    0.5938    0.5919      1600\n",
      "weighted avg     0.5961    0.5938    0.5919      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4362    0.5300    0.4786       200\n",
      "           1     0.5744    0.6950    0.6290       200\n",
      "           2     0.5435    0.3750    0.4438       200\n",
      "           3     0.5608    0.5300    0.5450       200\n",
      "           4     0.8419    0.9050    0.8723       200\n",
      "           5     0.8804    0.8100    0.8438       200\n",
      "           6     0.4897    0.4750    0.4822       200\n",
      "           7     0.4667    0.4550    0.4608       200\n",
      "\n",
      "    accuracy                         0.5969      1600\n",
      "   macro avg     0.5992    0.5969    0.5944      1600\n",
      "weighted avg     0.5992    0.5969    0.5944      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4362    0.5300    0.4786       200\n",
      "           1     0.5620    0.6800    0.6154       200\n",
      "           2     0.5429    0.3800    0.4471       200\n",
      "           3     0.5622    0.5200    0.5403       200\n",
      "           4     0.8443    0.8950    0.8689       200\n",
      "           5     0.8717    0.8150    0.8424       200\n",
      "           6     0.4821    0.4700    0.4759       200\n",
      "           7     0.4541    0.4450    0.4495       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5944    0.5919    0.5898      1600\n",
      "weighted avg     0.5944    0.5919    0.5898      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4398    0.5300    0.4807       200\n",
      "           1     0.5615    0.6850    0.6171       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5487    0.5350    0.5418       200\n",
      "           4     0.8443    0.8950    0.8689       200\n",
      "           5     0.8717    0.8150    0.8424       200\n",
      "           6     0.4718    0.4600    0.4658       200\n",
      "           7     0.4703    0.4350    0.4519       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5943    0.5925    0.5900      1600\n",
      "weighted avg     0.5943    0.5925    0.5900      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4362    0.5300    0.4786       200\n",
      "           1     0.5744    0.6950    0.6290       200\n",
      "           2     0.5435    0.3750    0.4438       200\n",
      "           3     0.5608    0.5300    0.5450       200\n",
      "           4     0.8419    0.9050    0.8723       200\n",
      "           5     0.8804    0.8100    0.8438       200\n",
      "           6     0.4897    0.4750    0.4822       200\n",
      "           7     0.4667    0.4550    0.4608       200\n",
      "\n",
      "    accuracy                         0.5969      1600\n",
      "   macro avg     0.5992    0.5969    0.5944      1600\n",
      "weighted avg     0.5992    0.5969    0.5944      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4309    0.5300    0.4753       200\n",
      "           1     0.5732    0.6850    0.6241       200\n",
      "           2     0.5347    0.3850    0.4477       200\n",
      "           3     0.5659    0.5150    0.5393       200\n",
      "           4     0.8451    0.9000    0.8717       200\n",
      "           5     0.8811    0.8150    0.8468       200\n",
      "           6     0.4872    0.4750    0.4810       200\n",
      "           7     0.4541    0.4450    0.4495       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5965    0.5938    0.5919      1600\n",
      "weighted avg     0.5965    0.5938    0.5919      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4426    0.5400    0.4865       200\n",
      "           1     0.5667    0.6800    0.6182       200\n",
      "           2     0.5479    0.4000    0.4624       200\n",
      "           3     0.5668    0.5300    0.5478       200\n",
      "           4     0.8458    0.9050    0.8744       200\n",
      "           5     0.8859    0.8150    0.8490       200\n",
      "           6     0.4923    0.4800    0.4861       200\n",
      "           7     0.4684    0.4450    0.4564       200\n",
      "\n",
      "    accuracy                         0.5994      1600\n",
      "   macro avg     0.6021    0.5994    0.5976      1600\n",
      "weighted avg     0.6021    0.5994    0.5976      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4385    0.5350    0.4820       200\n",
      "           1     0.5620    0.6800    0.6154       200\n",
      "           2     0.5319    0.3750    0.4399       200\n",
      "           3     0.5677    0.5450    0.5561       200\n",
      "           4     0.8498    0.9050    0.8765       200\n",
      "           5     0.8865    0.8200    0.8519       200\n",
      "           6     0.4948    0.4750    0.4847       200\n",
      "           7     0.4764    0.4550    0.4655       200\n",
      "\n",
      "    accuracy                         0.5988      1600\n",
      "   macro avg     0.6010    0.5988    0.5965      1600\n",
      "weighted avg     0.6010    0.5988    0.5965      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4368    0.5700    0.4946       200\n",
      "           1     0.5622    0.7000    0.6236       200\n",
      "           2     0.5714    0.3400    0.4263       200\n",
      "           3     0.5312    0.5100    0.5204       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4703    0.4750    0.4726       200\n",
      "           7     0.4663    0.4150    0.4392       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5939    0.5894    0.5855      1600\n",
      "weighted avg     0.5939    0.5894    0.5855      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4444    0.5800    0.5033       200\n",
      "           1     0.5560    0.6950    0.6178       200\n",
      "           2     0.5630    0.3350    0.4201       200\n",
      "           3     0.5357    0.5250    0.5303       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4650    0.4650    0.4650       200\n",
      "           7     0.4914    0.4300    0.4587       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5961    0.5919    0.5878      1600\n",
      "weighted avg     0.5961    0.5919    0.5878      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4392    0.5600    0.4923       200\n",
      "           1     0.5508    0.7050    0.6184       200\n",
      "           2     0.5690    0.3300    0.4177       200\n",
      "           3     0.5323    0.5350    0.5337       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4747    0.4700    0.4724       200\n",
      "           7     0.4857    0.4250    0.4533       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5956    0.5913    0.5869      1600\n",
      "weighted avg     0.5956    0.5913    0.5869      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4449    0.5850    0.5054       200\n",
      "           1     0.5628    0.6950    0.6219       200\n",
      "           2     0.5667    0.3400    0.4250       200\n",
      "           3     0.5377    0.5350    0.5363       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4650    0.4650    0.4650       200\n",
      "           7     0.5000    0.4300    0.4624       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5988    0.5944    0.5904      1600\n",
      "weighted avg     0.5988    0.5944    0.5904      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4411    0.5800    0.5011       200\n",
      "           1     0.5600    0.7000    0.6222       200\n",
      "           2     0.5678    0.3350    0.4214       200\n",
      "           3     0.5408    0.5300    0.5354       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4650    0.4650    0.4650       200\n",
      "           7     0.4885    0.4250    0.4545       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5970    0.5925    0.5884      1600\n",
      "weighted avg     0.5970    0.5925    0.5884      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4392    0.5600    0.4923       200\n",
      "           1     0.5508    0.7050    0.6184       200\n",
      "           2     0.5690    0.3300    0.4177       200\n",
      "           3     0.5323    0.5350    0.5337       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4747    0.4700    0.4724       200\n",
      "           7     0.4857    0.4250    0.4533       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5956    0.5913    0.5869      1600\n",
      "weighted avg     0.5956    0.5913    0.5869      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4406    0.5750    0.4989       200\n",
      "           1     0.5556    0.7000    0.6195       200\n",
      "           2     0.5593    0.3300    0.4151       200\n",
      "           3     0.5350    0.5350    0.5350       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4697    0.4650    0.4673       200\n",
      "           7     0.4942    0.4250    0.4570       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5959    0.5919    0.5875      1600\n",
      "weighted avg     0.5959    0.5919    0.5875      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4406    0.5750    0.4989       200\n",
      "           1     0.5578    0.7000    0.6208       200\n",
      "           2     0.5593    0.3300    0.4151       200\n",
      "           3     0.5327    0.5300    0.5313       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4697    0.4650    0.4673       200\n",
      "           7     0.4828    0.4200    0.4492       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5945    0.5906    0.5863      1600\n",
      "weighted avg     0.5945    0.5906    0.5863      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4392    0.5600    0.4923       200\n",
      "           1     0.5508    0.7050    0.6184       200\n",
      "           2     0.5690    0.3300    0.4177       200\n",
      "           3     0.5297    0.5350    0.5323       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4747    0.4700    0.4724       200\n",
      "           7     0.4885    0.4250    0.4545       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5956    0.5913    0.5869      1600\n",
      "weighted avg     0.5956    0.5913    0.5869      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4530    0.5300    0.4885       200\n",
      "           1     0.5794    0.6750    0.6236       200\n",
      "           2     0.5312    0.4250    0.4722       200\n",
      "           3     0.5872    0.5050    0.5430       200\n",
      "           4     0.8396    0.8900    0.8641       200\n",
      "           5     0.8656    0.8050    0.8342       200\n",
      "           6     0.4822    0.4750    0.4786       200\n",
      "           7     0.4466    0.4600    0.4532       200\n",
      "\n",
      "    accuracy                         0.5956      1600\n",
      "   macro avg     0.5981    0.5956    0.5947      1600\n",
      "weighted avg     0.5981    0.5956    0.5947      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4309    0.5300    0.4753       200\n",
      "           1     0.5823    0.6900    0.6316       200\n",
      "           2     0.5385    0.3850    0.4490       200\n",
      "           3     0.5598    0.5150    0.5365       200\n",
      "           4     0.8357    0.8900    0.8620       200\n",
      "           5     0.8656    0.8050    0.8342       200\n",
      "           6     0.4798    0.4750    0.4774       200\n",
      "           7     0.4508    0.4350    0.4427       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5929    0.5906    0.5886      1600\n",
      "weighted avg     0.5929    0.5906    0.5886      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4467    0.5450    0.4910       200\n",
      "           1     0.5678    0.6700    0.6147       200\n",
      "           2     0.5493    0.3900    0.4561       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8757    0.8100    0.8416       200\n",
      "           6     0.4794    0.4650    0.4721       200\n",
      "           7     0.4635    0.4450    0.4541       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5959    0.5938    0.5917      1600\n",
      "weighted avg     0.5959    0.5938    0.5917      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4430    0.5250    0.4805       200\n",
      "           1     0.5672    0.6750    0.6164       200\n",
      "           2     0.5556    0.4000    0.4651       200\n",
      "           3     0.5608    0.5300    0.5450       200\n",
      "           4     0.8372    0.9000    0.8675       200\n",
      "           5     0.8703    0.8050    0.8364       200\n",
      "           6     0.4750    0.4750    0.4750       200\n",
      "           7     0.4583    0.4400    0.4490       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5959    0.5938    0.5919      1600\n",
      "weighted avg     0.5959    0.5938    0.5919      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4321    0.5250    0.4740       200\n",
      "           1     0.5656    0.6900    0.6216       200\n",
      "           2     0.5532    0.3900    0.4575       200\n",
      "           3     0.5508    0.5150    0.5323       200\n",
      "           4     0.8364    0.8950    0.8647       200\n",
      "           5     0.8703    0.8050    0.8364       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4607    0.4400    0.4501       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5933    0.5906    0.5884      1600\n",
      "weighted avg     0.5933    0.5906    0.5884      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4467    0.5450    0.4910       200\n",
      "           1     0.5678    0.6700    0.6147       200\n",
      "           2     0.5493    0.3900    0.4561       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8757    0.8100    0.8416       200\n",
      "           6     0.4794    0.4650    0.4721       200\n",
      "           7     0.4635    0.4450    0.4541       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5959    0.5938    0.5917      1600\n",
      "weighted avg     0.5959    0.5938    0.5917      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4403    0.5350    0.4831       200\n",
      "           1     0.5798    0.6900    0.6301       200\n",
      "           2     0.5517    0.4000    0.4638       200\n",
      "           3     0.5838    0.5050    0.5416       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4774    0.4750    0.4762       200\n",
      "           7     0.4433    0.4500    0.4467       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5985    0.5950    0.5934      1600\n",
      "weighted avg     0.5985    0.5950    0.5934      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4280    0.5350    0.4756       200\n",
      "           1     0.5798    0.6900    0.6301       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5574    0.5100    0.5326       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4819    0.4650    0.4733       200\n",
      "           7     0.4490    0.4400    0.4444       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5942    0.5913    0.5892      1600\n",
      "weighted avg     0.5942    0.5913    0.5892      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=log2, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4426    0.5400    0.4865       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5310    0.3850    0.4464       200\n",
      "           3     0.5482    0.5400    0.5441       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4734    0.4450    0.4588       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5954    0.5944    0.5920      1600\n",
      "weighted avg     0.5954    0.5944    0.5920      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 57.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4235    0.5400    0.4747       200\n",
      "           1     0.5500    0.6050    0.5762       200\n",
      "           2     0.5252    0.3650    0.4307       200\n",
      "           3     0.5731    0.4900    0.5283       200\n",
      "           4     0.9302    0.8000    0.8602       200\n",
      "           5     0.8206    0.9150    0.8652       200\n",
      "           6     0.4238    0.4450    0.4341       200\n",
      "           7     0.4286    0.4500    0.4390       200\n",
      "\n",
      "    accuracy                         0.5763      1600\n",
      "   macro avg     0.5844    0.5763    0.5761      1600\n",
      "weighted avg     0.5844    0.5763    0.5761      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 57.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4258    0.5450    0.4781       200\n",
      "           1     0.5611    0.6200    0.5891       200\n",
      "           2     0.5172    0.3750    0.4348       200\n",
      "           3     0.5455    0.5100    0.5271       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4171    0.4400    0.4282       200\n",
      "           7     0.4378    0.4050    0.4208       200\n",
      "\n",
      "    accuracy                         0.5756      1600\n",
      "   macro avg     0.5814    0.5756    0.5748      1600\n",
      "weighted avg     0.5814    0.5756    0.5748      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4268    0.5250    0.4709       200\n",
      "           1     0.5721    0.6150    0.5928       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5297    0.4900    0.5091       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4110    0.4500    0.4296       200\n",
      "           7     0.4433    0.4300    0.4365       200\n",
      "\n",
      "    accuracy                         0.5744      1600\n",
      "   macro avg     0.5804    0.5744    0.5741      1600\n",
      "weighted avg     0.5804    0.5744    0.5741      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 57.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4327    0.5300    0.4764       200\n",
      "           1     0.5525    0.6050    0.5776       200\n",
      "           2     0.5248    0.3700    0.4340       200\n",
      "           3     0.5319    0.5000    0.5155       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4099    0.4550    0.4313       200\n",
      "           7     0.4368    0.4150    0.4256       200\n",
      "\n",
      "    accuracy                         0.5731      1600\n",
      "   macro avg     0.5794    0.5731    0.5726      1600\n",
      "weighted avg     0.5794    0.5731    0.5726      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4332    0.5350    0.4787       200\n",
      "           1     0.5674    0.6100    0.5880       200\n",
      "           2     0.5214    0.3650    0.4294       200\n",
      "           3     0.5323    0.4950    0.5130       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4099    0.4550    0.4313       200\n",
      "           7     0.4410    0.4300    0.4354       200\n",
      "\n",
      "    accuracy                         0.5750      1600\n",
      "   macro avg     0.5815    0.5750    0.5745      1600\n",
      "weighted avg     0.5815    0.5750    0.5745      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4268    0.5250    0.4709       200\n",
      "           1     0.5721    0.6150    0.5928       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5297    0.4900    0.5091       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4110    0.4500    0.4296       200\n",
      "           7     0.4433    0.4300    0.4365       200\n",
      "\n",
      "    accuracy                         0.5744      1600\n",
      "   macro avg     0.5804    0.5744    0.5741      1600\n",
      "weighted avg     0.5804    0.5744    0.5741      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 57.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4303    0.5250    0.4730       200\n",
      "           1     0.5602    0.6050    0.5817       200\n",
      "           2     0.5205    0.3800    0.4393       200\n",
      "           3     0.5435    0.5000    0.5208       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4144    0.4600    0.4360       200\n",
      "           7     0.4352    0.4200    0.4275       200\n",
      "\n",
      "    accuracy                         0.5750      1600\n",
      "   macro avg     0.5814    0.5750    0.5748      1600\n",
      "weighted avg     0.5814    0.5750    0.5748      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4286    0.5250    0.4719       200\n",
      "           1     0.5688    0.6200    0.5933       200\n",
      "           2     0.5177    0.3650    0.4282       200\n",
      "           3     0.5365    0.5150    0.5255       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4189    0.4650    0.4408       200\n",
      "           7     0.4385    0.4100    0.4238       200\n",
      "\n",
      "    accuracy                         0.5763      1600\n",
      "   macro avg     0.5820    0.5762    0.5755      1600\n",
      "weighted avg     0.5820    0.5763    0.5755      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 57.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4309    0.5300    0.4753       200\n",
      "           1     0.5701    0.6100    0.5894       200\n",
      "           2     0.5034    0.3750    0.4298       200\n",
      "           3     0.5228    0.5150    0.5189       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4159    0.4450    0.4300       200\n",
      "           7     0.4432    0.4100    0.4260       200\n",
      "\n",
      "    accuracy                         0.5744      1600\n",
      "   macro avg     0.5791    0.5744    0.5737      1600\n",
      "weighted avg     0.5791    0.5744    0.5737      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 57.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4235    0.5400    0.4747       200\n",
      "           1     0.5500    0.6050    0.5762       200\n",
      "           2     0.5252    0.3650    0.4307       200\n",
      "           3     0.5731    0.4900    0.5283       200\n",
      "           4     0.9302    0.8000    0.8602       200\n",
      "           5     0.8206    0.9150    0.8652       200\n",
      "           6     0.4238    0.4450    0.4341       200\n",
      "           7     0.4286    0.4500    0.4390       200\n",
      "\n",
      "    accuracy                         0.5763      1600\n",
      "   macro avg     0.5844    0.5763    0.5761      1600\n",
      "weighted avg     0.5844    0.5763    0.5761      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 57.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4258    0.5450    0.4781       200\n",
      "           1     0.5611    0.6200    0.5891       200\n",
      "           2     0.5172    0.3750    0.4348       200\n",
      "           3     0.5455    0.5100    0.5271       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4171    0.4400    0.4282       200\n",
      "           7     0.4378    0.4050    0.4208       200\n",
      "\n",
      "    accuracy                         0.5756      1600\n",
      "   macro avg     0.5814    0.5756    0.5748      1600\n",
      "weighted avg     0.5814    0.5756    0.5748      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4268    0.5250    0.4709       200\n",
      "           1     0.5721    0.6150    0.5928       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5297    0.4900    0.5091       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4110    0.4500    0.4296       200\n",
      "           7     0.4433    0.4300    0.4365       200\n",
      "\n",
      "    accuracy                         0.5744      1600\n",
      "   macro avg     0.5804    0.5744    0.5741      1600\n",
      "weighted avg     0.5804    0.5744    0.5741      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 57.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4327    0.5300    0.4764       200\n",
      "           1     0.5525    0.6050    0.5776       200\n",
      "           2     0.5248    0.3700    0.4340       200\n",
      "           3     0.5319    0.5000    0.5155       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4099    0.4550    0.4313       200\n",
      "           7     0.4368    0.4150    0.4256       200\n",
      "\n",
      "    accuracy                         0.5731      1600\n",
      "   macro avg     0.5794    0.5731    0.5726      1600\n",
      "weighted avg     0.5794    0.5731    0.5726      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4332    0.5350    0.4787       200\n",
      "           1     0.5674    0.6100    0.5880       200\n",
      "           2     0.5214    0.3650    0.4294       200\n",
      "           3     0.5323    0.4950    0.5130       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4099    0.4550    0.4313       200\n",
      "           7     0.4410    0.4300    0.4354       200\n",
      "\n",
      "    accuracy                         0.5750      1600\n",
      "   macro avg     0.5815    0.5750    0.5745      1600\n",
      "weighted avg     0.5815    0.5750    0.5745      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4268    0.5250    0.4709       200\n",
      "           1     0.5721    0.6150    0.5928       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5297    0.4900    0.5091       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4110    0.4500    0.4296       200\n",
      "           7     0.4433    0.4300    0.4365       200\n",
      "\n",
      "    accuracy                         0.5744      1600\n",
      "   macro avg     0.5804    0.5744    0.5741      1600\n",
      "weighted avg     0.5804    0.5744    0.5741      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 57.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4303    0.5250    0.4730       200\n",
      "           1     0.5602    0.6050    0.5817       200\n",
      "           2     0.5205    0.3800    0.4393       200\n",
      "           3     0.5435    0.5000    0.5208       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4144    0.4600    0.4360       200\n",
      "           7     0.4352    0.4200    0.4275       200\n",
      "\n",
      "    accuracy                         0.5750      1600\n",
      "   macro avg     0.5814    0.5750    0.5748      1600\n",
      "weighted avg     0.5814    0.5750    0.5748      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4286    0.5250    0.4719       200\n",
      "           1     0.5688    0.6200    0.5933       200\n",
      "           2     0.5177    0.3650    0.4282       200\n",
      "           3     0.5365    0.5150    0.5255       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4189    0.4650    0.4408       200\n",
      "           7     0.4385    0.4100    0.4238       200\n",
      "\n",
      "    accuracy                         0.5763      1600\n",
      "   macro avg     0.5820    0.5762    0.5755      1600\n",
      "weighted avg     0.5820    0.5763    0.5755      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 57.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4309    0.5300    0.4753       200\n",
      "           1     0.5701    0.6100    0.5894       200\n",
      "           2     0.5034    0.3750    0.4298       200\n",
      "           3     0.5228    0.5150    0.5189       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4159    0.4450    0.4300       200\n",
      "           7     0.4432    0.4100    0.4260       200\n",
      "\n",
      "    accuracy                         0.5744      1600\n",
      "   macro avg     0.5791    0.5744    0.5737      1600\n",
      "weighted avg     0.5791    0.5744    0.5737      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4268    0.5250    0.4709       200\n",
      "           1     0.5622    0.6100    0.5851       200\n",
      "           2     0.5245    0.3750    0.4373       200\n",
      "           3     0.5889    0.5300    0.5579       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8243    0.9150    0.8673       200\n",
      "           6     0.4208    0.4650    0.4418       200\n",
      "           7     0.4350    0.4350    0.4350       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5890    0.5813    0.5816      1600\n",
      "weighted avg     0.5890    0.5813    0.5816      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 57.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4403    0.5350    0.4831       200\n",
      "           1     0.5668    0.6150    0.5899       200\n",
      "           2     0.5208    0.3750    0.4360       200\n",
      "           3     0.5288    0.5050    0.5166       200\n",
      "           4     0.9302    0.8000    0.8602       200\n",
      "           5     0.8206    0.9150    0.8652       200\n",
      "           6     0.4136    0.4550    0.4333       200\n",
      "           7     0.4368    0.4150    0.4256       200\n",
      "\n",
      "    accuracy                         0.5769      1600\n",
      "   macro avg     0.5823    0.5769    0.5763      1600\n",
      "weighted avg     0.5823    0.5769    0.5763      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4298    0.5200    0.4706       200\n",
      "           1     0.5668    0.6150    0.5899       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5179    0.5050    0.5114       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4167    0.4500    0.4327       200\n",
      "           7     0.4339    0.4100    0.4216       200\n",
      "\n",
      "    accuracy                         0.5731      1600\n",
      "   macro avg     0.5782    0.5731    0.5725      1600\n",
      "weighted avg     0.5782    0.5731    0.5725      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 57.12%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4245    0.5200    0.4674       200\n",
      "           1     0.5622    0.6100    0.5851       200\n",
      "           2     0.5290    0.3650    0.4320       200\n",
      "           3     0.5215    0.4850    0.5026       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4062    0.4550    0.4292       200\n",
      "           7     0.4359    0.4250    0.4304       200\n",
      "\n",
      "    accuracy                         0.5713      1600\n",
      "   macro avg     0.5783    0.5713    0.5709      1600\n",
      "weighted avg     0.5783    0.5713    0.5709      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4286    0.5250    0.4719       200\n",
      "           1     0.5674    0.6100    0.5880       200\n",
      "           2     0.5177    0.3650    0.4282       200\n",
      "           3     0.5255    0.5150    0.5202       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4081    0.4550    0.4303       200\n",
      "           7     0.4378    0.4050    0.4208       200\n",
      "\n",
      "    accuracy                         0.5731      1600\n",
      "   macro avg     0.5790    0.5731    0.5725      1600\n",
      "weighted avg     0.5790    0.5731    0.5725      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4298    0.5200    0.4706       200\n",
      "           1     0.5668    0.6150    0.5899       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5179    0.5050    0.5114       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4167    0.4500    0.4327       200\n",
      "           7     0.4339    0.4100    0.4216       200\n",
      "\n",
      "    accuracy                         0.5731      1600\n",
      "   macro avg     0.5782    0.5731    0.5725      1600\n",
      "weighted avg     0.5782    0.5731    0.5725      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 57.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4245    0.5200    0.4674       200\n",
      "           1     0.5701    0.6100    0.5894       200\n",
      "           2     0.5172    0.3750    0.4348       200\n",
      "           3     0.5464    0.5300    0.5381       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4178    0.4700    0.4424       200\n",
      "           7     0.4451    0.4050    0.4241       200\n",
      "\n",
      "    accuracy                         0.5775      1600\n",
      "   macro avg     0.5835    0.5775    0.5771      1600\n",
      "weighted avg     0.5835    0.5775    0.5771      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4303    0.5250    0.4730       200\n",
      "           1     0.5642    0.6150    0.5885       200\n",
      "           2     0.5172    0.3750    0.4348       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4196    0.4700    0.4434       200\n",
      "           7     0.4475    0.4050    0.4252       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5837    0.5781    0.5774      1600\n",
      "weighted avg     0.5837    0.5781    0.5774      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 57.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4332    0.5350    0.4787       200\n",
      "           1     0.5648    0.6100    0.5865       200\n",
      "           2     0.5175    0.3700    0.4315       200\n",
      "           3     0.5300    0.5300    0.5300       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4112    0.4400    0.4251       200\n",
      "           7     0.4432    0.4100    0.4260       200\n",
      "\n",
      "    accuracy                         0.5756      1600\n",
      "   macro avg     0.5808    0.5756    0.5748      1600\n",
      "weighted avg     0.5808    0.5756    0.5748      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4373    0.5750    0.4968       200\n",
      "           1     0.5780    0.6300    0.6029       200\n",
      "           2     0.5433    0.3450    0.4220       200\n",
      "           3     0.5158    0.4900    0.5026       200\n",
      "           4     0.9148    0.8050    0.8564       200\n",
      "           5     0.8190    0.9050    0.8599       200\n",
      "           6     0.4363    0.4450    0.4406       200\n",
      "           7     0.4328    0.4350    0.4339       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5847    0.5787    0.5769      1600\n",
      "weighted avg     0.5847    0.5787    0.5769      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 57.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4361    0.5800    0.4979       200\n",
      "           1     0.5753    0.6300    0.6014       200\n",
      "           2     0.5417    0.3250    0.4062       200\n",
      "           3     0.5179    0.5050    0.5114       200\n",
      "           4     0.9148    0.8050    0.8564       200\n",
      "           5     0.8190    0.9050    0.8599       200\n",
      "           6     0.4363    0.4450    0.4406       200\n",
      "           7     0.4422    0.4400    0.4411       200\n",
      "\n",
      "    accuracy                         0.5794      1600\n",
      "   macro avg     0.5854    0.5794    0.5769      1600\n",
      "weighted avg     0.5854    0.5794    0.5769      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4394    0.5800    0.5000       200\n",
      "           1     0.5695    0.6350    0.6005       200\n",
      "           2     0.5508    0.3250    0.4088       200\n",
      "           3     0.5153    0.5050    0.5101       200\n",
      "           4     0.9148    0.8050    0.8564       200\n",
      "           5     0.8190    0.9050    0.8599       200\n",
      "           6     0.4378    0.4400    0.4389       200\n",
      "           7     0.4279    0.4300    0.4289       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5843    0.5781    0.5754      1600\n",
      "weighted avg     0.5843    0.5781    0.5754      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4394    0.5800    0.5000       200\n",
      "           1     0.5799    0.6350    0.6062       200\n",
      "           2     0.5349    0.3450    0.4195       200\n",
      "           3     0.5294    0.4950    0.5116       200\n",
      "           4     0.9148    0.8050    0.8564       200\n",
      "           5     0.8190    0.9050    0.8599       200\n",
      "           6     0.4320    0.4450    0.4384       200\n",
      "           7     0.4343    0.4300    0.4322       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5855    0.5800    0.5780      1600\n",
      "weighted avg     0.5855    0.5800    0.5780      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4377    0.5800    0.4989       200\n",
      "           1     0.5753    0.6300    0.6014       200\n",
      "           2     0.5410    0.3300    0.4099       200\n",
      "           3     0.5206    0.5050    0.5127       200\n",
      "           4     0.9148    0.8050    0.8564       200\n",
      "           5     0.8190    0.9050    0.8599       200\n",
      "           6     0.4341    0.4450    0.4395       200\n",
      "           7     0.4343    0.4300    0.4322       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5846    0.5787    0.5764      1600\n",
      "weighted avg     0.5846    0.5787    0.5764      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4394    0.5800    0.5000       200\n",
      "           1     0.5695    0.6350    0.6005       200\n",
      "           2     0.5508    0.3250    0.4088       200\n",
      "           3     0.5153    0.5050    0.5101       200\n",
      "           4     0.9148    0.8050    0.8564       200\n",
      "           5     0.8190    0.9050    0.8599       200\n",
      "           6     0.4378    0.4400    0.4389       200\n",
      "           7     0.4279    0.4300    0.4289       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5843    0.5781    0.5754      1600\n",
      "weighted avg     0.5843    0.5781    0.5754      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 57.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4415    0.5850    0.5032       200\n",
      "           1     0.5787    0.6250    0.6010       200\n",
      "           2     0.5354    0.3400    0.4159       200\n",
      "           3     0.5189    0.4800    0.4987       200\n",
      "           4     0.9148    0.8050    0.8564       200\n",
      "           5     0.8190    0.9050    0.8599       200\n",
      "           6     0.4320    0.4450    0.4384       200\n",
      "           7     0.4265    0.4350    0.4307       200\n",
      "\n",
      "    accuracy                         0.5775      1600\n",
      "   macro avg     0.5834    0.5775    0.5755      1600\n",
      "weighted avg     0.5834    0.5775    0.5755      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4377    0.5800    0.4989       200\n",
      "           1     0.5753    0.6300    0.6014       200\n",
      "           2     0.5410    0.3300    0.4099       200\n",
      "           3     0.5178    0.5100    0.5139       200\n",
      "           4     0.9148    0.8050    0.8564       200\n",
      "           5     0.8190    0.9050    0.8599       200\n",
      "           6     0.4363    0.4450    0.4406       200\n",
      "           7     0.4337    0.4250    0.4293       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5844    0.5787    0.5763      1600\n",
      "weighted avg     0.5844    0.5787    0.5763      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4394    0.5800    0.5000       200\n",
      "           1     0.5695    0.6350    0.6005       200\n",
      "           2     0.5508    0.3250    0.4088       200\n",
      "           3     0.5127    0.5050    0.5088       200\n",
      "           4     0.9148    0.8050    0.8564       200\n",
      "           5     0.8190    0.9050    0.8599       200\n",
      "           6     0.4356    0.4400    0.4378       200\n",
      "           7     0.4322    0.4300    0.4311       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5843    0.5781    0.5754      1600\n",
      "weighted avg     0.5843    0.5781    0.5754      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 57.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4235    0.5400    0.4747       200\n",
      "           1     0.5500    0.6050    0.5762       200\n",
      "           2     0.5252    0.3650    0.4307       200\n",
      "           3     0.5731    0.4900    0.5283       200\n",
      "           4     0.9302    0.8000    0.8602       200\n",
      "           5     0.8206    0.9150    0.8652       200\n",
      "           6     0.4238    0.4450    0.4341       200\n",
      "           7     0.4286    0.4500    0.4390       200\n",
      "\n",
      "    accuracy                         0.5763      1600\n",
      "   macro avg     0.5844    0.5763    0.5761      1600\n",
      "weighted avg     0.5844    0.5763    0.5761      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 57.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4258    0.5450    0.4781       200\n",
      "           1     0.5611    0.6200    0.5891       200\n",
      "           2     0.5172    0.3750    0.4348       200\n",
      "           3     0.5455    0.5100    0.5271       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4171    0.4400    0.4282       200\n",
      "           7     0.4378    0.4050    0.4208       200\n",
      "\n",
      "    accuracy                         0.5756      1600\n",
      "   macro avg     0.5814    0.5756    0.5748      1600\n",
      "weighted avg     0.5814    0.5756    0.5748      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4268    0.5250    0.4709       200\n",
      "           1     0.5721    0.6150    0.5928       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5297    0.4900    0.5091       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4110    0.4500    0.4296       200\n",
      "           7     0.4433    0.4300    0.4365       200\n",
      "\n",
      "    accuracy                         0.5744      1600\n",
      "   macro avg     0.5804    0.5744    0.5741      1600\n",
      "weighted avg     0.5804    0.5744    0.5741      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 57.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4327    0.5300    0.4764       200\n",
      "           1     0.5525    0.6050    0.5776       200\n",
      "           2     0.5248    0.3700    0.4340       200\n",
      "           3     0.5319    0.5000    0.5155       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4099    0.4550    0.4313       200\n",
      "           7     0.4368    0.4150    0.4256       200\n",
      "\n",
      "    accuracy                         0.5731      1600\n",
      "   macro avg     0.5794    0.5731    0.5726      1600\n",
      "weighted avg     0.5794    0.5731    0.5726      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4332    0.5350    0.4787       200\n",
      "           1     0.5674    0.6100    0.5880       200\n",
      "           2     0.5214    0.3650    0.4294       200\n",
      "           3     0.5323    0.4950    0.5130       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4099    0.4550    0.4313       200\n",
      "           7     0.4410    0.4300    0.4354       200\n",
      "\n",
      "    accuracy                         0.5750      1600\n",
      "   macro avg     0.5815    0.5750    0.5745      1600\n",
      "weighted avg     0.5815    0.5750    0.5745      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4268    0.5250    0.4709       200\n",
      "           1     0.5721    0.6150    0.5928       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5297    0.4900    0.5091       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4110    0.4500    0.4296       200\n",
      "           7     0.4433    0.4300    0.4365       200\n",
      "\n",
      "    accuracy                         0.5744      1600\n",
      "   macro avg     0.5804    0.5744    0.5741      1600\n",
      "weighted avg     0.5804    0.5744    0.5741      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 57.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4303    0.5250    0.4730       200\n",
      "           1     0.5602    0.6050    0.5817       200\n",
      "           2     0.5205    0.3800    0.4393       200\n",
      "           3     0.5435    0.5000    0.5208       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4144    0.4600    0.4360       200\n",
      "           7     0.4352    0.4200    0.4275       200\n",
      "\n",
      "    accuracy                         0.5750      1600\n",
      "   macro avg     0.5814    0.5750    0.5748      1600\n",
      "weighted avg     0.5814    0.5750    0.5748      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4286    0.5250    0.4719       200\n",
      "           1     0.5688    0.6200    0.5933       200\n",
      "           2     0.5177    0.3650    0.4282       200\n",
      "           3     0.5365    0.5150    0.5255       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4189    0.4650    0.4408       200\n",
      "           7     0.4385    0.4100    0.4238       200\n",
      "\n",
      "    accuracy                         0.5763      1600\n",
      "   macro avg     0.5820    0.5762    0.5755      1600\n",
      "weighted avg     0.5820    0.5763    0.5755      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=5, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 57.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4309    0.5300    0.4753       200\n",
      "           1     0.5701    0.6100    0.5894       200\n",
      "           2     0.5034    0.3750    0.4298       200\n",
      "           3     0.5228    0.5150    0.5189       200\n",
      "           4     0.9298    0.7950    0.8571       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4159    0.4450    0.4300       200\n",
      "           7     0.4432    0.4100    0.4260       200\n",
      "\n",
      "    accuracy                         0.5744      1600\n",
      "   macro avg     0.5791    0.5744    0.5737      1600\n",
      "weighted avg     0.5791    0.5744    0.5737      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4259    0.5600    0.4838       200\n",
      "           1     0.5808    0.6650    0.6200       200\n",
      "           2     0.5556    0.3500    0.4294       200\n",
      "           3     0.5556    0.5500    0.5528       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4742    0.4600    0.4670       200\n",
      "           7     0.4485    0.4350    0.4416       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5985    0.5938    0.5916      1600\n",
      "weighted avg     0.5985    0.5938    0.5916      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4179    0.5600    0.4786       200\n",
      "           1     0.5862    0.6800    0.6296       200\n",
      "           2     0.5366    0.3300    0.4087       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4515    0.4650    0.4581       200\n",
      "           7     0.4652    0.4350    0.4496       200\n",
      "\n",
      "    accuracy                         0.5887      1600\n",
      "   macro avg     0.5930    0.5887    0.5859      1600\n",
      "weighted avg     0.5930    0.5887    0.5859      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4142    0.5550    0.4744       200\n",
      "           1     0.5855    0.6850    0.6313       200\n",
      "           2     0.5397    0.3400    0.4172       200\n",
      "           3     0.5482    0.5400    0.5441       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4600    0.4600    0.4600       200\n",
      "           7     0.4775    0.4250    0.4497       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5960    0.5919    0.5891      1600\n",
      "weighted avg     0.5960    0.5919    0.5891      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4173    0.5550    0.4764       200\n",
      "           1     0.5867    0.6600    0.6212       200\n",
      "           2     0.5197    0.3300    0.4037       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4515    0.4650    0.4581       200\n",
      "           7     0.4456    0.4300    0.4377       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5900    0.5863    0.5838      1600\n",
      "weighted avg     0.5900    0.5863    0.5838      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4179    0.5600    0.4786       200\n",
      "           1     0.5815    0.6600    0.6183       200\n",
      "           2     0.5280    0.3300    0.4062       200\n",
      "           3     0.5543    0.5100    0.5312       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4541    0.4700    0.4619       200\n",
      "           7     0.4607    0.4400    0.4501       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5919    0.5875    0.5851      1600\n",
      "weighted avg     0.5919    0.5875    0.5851      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4142    0.5550    0.4744       200\n",
      "           1     0.5855    0.6850    0.6313       200\n",
      "           2     0.5397    0.3400    0.4172       200\n",
      "           3     0.5482    0.5400    0.5441       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4600    0.4600    0.4600       200\n",
      "           7     0.4775    0.4250    0.4497       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5960    0.5919    0.5891      1600\n",
      "weighted avg     0.5960    0.5919    0.5891      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4242    0.5600    0.4828       200\n",
      "           1     0.5903    0.6700    0.6276       200\n",
      "           2     0.5259    0.3550    0.4239       200\n",
      "           3     0.5814    0.5000    0.5376       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4645    0.4900    0.4769       200\n",
      "           7     0.4485    0.4350    0.4416       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5972    0.5925    0.5908      1600\n",
      "weighted avg     0.5972    0.5925    0.5908      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4205    0.5550    0.4784       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5354    0.3400    0.4159       200\n",
      "           3     0.5455    0.5100    0.5271       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4597    0.4850    0.4720       200\n",
      "           7     0.4699    0.4300    0.4491       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5935    0.5894    0.5869      1600\n",
      "weighted avg     0.5935    0.5894    0.5869      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4179    0.5600    0.4786       200\n",
      "           1     0.5880    0.6850    0.6328       200\n",
      "           2     0.5484    0.3400    0.4198       200\n",
      "           3     0.5596    0.5400    0.5496       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4585    0.4700    0.4642       200\n",
      "           7     0.4778    0.4300    0.4526       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5992    0.5944    0.5917      1600\n",
      "weighted avg     0.5992    0.5944    0.5917      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4259    0.5600    0.4838       200\n",
      "           1     0.5808    0.6650    0.6200       200\n",
      "           2     0.5556    0.3500    0.4294       200\n",
      "           3     0.5556    0.5500    0.5528       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4742    0.4600    0.4670       200\n",
      "           7     0.4485    0.4350    0.4416       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5985    0.5938    0.5916      1600\n",
      "weighted avg     0.5985    0.5938    0.5916      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4179    0.5600    0.4786       200\n",
      "           1     0.5862    0.6800    0.6296       200\n",
      "           2     0.5366    0.3300    0.4087       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4515    0.4650    0.4581       200\n",
      "           7     0.4652    0.4350    0.4496       200\n",
      "\n",
      "    accuracy                         0.5887      1600\n",
      "   macro avg     0.5930    0.5887    0.5859      1600\n",
      "weighted avg     0.5930    0.5887    0.5859      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4142    0.5550    0.4744       200\n",
      "           1     0.5855    0.6850    0.6313       200\n",
      "           2     0.5397    0.3400    0.4172       200\n",
      "           3     0.5482    0.5400    0.5441       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4600    0.4600    0.4600       200\n",
      "           7     0.4775    0.4250    0.4497       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5960    0.5919    0.5891      1600\n",
      "weighted avg     0.5960    0.5919    0.5891      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4173    0.5550    0.4764       200\n",
      "           1     0.5867    0.6600    0.6212       200\n",
      "           2     0.5197    0.3300    0.4037       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4515    0.4650    0.4581       200\n",
      "           7     0.4456    0.4300    0.4377       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5900    0.5863    0.5838      1600\n",
      "weighted avg     0.5900    0.5863    0.5838      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4179    0.5600    0.4786       200\n",
      "           1     0.5815    0.6600    0.6183       200\n",
      "           2     0.5280    0.3300    0.4062       200\n",
      "           3     0.5543    0.5100    0.5312       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4541    0.4700    0.4619       200\n",
      "           7     0.4607    0.4400    0.4501       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5919    0.5875    0.5851      1600\n",
      "weighted avg     0.5919    0.5875    0.5851      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4142    0.5550    0.4744       200\n",
      "           1     0.5855    0.6850    0.6313       200\n",
      "           2     0.5397    0.3400    0.4172       200\n",
      "           3     0.5482    0.5400    0.5441       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4600    0.4600    0.4600       200\n",
      "           7     0.4775    0.4250    0.4497       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5960    0.5919    0.5891      1600\n",
      "weighted avg     0.5960    0.5919    0.5891      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4242    0.5600    0.4828       200\n",
      "           1     0.5903    0.6700    0.6276       200\n",
      "           2     0.5259    0.3550    0.4239       200\n",
      "           3     0.5814    0.5000    0.5376       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4645    0.4900    0.4769       200\n",
      "           7     0.4485    0.4350    0.4416       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5972    0.5925    0.5908      1600\n",
      "weighted avg     0.5972    0.5925    0.5908      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4205    0.5550    0.4784       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5354    0.3400    0.4159       200\n",
      "           3     0.5455    0.5100    0.5271       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4597    0.4850    0.4720       200\n",
      "           7     0.4699    0.4300    0.4491       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5935    0.5894    0.5869      1600\n",
      "weighted avg     0.5935    0.5894    0.5869      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4179    0.5600    0.4786       200\n",
      "           1     0.5880    0.6850    0.6328       200\n",
      "           2     0.5484    0.3400    0.4198       200\n",
      "           3     0.5596    0.5400    0.5496       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4585    0.4700    0.4642       200\n",
      "           7     0.4778    0.4300    0.4526       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5992    0.5944    0.5917      1600\n",
      "weighted avg     0.5992    0.5944    0.5917      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4237    0.5550    0.4805       200\n",
      "           1     0.5939    0.6800    0.6340       200\n",
      "           2     0.5462    0.3550    0.4303       200\n",
      "           3     0.5515    0.5350    0.5431       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4643    0.4550    0.4596       200\n",
      "           7     0.4560    0.4400    0.4478       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5979    0.5938    0.5917      1600\n",
      "weighted avg     0.5979    0.5938    0.5917      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4205    0.5550    0.4784       200\n",
      "           1     0.5812    0.6800    0.6267       200\n",
      "           2     0.5410    0.3300    0.4099       200\n",
      "           3     0.5538    0.5150    0.5337       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4519    0.4700    0.4608       200\n",
      "           7     0.4628    0.4350    0.4485       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5937    0.5894    0.5865      1600\n",
      "weighted avg     0.5937    0.5894    0.5865      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4164    0.5600    0.4776       200\n",
      "           1     0.5880    0.6850    0.6328       200\n",
      "           2     0.5426    0.3500    0.4255       200\n",
      "           3     0.5544    0.5350    0.5445       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4623    0.4600    0.4612       200\n",
      "           7     0.4778    0.4300    0.4526       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5981    0.5938    0.5913      1600\n",
      "weighted avg     0.5981    0.5938    0.5913      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5830    0.6500    0.6147       200\n",
      "           2     0.5267    0.3450    0.4169       200\n",
      "           3     0.5604    0.5100    0.5340       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4589    0.4750    0.4668       200\n",
      "           7     0.4467    0.4400    0.4433       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5919    0.5881    0.5862      1600\n",
      "weighted avg     0.5919    0.5881    0.5862      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4211    0.5600    0.4807       200\n",
      "           1     0.5841    0.6600    0.6197       200\n",
      "           2     0.5276    0.3350    0.4098       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4567    0.4750    0.4657       200\n",
      "           7     0.4603    0.4350    0.4473       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5921    0.5881    0.5857      1600\n",
      "weighted avg     0.5921    0.5881    0.5857      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4164    0.5600    0.4776       200\n",
      "           1     0.5880    0.6850    0.6328       200\n",
      "           2     0.5426    0.3500    0.4255       200\n",
      "           3     0.5544    0.5350    0.5445       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4623    0.4600    0.4612       200\n",
      "           7     0.4778    0.4300    0.4526       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5981    0.5938    0.5913      1600\n",
      "weighted avg     0.5981    0.5938    0.5913      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4259    0.5600    0.4838       200\n",
      "           1     0.5903    0.6700    0.6276       200\n",
      "           2     0.5294    0.3600    0.4286       200\n",
      "           3     0.5746    0.5200    0.5459       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4667    0.4900    0.4780       200\n",
      "           7     0.4624    0.4300    0.4456       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5990    0.5950    0.5932      1600\n",
      "weighted avg     0.5990    0.5950    0.5932      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4189    0.5550    0.4774       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5234    0.3350    0.4085       200\n",
      "           3     0.5450    0.5150    0.5296       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4641    0.4850    0.4743       200\n",
      "           7     0.4751    0.4300    0.4514       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5929    0.5894    0.5868      1600\n",
      "weighted avg     0.5929    0.5894    0.5868      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.62%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4201    0.5650    0.4819       200\n",
      "           1     0.5905    0.6850    0.6343       200\n",
      "           2     0.5469    0.3500    0.4268       200\n",
      "           3     0.5590    0.5450    0.5519       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4653    0.4700    0.4677       200\n",
      "           7     0.4802    0.4250    0.4509       200\n",
      "\n",
      "    accuracy                         0.5962      1600\n",
      "   macro avg     0.6006    0.5962    0.5937      1600\n",
      "weighted avg     0.6006    0.5962    0.5937      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4047    0.6050    0.4850       200\n",
      "           1     0.5805    0.6850    0.6284       200\n",
      "           2     0.6023    0.2650    0.3681       200\n",
      "           3     0.5045    0.5600    0.5308       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4244    0.4350    0.4296       200\n",
      "           7     0.4934    0.3750    0.4261       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5935    0.5819    0.5753      1600\n",
      "weighted avg     0.5935    0.5819    0.5753      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 57.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4060    0.6050    0.4859       200\n",
      "           1     0.5685    0.6850    0.6213       200\n",
      "           2     0.6024    0.2500    0.3534       200\n",
      "           3     0.5047    0.5400    0.5217       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4175    0.4300    0.4236       200\n",
      "           7     0.4688    0.3750    0.4167       200\n",
      "\n",
      "    accuracy                         0.5769      1600\n",
      "   macro avg     0.5883    0.5769    0.5696      1600\n",
      "weighted avg     0.5883    0.5769    0.5696      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4054    0.6000    0.4839       200\n",
      "           1     0.5661    0.6850    0.6199       200\n",
      "           2     0.6173    0.2500    0.3559       200\n",
      "           3     0.5138    0.5600    0.5359       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4229    0.4250    0.4239       200\n",
      "           7     0.4663    0.3800    0.4187       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5908    0.5788    0.5713      1600\n",
      "weighted avg     0.5908    0.5787    0.5713      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4060    0.6050    0.4859       200\n",
      "           1     0.5708    0.6850    0.6227       200\n",
      "           2     0.6071    0.2550    0.3592       200\n",
      "           3     0.5068    0.5550    0.5298       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4223    0.4350    0.4286       200\n",
      "           7     0.4903    0.3800    0.4282       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5928    0.5806    0.5736      1600\n",
      "weighted avg     0.5928    0.5806    0.5736      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4060    0.6050    0.4859       200\n",
      "           1     0.5685    0.6850    0.6213       200\n",
      "           2     0.6024    0.2500    0.3534       200\n",
      "           3     0.5023    0.5400    0.5205       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4175    0.4300    0.4236       200\n",
      "           7     0.4717    0.3750    0.4178       200\n",
      "\n",
      "    accuracy                         0.5769      1600\n",
      "   macro avg     0.5884    0.5769    0.5696      1600\n",
      "weighted avg     0.5884    0.5769    0.5696      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4054    0.6000    0.4839       200\n",
      "           1     0.5661    0.6850    0.6199       200\n",
      "           2     0.6173    0.2500    0.3559       200\n",
      "           3     0.5138    0.5600    0.5359       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4229    0.4250    0.4239       200\n",
      "           7     0.4663    0.3800    0.4187       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5908    0.5788    0.5713      1600\n",
      "weighted avg     0.5908    0.5787    0.5713      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4054    0.6000    0.4839       200\n",
      "           1     0.5661    0.6850    0.6199       200\n",
      "           2     0.6071    0.2550    0.3592       200\n",
      "           3     0.5068    0.5600    0.5321       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4257    0.4300    0.4279       200\n",
      "           7     0.4841    0.3800    0.4258       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5917    0.5800    0.5728      1600\n",
      "weighted avg     0.5917    0.5800    0.5728      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4054    0.6000    0.4839       200\n",
      "           1     0.5667    0.6800    0.6182       200\n",
      "           2     0.6024    0.2500    0.3534       200\n",
      "           3     0.5045    0.5550    0.5286       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4236    0.4300    0.4268       200\n",
      "           7     0.4688    0.3750    0.4167       200\n",
      "\n",
      "    accuracy                         0.5775      1600\n",
      "   macro avg     0.5888    0.5775    0.5702      1600\n",
      "weighted avg     0.5888    0.5775    0.5702      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4054    0.6000    0.4839       200\n",
      "           1     0.5638    0.6850    0.6185       200\n",
      "           2     0.6173    0.2500    0.3559       200\n",
      "           3     0.5136    0.5650    0.5381       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4257    0.4300    0.4279       200\n",
      "           7     0.4780    0.3800    0.4234       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5923    0.5800    0.5724      1600\n",
      "weighted avg     0.5923    0.5800    0.5724      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4259    0.5600    0.4838       200\n",
      "           1     0.5808    0.6650    0.6200       200\n",
      "           2     0.5556    0.3500    0.4294       200\n",
      "           3     0.5556    0.5500    0.5528       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4742    0.4600    0.4670       200\n",
      "           7     0.4485    0.4350    0.4416       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5985    0.5938    0.5916      1600\n",
      "weighted avg     0.5985    0.5938    0.5916      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4179    0.5600    0.4786       200\n",
      "           1     0.5862    0.6800    0.6296       200\n",
      "           2     0.5366    0.3300    0.4087       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4515    0.4650    0.4581       200\n",
      "           7     0.4652    0.4350    0.4496       200\n",
      "\n",
      "    accuracy                         0.5887      1600\n",
      "   macro avg     0.5930    0.5887    0.5859      1600\n",
      "weighted avg     0.5930    0.5887    0.5859      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4142    0.5550    0.4744       200\n",
      "           1     0.5855    0.6850    0.6313       200\n",
      "           2     0.5397    0.3400    0.4172       200\n",
      "           3     0.5482    0.5400    0.5441       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4600    0.4600    0.4600       200\n",
      "           7     0.4775    0.4250    0.4497       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5960    0.5919    0.5891      1600\n",
      "weighted avg     0.5960    0.5919    0.5891      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4173    0.5550    0.4764       200\n",
      "           1     0.5867    0.6600    0.6212       200\n",
      "           2     0.5197    0.3300    0.4037       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4515    0.4650    0.4581       200\n",
      "           7     0.4456    0.4300    0.4377       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5900    0.5863    0.5838      1600\n",
      "weighted avg     0.5900    0.5863    0.5838      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4179    0.5600    0.4786       200\n",
      "           1     0.5815    0.6600    0.6183       200\n",
      "           2     0.5280    0.3300    0.4062       200\n",
      "           3     0.5543    0.5100    0.5312       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4541    0.4700    0.4619       200\n",
      "           7     0.4607    0.4400    0.4501       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5919    0.5875    0.5851      1600\n",
      "weighted avg     0.5919    0.5875    0.5851      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4142    0.5550    0.4744       200\n",
      "           1     0.5855    0.6850    0.6313       200\n",
      "           2     0.5397    0.3400    0.4172       200\n",
      "           3     0.5482    0.5400    0.5441       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4600    0.4600    0.4600       200\n",
      "           7     0.4775    0.4250    0.4497       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5960    0.5919    0.5891      1600\n",
      "weighted avg     0.5960    0.5919    0.5891      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4242    0.5600    0.4828       200\n",
      "           1     0.5903    0.6700    0.6276       200\n",
      "           2     0.5259    0.3550    0.4239       200\n",
      "           3     0.5814    0.5000    0.5376       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4645    0.4900    0.4769       200\n",
      "           7     0.4485    0.4350    0.4416       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5972    0.5925    0.5908      1600\n",
      "weighted avg     0.5972    0.5925    0.5908      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4205    0.5550    0.4784       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5354    0.3400    0.4159       200\n",
      "           3     0.5455    0.5100    0.5271       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4597    0.4850    0.4720       200\n",
      "           7     0.4699    0.4300    0.4491       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5935    0.5894    0.5869      1600\n",
      "weighted avg     0.5935    0.5894    0.5869      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=8, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4179    0.5600    0.4786       200\n",
      "           1     0.5880    0.6850    0.6328       200\n",
      "           2     0.5484    0.3400    0.4198       200\n",
      "           3     0.5596    0.5400    0.5496       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4585    0.4700    0.4642       200\n",
      "           7     0.4778    0.4300    0.4526       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5992    0.5944    0.5917      1600\n",
      "weighted avg     0.5992    0.5944    0.5917      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4186    0.5400    0.4716       200\n",
      "           1     0.5478    0.6300    0.5860       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5771    0.5050    0.5387       200\n",
      "           4     0.9368    0.8150    0.8717       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4608    0.4700    0.4653       200\n",
      "           7     0.4510    0.4600    0.4554       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5920    0.5844    0.5838      1600\n",
      "weighted avg     0.5920    0.5844    0.5838      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 57.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4008    0.5050    0.4469       200\n",
      "           1     0.5556    0.6250    0.5882       200\n",
      "           2     0.4930    0.3500    0.4094       200\n",
      "           3     0.5801    0.5250    0.5512       200\n",
      "           4     0.9318    0.8200    0.8723       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4479    0.4300    0.4388       200\n",
      "           7     0.4272    0.4550    0.4407       200\n",
      "\n",
      "    accuracy                         0.5763      1600\n",
      "   macro avg     0.5823    0.5762    0.5758      1600\n",
      "weighted avg     0.5823    0.5763    0.5758      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4016    0.5100    0.4493       200\n",
      "           1     0.5551    0.6550    0.6009       200\n",
      "           2     0.4932    0.3600    0.4162       200\n",
      "           3     0.5792    0.5300    0.5535       200\n",
      "           4     0.9314    0.8150    0.8693       200\n",
      "           5     0.8182    0.9000    0.8571       200\n",
      "           6     0.4620    0.4250    0.4427       200\n",
      "           7     0.4455    0.4500    0.4478       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5858    0.5806    0.5796      1600\n",
      "weighted avg     0.5858    0.5806    0.5796      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4150    0.5250    0.4636       200\n",
      "           1     0.5614    0.6400    0.5981       200\n",
      "           2     0.4859    0.3450    0.4035       200\n",
      "           3     0.5792    0.5300    0.5535       200\n",
      "           4     0.9318    0.8200    0.8723       200\n",
      "           5     0.8235    0.9100    0.8646       200\n",
      "           6     0.4611    0.4450    0.4529       200\n",
      "           7     0.4510    0.4600    0.4554       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5886    0.5844    0.5830      1600\n",
      "weighted avg     0.5886    0.5844    0.5830      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.3992    0.5150    0.4498       200\n",
      "           1     0.5560    0.6450    0.5972       200\n",
      "           2     0.5000    0.3450    0.4083       200\n",
      "           3     0.5756    0.4950    0.5323       200\n",
      "           4     0.9318    0.8200    0.8723       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4481    0.4100    0.4282       200\n",
      "           7     0.4144    0.4600    0.4360       200\n",
      "\n",
      "    accuracy                         0.5737      1600\n",
      "   macro avg     0.5809    0.5737    0.5729      1600\n",
      "weighted avg     0.5809    0.5737    0.5729      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4016    0.5100    0.4493       200\n",
      "           1     0.5551    0.6550    0.6009       200\n",
      "           2     0.4932    0.3600    0.4162       200\n",
      "           3     0.5792    0.5300    0.5535       200\n",
      "           4     0.9314    0.8150    0.8693       200\n",
      "           5     0.8182    0.9000    0.8571       200\n",
      "           6     0.4620    0.4250    0.4427       200\n",
      "           7     0.4455    0.4500    0.4478       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5858    0.5806    0.5796      1600\n",
      "weighted avg     0.5858    0.5806    0.5796      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4104    0.5150    0.4568       200\n",
      "           1     0.5507    0.6250    0.5855       200\n",
      "           2     0.4894    0.3450    0.4047       200\n",
      "           3     0.5778    0.5200    0.5474       200\n",
      "           4     0.9368    0.8150    0.8717       200\n",
      "           5     0.8235    0.9100    0.8646       200\n",
      "           6     0.4573    0.4550    0.4561       200\n",
      "           7     0.4396    0.4550    0.4472       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5857    0.5800    0.5792      1600\n",
      "weighted avg     0.5857    0.5800    0.5792      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4109    0.5300    0.4629       200\n",
      "           1     0.5498    0.6350    0.5893       200\n",
      "           2     0.5152    0.3400    0.4096       200\n",
      "           3     0.5819    0.5150    0.5464       200\n",
      "           4     0.9314    0.8150    0.8693       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4588    0.4450    0.4518       200\n",
      "           7     0.4346    0.4650    0.4493       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5880    0.5806    0.5797      1600\n",
      "weighted avg     0.5880    0.5806    0.5797      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4024    0.5050    0.4479       200\n",
      "           1     0.5551    0.6550    0.6009       200\n",
      "           2     0.4932    0.3600    0.4162       200\n",
      "           3     0.5819    0.5150    0.5464       200\n",
      "           4     0.9314    0.8150    0.8693       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4677    0.4350    0.4508       200\n",
      "           7     0.4333    0.4550    0.4439       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5859    0.5800    0.5793      1600\n",
      "weighted avg     0.5859    0.5800    0.5793      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4186    0.5400    0.4716       200\n",
      "           1     0.5478    0.6300    0.5860       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5771    0.5050    0.5387       200\n",
      "           4     0.9368    0.8150    0.8717       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4608    0.4700    0.4653       200\n",
      "           7     0.4510    0.4600    0.4554       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5920    0.5844    0.5838      1600\n",
      "weighted avg     0.5920    0.5844    0.5838      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 57.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4008    0.5050    0.4469       200\n",
      "           1     0.5556    0.6250    0.5882       200\n",
      "           2     0.4930    0.3500    0.4094       200\n",
      "           3     0.5801    0.5250    0.5512       200\n",
      "           4     0.9318    0.8200    0.8723       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4479    0.4300    0.4388       200\n",
      "           7     0.4272    0.4550    0.4407       200\n",
      "\n",
      "    accuracy                         0.5763      1600\n",
      "   macro avg     0.5823    0.5762    0.5758      1600\n",
      "weighted avg     0.5823    0.5763    0.5758      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4016    0.5100    0.4493       200\n",
      "           1     0.5551    0.6550    0.6009       200\n",
      "           2     0.4932    0.3600    0.4162       200\n",
      "           3     0.5792    0.5300    0.5535       200\n",
      "           4     0.9314    0.8150    0.8693       200\n",
      "           5     0.8182    0.9000    0.8571       200\n",
      "           6     0.4620    0.4250    0.4427       200\n",
      "           7     0.4455    0.4500    0.4478       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5858    0.5806    0.5796      1600\n",
      "weighted avg     0.5858    0.5806    0.5796      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4150    0.5250    0.4636       200\n",
      "           1     0.5614    0.6400    0.5981       200\n",
      "           2     0.4859    0.3450    0.4035       200\n",
      "           3     0.5792    0.5300    0.5535       200\n",
      "           4     0.9318    0.8200    0.8723       200\n",
      "           5     0.8235    0.9100    0.8646       200\n",
      "           6     0.4611    0.4450    0.4529       200\n",
      "           7     0.4510    0.4600    0.4554       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5886    0.5844    0.5830      1600\n",
      "weighted avg     0.5886    0.5844    0.5830      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.3992    0.5150    0.4498       200\n",
      "           1     0.5560    0.6450    0.5972       200\n",
      "           2     0.5000    0.3450    0.4083       200\n",
      "           3     0.5756    0.4950    0.5323       200\n",
      "           4     0.9318    0.8200    0.8723       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4481    0.4100    0.4282       200\n",
      "           7     0.4144    0.4600    0.4360       200\n",
      "\n",
      "    accuracy                         0.5737      1600\n",
      "   macro avg     0.5809    0.5737    0.5729      1600\n",
      "weighted avg     0.5809    0.5737    0.5729      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4016    0.5100    0.4493       200\n",
      "           1     0.5551    0.6550    0.6009       200\n",
      "           2     0.4932    0.3600    0.4162       200\n",
      "           3     0.5792    0.5300    0.5535       200\n",
      "           4     0.9314    0.8150    0.8693       200\n",
      "           5     0.8182    0.9000    0.8571       200\n",
      "           6     0.4620    0.4250    0.4427       200\n",
      "           7     0.4455    0.4500    0.4478       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5858    0.5806    0.5796      1600\n",
      "weighted avg     0.5858    0.5806    0.5796      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4104    0.5150    0.4568       200\n",
      "           1     0.5507    0.6250    0.5855       200\n",
      "           2     0.4894    0.3450    0.4047       200\n",
      "           3     0.5778    0.5200    0.5474       200\n",
      "           4     0.9368    0.8150    0.8717       200\n",
      "           5     0.8235    0.9100    0.8646       200\n",
      "           6     0.4573    0.4550    0.4561       200\n",
      "           7     0.4396    0.4550    0.4472       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5857    0.5800    0.5792      1600\n",
      "weighted avg     0.5857    0.5800    0.5792      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4109    0.5300    0.4629       200\n",
      "           1     0.5498    0.6350    0.5893       200\n",
      "           2     0.5152    0.3400    0.4096       200\n",
      "           3     0.5819    0.5150    0.5464       200\n",
      "           4     0.9314    0.8150    0.8693       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4588    0.4450    0.4518       200\n",
      "           7     0.4346    0.4650    0.4493       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5880    0.5806    0.5797      1600\n",
      "weighted avg     0.5880    0.5806    0.5797      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4024    0.5050    0.4479       200\n",
      "           1     0.5551    0.6550    0.6009       200\n",
      "           2     0.4932    0.3600    0.4162       200\n",
      "           3     0.5819    0.5150    0.5464       200\n",
      "           4     0.9314    0.8150    0.8693       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4677    0.4350    0.4508       200\n",
      "           7     0.4333    0.4550    0.4439       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5859    0.5800    0.5793      1600\n",
      "weighted avg     0.5859    0.5800    0.5793      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4202    0.5400    0.4726       200\n",
      "           1     0.5609    0.6450    0.6000       200\n",
      "           2     0.5379    0.3550    0.4277       200\n",
      "           3     0.5756    0.4950    0.5323       200\n",
      "           4     0.9314    0.8150    0.8693       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4589    0.4750    0.4668       200\n",
      "           7     0.4375    0.4550    0.4461       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5930    0.5850    0.5843      1600\n",
      "weighted avg     0.5930    0.5850    0.5843      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 57.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4032    0.5100    0.4503       200\n",
      "           1     0.5595    0.6350    0.5948       200\n",
      "           2     0.4929    0.3450    0.4059       200\n",
      "           3     0.5801    0.5250    0.5512       200\n",
      "           4     0.9318    0.8200    0.8723       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4456    0.4300    0.4377       200\n",
      "           7     0.4313    0.4550    0.4428       200\n",
      "\n",
      "    accuracy                         0.5775      1600\n",
      "   macro avg     0.5833    0.5775    0.5768      1600\n",
      "weighted avg     0.5833    0.5775    0.5768      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4000    0.5100    0.4484       200\n",
      "           1     0.5551    0.6550    0.6009       200\n",
      "           2     0.4932    0.3600    0.4162       200\n",
      "           3     0.5815    0.5350    0.5573       200\n",
      "           4     0.9314    0.8150    0.8693       200\n",
      "           5     0.8182    0.9000    0.8571       200\n",
      "           6     0.4595    0.4250    0.4416       200\n",
      "           7     0.4422    0.4400    0.4411       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5851    0.5800    0.5790      1600\n",
      "weighted avg     0.5851    0.5800    0.5790      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4143    0.5200    0.4612       200\n",
      "           1     0.5575    0.6300    0.5915       200\n",
      "           2     0.4828    0.3500    0.4058       200\n",
      "           3     0.5761    0.5300    0.5521       200\n",
      "           4     0.9318    0.8200    0.8723       200\n",
      "           5     0.8235    0.9100    0.8646       200\n",
      "           6     0.4611    0.4450    0.4529       200\n",
      "           7     0.4510    0.4600    0.4554       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5873    0.5831    0.5820      1600\n",
      "weighted avg     0.5873    0.5831    0.5820      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.3969    0.5100    0.4464       200\n",
      "           1     0.5541    0.6400    0.5940       200\n",
      "           2     0.5000    0.3450    0.4083       200\n",
      "           3     0.5723    0.4950    0.5308       200\n",
      "           4     0.9318    0.8200    0.8723       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4481    0.4100    0.4282       200\n",
      "           7     0.4170    0.4650    0.4397       200\n",
      "\n",
      "    accuracy                         0.5731      1600\n",
      "   macro avg     0.5803    0.5731    0.5724      1600\n",
      "weighted avg     0.5803    0.5731    0.5724      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4000    0.5100    0.4484       200\n",
      "           1     0.5551    0.6550    0.6009       200\n",
      "           2     0.4932    0.3600    0.4162       200\n",
      "           3     0.5815    0.5350    0.5573       200\n",
      "           4     0.9314    0.8150    0.8693       200\n",
      "           5     0.8182    0.9000    0.8571       200\n",
      "           6     0.4595    0.4250    0.4416       200\n",
      "           7     0.4422    0.4400    0.4411       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5851    0.5800    0.5790      1600\n",
      "weighted avg     0.5851    0.5800    0.5790      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4104    0.5150    0.4568       200\n",
      "           1     0.5507    0.6250    0.5855       200\n",
      "           2     0.4894    0.3450    0.4047       200\n",
      "           3     0.5801    0.5250    0.5512       200\n",
      "           4     0.9368    0.8150    0.8717       200\n",
      "           5     0.8235    0.9100    0.8646       200\n",
      "           6     0.4573    0.4550    0.4561       200\n",
      "           7     0.4466    0.4600    0.4532       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5868    0.5813    0.5805      1600\n",
      "weighted avg     0.5868    0.5813    0.5805      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4093    0.5300    0.4619       200\n",
      "           1     0.5517    0.6400    0.5926       200\n",
      "           2     0.5191    0.3400    0.4109       200\n",
      "           3     0.5819    0.5150    0.5464       200\n",
      "           4     0.9314    0.8150    0.8693       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4588    0.4450    0.4518       200\n",
      "           7     0.4319    0.4600    0.4455       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5883    0.5806    0.5797      1600\n",
      "weighted avg     0.5883    0.5806    0.5797      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4024    0.5050    0.4479       200\n",
      "           1     0.5551    0.6550    0.6009       200\n",
      "           2     0.4932    0.3600    0.4162       200\n",
      "           3     0.5819    0.5150    0.5464       200\n",
      "           4     0.9314    0.8150    0.8693       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4677    0.4350    0.4508       200\n",
      "           7     0.4333    0.4550    0.4439       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5859    0.5800    0.5793      1600\n",
      "weighted avg     0.5859    0.5800    0.5793      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4027    0.5900    0.4787       200\n",
      "           1     0.5366    0.6600    0.5919       200\n",
      "           2     0.5545    0.2800    0.3721       200\n",
      "           3     0.5517    0.5600    0.5558       200\n",
      "           4     0.8663    0.8750    0.8706       200\n",
      "           5     0.8557    0.8300    0.8426       200\n",
      "           6     0.4474    0.4250    0.4359       200\n",
      "           7     0.4737    0.4050    0.4367       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5861    0.5781    0.5730      1600\n",
      "weighted avg     0.5861    0.5781    0.5730      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 57.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.3993    0.5850    0.4746       200\n",
      "           1     0.5301    0.6600    0.5880       200\n",
      "           2     0.5773    0.2800    0.3771       200\n",
      "           3     0.5385    0.5600    0.5490       200\n",
      "           4     0.8706    0.8750    0.8728       200\n",
      "           5     0.8557    0.8300    0.8426       200\n",
      "           6     0.4516    0.4200    0.4352       200\n",
      "           7     0.4709    0.4050    0.4355       200\n",
      "\n",
      "    accuracy                         0.5769      1600\n",
      "   macro avg     0.5868    0.5769    0.5719      1600\n",
      "weighted avg     0.5868    0.5769    0.5719      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4021    0.5850    0.4766       200\n",
      "           1     0.5400    0.6750    0.6000       200\n",
      "           2     0.5761    0.2650    0.3630       200\n",
      "           3     0.5244    0.5900    0.5553       200\n",
      "           4     0.8706    0.8750    0.8728       200\n",
      "           5     0.8557    0.8300    0.8426       200\n",
      "           6     0.4526    0.4300    0.4410       200\n",
      "           7     0.4904    0.3850    0.4314       200\n",
      "\n",
      "    accuracy                         0.5794      1600\n",
      "   macro avg     0.5890    0.5794    0.5728      1600\n",
      "weighted avg     0.5890    0.5794    0.5728      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4048    0.5950    0.4818       200\n",
      "           1     0.5385    0.6650    0.5951       200\n",
      "           2     0.5700    0.2850    0.3800       200\n",
      "           3     0.5507    0.5700    0.5602       200\n",
      "           4     0.8663    0.8750    0.8706       200\n",
      "           5     0.8557    0.8300    0.8426       200\n",
      "           6     0.4526    0.4300    0.4410       200\n",
      "           7     0.4819    0.4000    0.4372       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5901    0.5813    0.5761      1600\n",
      "weighted avg     0.5901    0.5813    0.5761      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.3993    0.5850    0.4746       200\n",
      "           1     0.5299    0.6650    0.5898       200\n",
      "           2     0.5773    0.2800    0.3771       200\n",
      "           3     0.5377    0.5700    0.5534       200\n",
      "           4     0.8706    0.8750    0.8728       200\n",
      "           5     0.8557    0.8300    0.8426       200\n",
      "           6     0.4545    0.4250    0.4393       200\n",
      "           7     0.4788    0.3950    0.4329       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5880    0.5781    0.5728      1600\n",
      "weighted avg     0.5880    0.5781    0.5728      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4021    0.5850    0.4766       200\n",
      "           1     0.5400    0.6750    0.6000       200\n",
      "           2     0.5761    0.2650    0.3630       200\n",
      "           3     0.5244    0.5900    0.5553       200\n",
      "           4     0.8706    0.8750    0.8728       200\n",
      "           5     0.8557    0.8300    0.8426       200\n",
      "           6     0.4526    0.4300    0.4410       200\n",
      "           7     0.4904    0.3850    0.4314       200\n",
      "\n",
      "    accuracy                         0.5794      1600\n",
      "   macro avg     0.5890    0.5794    0.5728      1600\n",
      "weighted avg     0.5890    0.5794    0.5728      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4048    0.5950    0.4818       200\n",
      "           1     0.5403    0.6700    0.5982       200\n",
      "           2     0.5816    0.2850    0.3826       200\n",
      "           3     0.5437    0.5600    0.5517       200\n",
      "           4     0.8663    0.8750    0.8706       200\n",
      "           5     0.8557    0.8300    0.8426       200\n",
      "           6     0.4490    0.4400    0.4444       200\n",
      "           7     0.4753    0.3850    0.4254       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5896    0.5800    0.5747      1600\n",
      "weighted avg     0.5896    0.5800    0.5747      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4007    0.5850    0.4756       200\n",
      "           1     0.5339    0.6700    0.5942       200\n",
      "           2     0.5895    0.2800    0.3797       200\n",
      "           3     0.5429    0.5700    0.5561       200\n",
      "           4     0.8706    0.8750    0.8728       200\n",
      "           5     0.8557    0.8300    0.8426       200\n",
      "           6     0.4485    0.4350    0.4416       200\n",
      "           7     0.4847    0.3950    0.4353       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5908    0.5800    0.5747      1600\n",
      "weighted avg     0.5908    0.5800    0.5747      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4000    0.5800    0.4735       200\n",
      "           1     0.5357    0.6750    0.5973       200\n",
      "           2     0.5978    0.2750    0.3767       200\n",
      "           3     0.5342    0.5850    0.5585       200\n",
      "           4     0.8706    0.8750    0.8728       200\n",
      "           5     0.8557    0.8300    0.8426       200\n",
      "           6     0.4526    0.4300    0.4410       200\n",
      "           7     0.4877    0.3950    0.4365       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5918    0.5806    0.5749      1600\n",
      "weighted avg     0.5918    0.5806    0.5749      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4186    0.5400    0.4716       200\n",
      "           1     0.5478    0.6300    0.5860       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5771    0.5050    0.5387       200\n",
      "           4     0.9368    0.8150    0.8717       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4608    0.4700    0.4653       200\n",
      "           7     0.4510    0.4600    0.4554       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5920    0.5844    0.5838      1600\n",
      "weighted avg     0.5920    0.5844    0.5838      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 57.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4008    0.5050    0.4469       200\n",
      "           1     0.5556    0.6250    0.5882       200\n",
      "           2     0.4930    0.3500    0.4094       200\n",
      "           3     0.5801    0.5250    0.5512       200\n",
      "           4     0.9318    0.8200    0.8723       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4479    0.4300    0.4388       200\n",
      "           7     0.4272    0.4550    0.4407       200\n",
      "\n",
      "    accuracy                         0.5763      1600\n",
      "   macro avg     0.5823    0.5762    0.5758      1600\n",
      "weighted avg     0.5823    0.5763    0.5758      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4016    0.5100    0.4493       200\n",
      "           1     0.5551    0.6550    0.6009       200\n",
      "           2     0.4932    0.3600    0.4162       200\n",
      "           3     0.5792    0.5300    0.5535       200\n",
      "           4     0.9314    0.8150    0.8693       200\n",
      "           5     0.8182    0.9000    0.8571       200\n",
      "           6     0.4620    0.4250    0.4427       200\n",
      "           7     0.4455    0.4500    0.4478       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5858    0.5806    0.5796      1600\n",
      "weighted avg     0.5858    0.5806    0.5796      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4150    0.5250    0.4636       200\n",
      "           1     0.5614    0.6400    0.5981       200\n",
      "           2     0.4859    0.3450    0.4035       200\n",
      "           3     0.5792    0.5300    0.5535       200\n",
      "           4     0.9318    0.8200    0.8723       200\n",
      "           5     0.8235    0.9100    0.8646       200\n",
      "           6     0.4611    0.4450    0.4529       200\n",
      "           7     0.4510    0.4600    0.4554       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5886    0.5844    0.5830      1600\n",
      "weighted avg     0.5886    0.5844    0.5830      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.3992    0.5150    0.4498       200\n",
      "           1     0.5560    0.6450    0.5972       200\n",
      "           2     0.5000    0.3450    0.4083       200\n",
      "           3     0.5756    0.4950    0.5323       200\n",
      "           4     0.9318    0.8200    0.8723       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4481    0.4100    0.4282       200\n",
      "           7     0.4144    0.4600    0.4360       200\n",
      "\n",
      "    accuracy                         0.5737      1600\n",
      "   macro avg     0.5809    0.5737    0.5729      1600\n",
      "weighted avg     0.5809    0.5737    0.5729      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4016    0.5100    0.4493       200\n",
      "           1     0.5551    0.6550    0.6009       200\n",
      "           2     0.4932    0.3600    0.4162       200\n",
      "           3     0.5792    0.5300    0.5535       200\n",
      "           4     0.9314    0.8150    0.8693       200\n",
      "           5     0.8182    0.9000    0.8571       200\n",
      "           6     0.4620    0.4250    0.4427       200\n",
      "           7     0.4455    0.4500    0.4478       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5858    0.5806    0.5796      1600\n",
      "weighted avg     0.5858    0.5806    0.5796      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4104    0.5150    0.4568       200\n",
      "           1     0.5507    0.6250    0.5855       200\n",
      "           2     0.4894    0.3450    0.4047       200\n",
      "           3     0.5778    0.5200    0.5474       200\n",
      "           4     0.9368    0.8150    0.8717       200\n",
      "           5     0.8235    0.9100    0.8646       200\n",
      "           6     0.4573    0.4550    0.4561       200\n",
      "           7     0.4396    0.4550    0.4472       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5857    0.5800    0.5792      1600\n",
      "weighted avg     0.5857    0.5800    0.5792      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4109    0.5300    0.4629       200\n",
      "           1     0.5498    0.6350    0.5893       200\n",
      "           2     0.5152    0.3400    0.4096       200\n",
      "           3     0.5819    0.5150    0.5464       200\n",
      "           4     0.9314    0.8150    0.8693       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4588    0.4450    0.4518       200\n",
      "           7     0.4346    0.4650    0.4493       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5880    0.5806    0.5797      1600\n",
      "weighted avg     0.5880    0.5806    0.5797      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=12, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4024    0.5050    0.4479       200\n",
      "           1     0.5551    0.6550    0.6009       200\n",
      "           2     0.4932    0.3600    0.4162       200\n",
      "           3     0.5819    0.5150    0.5464       200\n",
      "           4     0.9314    0.8150    0.8693       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4677    0.4350    0.4508       200\n",
      "           7     0.4333    0.4550    0.4439       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5859    0.5800    0.5793      1600\n",
      "weighted avg     0.5859    0.5800    0.5793      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 60.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4818    0.5950    0.5324       200\n",
      "           1     0.5685    0.6850    0.6213       200\n",
      "           2     0.5455    0.3900    0.4548       200\n",
      "           3     0.5988    0.5000    0.5450       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4739    0.5000    0.4866       200\n",
      "           7     0.4847    0.4750    0.4798       200\n",
      "\n",
      "    accuracy                         0.6094      1600\n",
      "   macro avg     0.6132    0.6094    0.6076      1600\n",
      "weighted avg     0.6132    0.6094    0.6076      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 60.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4741    0.5950    0.5277       200\n",
      "           1     0.5592    0.6850    0.6157       200\n",
      "           2     0.5547    0.3800    0.4510       200\n",
      "           3     0.5847    0.5350    0.5587       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4698    0.5050    0.4867       200\n",
      "           7     0.5000    0.4350    0.4652       200\n",
      "\n",
      "    accuracy                         0.6081      1600\n",
      "   macro avg     0.6119    0.6081    0.6057      1600\n",
      "weighted avg     0.6119    0.6081    0.6057      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 60.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4766    0.6100    0.5351       200\n",
      "           1     0.5679    0.6900    0.6230       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4601    0.4900    0.4746       200\n",
      "           7     0.5223    0.4100    0.4594       200\n",
      "\n",
      "    accuracy                         0.6081      1600\n",
      "   macro avg     0.6112    0.6081    0.6052      1600\n",
      "weighted avg     0.6112    0.6081    0.6052      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 60.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4733    0.5750    0.5192       200\n",
      "           1     0.5502    0.6850    0.6102       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5876    0.5200    0.5517       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4717    0.5000    0.4854       200\n",
      "           7     0.4891    0.4500    0.4688       200\n",
      "\n",
      "    accuracy                         0.6056      1600\n",
      "   macro avg     0.6093    0.6056    0.6037      1600\n",
      "weighted avg     0.6093    0.6056    0.6037      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 60.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4754    0.5800    0.5225       200\n",
      "           1     0.5638    0.6850    0.6185       200\n",
      "           2     0.5580    0.3850    0.4556       200\n",
      "           3     0.5769    0.5250    0.5497       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4591    0.5050    0.4810       200\n",
      "           7     0.4831    0.4300    0.4550       200\n",
      "\n",
      "    accuracy                         0.6050      1600\n",
      "   macro avg     0.6086    0.6050    0.6029      1600\n",
      "weighted avg     0.6086    0.6050    0.6029      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 60.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4766    0.6100    0.5351       200\n",
      "           1     0.5679    0.6900    0.6230       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4601    0.4900    0.4746       200\n",
      "           7     0.5223    0.4100    0.4594       200\n",
      "\n",
      "    accuracy                         0.6081      1600\n",
      "   macro avg     0.6112    0.6081    0.6052      1600\n",
      "weighted avg     0.6112    0.6081    0.6052      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 60.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4752    0.5750    0.5204       200\n",
      "           1     0.5685    0.6850    0.6213       200\n",
      "           2     0.5405    0.4000    0.4598       200\n",
      "           3     0.5739    0.5050    0.5372       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4692    0.4950    0.4818       200\n",
      "           7     0.4866    0.4550    0.4703       200\n",
      "\n",
      "    accuracy                         0.6056      1600\n",
      "   macro avg     0.6083    0.6056    0.6039      1600\n",
      "weighted avg     0.6083    0.6056    0.6039      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 60.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4752    0.5750    0.5204       200\n",
      "           1     0.5756    0.6850    0.6256       200\n",
      "           2     0.5442    0.4000    0.4611       200\n",
      "           3     0.5824    0.5300    0.5550       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4916    0.4400    0.4644       200\n",
      "\n",
      "    accuracy                         0.6075      1600\n",
      "   macro avg     0.6103    0.6075    0.6058      1600\n",
      "weighted avg     0.6103    0.6075    0.6058      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 60.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4747    0.6100    0.5339       200\n",
      "           1     0.5656    0.6900    0.6216       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5612    0.5500    0.5556       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4667    0.4900    0.4780       200\n",
      "           7     0.5223    0.4100    0.4594       200\n",
      "\n",
      "    accuracy                         0.6081      1600\n",
      "   macro avg     0.6111    0.6081    0.6051      1600\n",
      "weighted avg     0.6111    0.6081    0.6051      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 60.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4818    0.5950    0.5324       200\n",
      "           1     0.5685    0.6850    0.6213       200\n",
      "           2     0.5455    0.3900    0.4548       200\n",
      "           3     0.5988    0.5000    0.5450       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4739    0.5000    0.4866       200\n",
      "           7     0.4847    0.4750    0.4798       200\n",
      "\n",
      "    accuracy                         0.6094      1600\n",
      "   macro avg     0.6132    0.6094    0.6076      1600\n",
      "weighted avg     0.6132    0.6094    0.6076      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 60.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4741    0.5950    0.5277       200\n",
      "           1     0.5592    0.6850    0.6157       200\n",
      "           2     0.5547    0.3800    0.4510       200\n",
      "           3     0.5847    0.5350    0.5587       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4698    0.5050    0.4867       200\n",
      "           7     0.5000    0.4350    0.4652       200\n",
      "\n",
      "    accuracy                         0.6081      1600\n",
      "   macro avg     0.6119    0.6081    0.6057      1600\n",
      "weighted avg     0.6119    0.6081    0.6057      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 60.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4766    0.6100    0.5351       200\n",
      "           1     0.5679    0.6900    0.6230       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4601    0.4900    0.4746       200\n",
      "           7     0.5223    0.4100    0.4594       200\n",
      "\n",
      "    accuracy                         0.6081      1600\n",
      "   macro avg     0.6112    0.6081    0.6052      1600\n",
      "weighted avg     0.6112    0.6081    0.6052      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 60.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4733    0.5750    0.5192       200\n",
      "           1     0.5502    0.6850    0.6102       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5876    0.5200    0.5517       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4717    0.5000    0.4854       200\n",
      "           7     0.4891    0.4500    0.4688       200\n",
      "\n",
      "    accuracy                         0.6056      1600\n",
      "   macro avg     0.6093    0.6056    0.6037      1600\n",
      "weighted avg     0.6093    0.6056    0.6037      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 60.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4754    0.5800    0.5225       200\n",
      "           1     0.5638    0.6850    0.6185       200\n",
      "           2     0.5580    0.3850    0.4556       200\n",
      "           3     0.5769    0.5250    0.5497       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4591    0.5050    0.4810       200\n",
      "           7     0.4831    0.4300    0.4550       200\n",
      "\n",
      "    accuracy                         0.6050      1600\n",
      "   macro avg     0.6086    0.6050    0.6029      1600\n",
      "weighted avg     0.6086    0.6050    0.6029      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 60.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4766    0.6100    0.5351       200\n",
      "           1     0.5679    0.6900    0.6230       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4601    0.4900    0.4746       200\n",
      "           7     0.5223    0.4100    0.4594       200\n",
      "\n",
      "    accuracy                         0.6081      1600\n",
      "   macro avg     0.6112    0.6081    0.6052      1600\n",
      "weighted avg     0.6112    0.6081    0.6052      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 60.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4752    0.5750    0.5204       200\n",
      "           1     0.5685    0.6850    0.6213       200\n",
      "           2     0.5405    0.4000    0.4598       200\n",
      "           3     0.5739    0.5050    0.5372       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4692    0.4950    0.4818       200\n",
      "           7     0.4866    0.4550    0.4703       200\n",
      "\n",
      "    accuracy                         0.6056      1600\n",
      "   macro avg     0.6083    0.6056    0.6039      1600\n",
      "weighted avg     0.6083    0.6056    0.6039      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 60.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4752    0.5750    0.5204       200\n",
      "           1     0.5756    0.6850    0.6256       200\n",
      "           2     0.5442    0.4000    0.4611       200\n",
      "           3     0.5824    0.5300    0.5550       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4916    0.4400    0.4644       200\n",
      "\n",
      "    accuracy                         0.6075      1600\n",
      "   macro avg     0.6103    0.6075    0.6058      1600\n",
      "weighted avg     0.6103    0.6075    0.6058      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 60.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4747    0.6100    0.5339       200\n",
      "           1     0.5656    0.6900    0.6216       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5612    0.5500    0.5556       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4667    0.4900    0.4780       200\n",
      "           7     0.5223    0.4100    0.4594       200\n",
      "\n",
      "    accuracy                         0.6081      1600\n",
      "   macro avg     0.6111    0.6081    0.6051      1600\n",
      "weighted avg     0.6111    0.6081    0.6051      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 60.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4795    0.5850    0.5270       200\n",
      "           1     0.5638    0.6850    0.6185       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5964    0.4950    0.5410       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4720    0.5050    0.4879       200\n",
      "           7     0.4772    0.4700    0.4736       200\n",
      "\n",
      "    accuracy                         0.6069      1600\n",
      "   macro avg     0.6109    0.6069    0.6050      1600\n",
      "weighted avg     0.6109    0.6069    0.6050      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 60.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4760    0.5950    0.5289       200\n",
      "           1     0.5638    0.6850    0.6185       200\n",
      "           2     0.5500    0.3850    0.4529       200\n",
      "           3     0.5847    0.5350    0.5587       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4676    0.5050    0.4856       200\n",
      "           7     0.4971    0.4300    0.4611       200\n",
      "\n",
      "    accuracy                         0.6081      1600\n",
      "   macro avg     0.6115    0.6081    0.6058      1600\n",
      "weighted avg     0.6115    0.6081    0.6058      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 60.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4747    0.6100    0.5339       200\n",
      "           1     0.5679    0.6900    0.6230       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4623    0.4900    0.4757       200\n",
      "           7     0.5223    0.4100    0.4594       200\n",
      "\n",
      "    accuracy                         0.6081      1600\n",
      "   macro avg     0.6112    0.6081    0.6052      1600\n",
      "weighted avg     0.6112    0.6081    0.6052      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 60.62%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4733    0.5750    0.5192       200\n",
      "           1     0.5547    0.6850    0.6130       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5876    0.5200    0.5517       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4720    0.5050    0.4879       200\n",
      "           7     0.4891    0.4500    0.4688       200\n",
      "\n",
      "    accuracy                         0.6062      1600\n",
      "   macro avg     0.6099    0.6062    0.6044      1600\n",
      "weighted avg     0.6099    0.6062    0.6044      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 60.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4754    0.5800    0.5225       200\n",
      "           1     0.5638    0.6850    0.6185       200\n",
      "           2     0.5580    0.3850    0.4556       200\n",
      "           3     0.5769    0.5250    0.5497       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4591    0.5050    0.4810       200\n",
      "           7     0.4831    0.4300    0.4550       200\n",
      "\n",
      "    accuracy                         0.6050      1600\n",
      "   macro avg     0.6086    0.6050    0.6029      1600\n",
      "weighted avg     0.6086    0.6050    0.6029      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 60.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4747    0.6100    0.5339       200\n",
      "           1     0.5679    0.6900    0.6230       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4623    0.4900    0.4757       200\n",
      "           7     0.5223    0.4100    0.4594       200\n",
      "\n",
      "    accuracy                         0.6081      1600\n",
      "   macro avg     0.6112    0.6081    0.6052      1600\n",
      "weighted avg     0.6112    0.6081    0.6052      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 60.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4752    0.5750    0.5204       200\n",
      "           1     0.5685    0.6850    0.6213       200\n",
      "           2     0.5405    0.4000    0.4598       200\n",
      "           3     0.5739    0.5050    0.5372       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4692    0.4950    0.4818       200\n",
      "           7     0.4866    0.4550    0.4703       200\n",
      "\n",
      "    accuracy                         0.6056      1600\n",
      "   macro avg     0.6083    0.6056    0.6039      1600\n",
      "weighted avg     0.6083    0.6056    0.6039      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 60.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4752    0.5750    0.5204       200\n",
      "           1     0.5756    0.6850    0.6256       200\n",
      "           2     0.5442    0.4000    0.4611       200\n",
      "           3     0.5824    0.5300    0.5550       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4916    0.4400    0.4644       200\n",
      "\n",
      "    accuracy                         0.6075      1600\n",
      "   macro avg     0.6103    0.6075    0.6058      1600\n",
      "weighted avg     0.6103    0.6075    0.6058      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 60.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4747    0.6100    0.5339       200\n",
      "           1     0.5656    0.6900    0.6216       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5612    0.5500    0.5556       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4667    0.4900    0.4780       200\n",
      "           7     0.5223    0.4100    0.4594       200\n",
      "\n",
      "    accuracy                         0.6081      1600\n",
      "   macro avg     0.6111    0.6081    0.6051      1600\n",
      "weighted avg     0.6111    0.6081    0.6051      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4218    0.6200    0.5020       200\n",
      "           1     0.5569    0.7100    0.6242       200\n",
      "           2     0.6118    0.2600    0.3649       200\n",
      "           3     0.5234    0.5600    0.5411       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8802    0.8450    0.8622       200\n",
      "           6     0.4667    0.4900    0.4780       200\n",
      "           7     0.5274    0.3850    0.4451       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.6070    0.5944    0.5867      1600\n",
      "weighted avg     0.6070    0.5944    0.5867      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4228    0.6300    0.5060       200\n",
      "           1     0.5508    0.7050    0.6184       200\n",
      "           2     0.6098    0.2500    0.3546       200\n",
      "           3     0.5263    0.5500    0.5379       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8802    0.8450    0.8622       200\n",
      "           6     0.4641    0.4850    0.4743       200\n",
      "           7     0.5333    0.4000    0.4571       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.6069    0.5938    0.5859      1600\n",
      "weighted avg     0.6069    0.5938    0.5859      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4238    0.6400    0.5100       200\n",
      "           1     0.5486    0.7050    0.6171       200\n",
      "           2     0.6203    0.2450    0.3513       200\n",
      "           3     0.5333    0.5600    0.5463       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4645    0.4900    0.4769       200\n",
      "           7     0.5411    0.3950    0.4566       200\n",
      "\n",
      "    accuracy                         0.5956      1600\n",
      "   macro avg     0.6105    0.5956    0.5874      1600\n",
      "weighted avg     0.6105    0.5956    0.5874      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4247    0.6350    0.5090       200\n",
      "           1     0.5595    0.7050    0.6239       200\n",
      "           2     0.6163    0.2650    0.3706       200\n",
      "           3     0.5362    0.5550    0.5455       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8802    0.8450    0.8622       200\n",
      "           6     0.4667    0.4900    0.4780       200\n",
      "           7     0.5267    0.3950    0.4514       200\n",
      "\n",
      "    accuracy                         0.5969      1600\n",
      "   macro avg     0.6097    0.5969    0.5896      1600\n",
      "weighted avg     0.6097    0.5969    0.5896      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4214    0.6300    0.5050       200\n",
      "           1     0.5529    0.7050    0.6198       200\n",
      "           2     0.6098    0.2500    0.3546       200\n",
      "           3     0.5263    0.5500    0.5379       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8802    0.8450    0.8622       200\n",
      "           6     0.4641    0.4850    0.4743       200\n",
      "           7     0.5333    0.4000    0.4571       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.6070    0.5938    0.5859      1600\n",
      "weighted avg     0.6070    0.5938    0.5859      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4238    0.6400    0.5100       200\n",
      "           1     0.5486    0.7050    0.6171       200\n",
      "           2     0.6203    0.2450    0.3513       200\n",
      "           3     0.5333    0.5600    0.5463       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4645    0.4900    0.4769       200\n",
      "           7     0.5411    0.3950    0.4566       200\n",
      "\n",
      "    accuracy                         0.5956      1600\n",
      "   macro avg     0.6105    0.5956    0.5874      1600\n",
      "weighted avg     0.6105    0.5956    0.5874      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4247    0.6350    0.5090       200\n",
      "           1     0.5595    0.7050    0.6239       200\n",
      "           2     0.6190    0.2600    0.3662       200\n",
      "           3     0.5362    0.5550    0.5455       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8802    0.8450    0.8622       200\n",
      "           6     0.4670    0.4950    0.4806       200\n",
      "           7     0.5333    0.4000    0.4571       200\n",
      "\n",
      "    accuracy                         0.5975      1600\n",
      "   macro avg     0.6110    0.5975    0.5901      1600\n",
      "weighted avg     0.6110    0.5975    0.5901      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4214    0.6300    0.5050       200\n",
      "           1     0.5529    0.7050    0.6198       200\n",
      "           2     0.6173    0.2500    0.3559       200\n",
      "           3     0.5286    0.5550    0.5415       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8802    0.8450    0.8622       200\n",
      "           6     0.4645    0.4900    0.4769       200\n",
      "           7     0.5405    0.4000    0.4598       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.6091    0.5950    0.5872      1600\n",
      "weighted avg     0.6091    0.5950    0.5872      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4238    0.6400    0.5100       200\n",
      "           1     0.5486    0.7050    0.6171       200\n",
      "           2     0.6203    0.2450    0.3513       200\n",
      "           3     0.5288    0.5500    0.5392       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4645    0.4900    0.4769       200\n",
      "           7     0.5405    0.4000    0.4598       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.6099    0.5950    0.5869      1600\n",
      "weighted avg     0.6099    0.5950    0.5869      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 60.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4818    0.5950    0.5324       200\n",
      "           1     0.5685    0.6850    0.6213       200\n",
      "           2     0.5455    0.3900    0.4548       200\n",
      "           3     0.5988    0.5000    0.5450       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4739    0.5000    0.4866       200\n",
      "           7     0.4847    0.4750    0.4798       200\n",
      "\n",
      "    accuracy                         0.6094      1600\n",
      "   macro avg     0.6132    0.6094    0.6076      1600\n",
      "weighted avg     0.6132    0.6094    0.6076      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 60.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4741    0.5950    0.5277       200\n",
      "           1     0.5592    0.6850    0.6157       200\n",
      "           2     0.5547    0.3800    0.4510       200\n",
      "           3     0.5847    0.5350    0.5587       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4698    0.5050    0.4867       200\n",
      "           7     0.5000    0.4350    0.4652       200\n",
      "\n",
      "    accuracy                         0.6081      1600\n",
      "   macro avg     0.6119    0.6081    0.6057      1600\n",
      "weighted avg     0.6119    0.6081    0.6057      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 60.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4766    0.6100    0.5351       200\n",
      "           1     0.5679    0.6900    0.6230       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4601    0.4900    0.4746       200\n",
      "           7     0.5223    0.4100    0.4594       200\n",
      "\n",
      "    accuracy                         0.6081      1600\n",
      "   macro avg     0.6112    0.6081    0.6052      1600\n",
      "weighted avg     0.6112    0.6081    0.6052      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 60.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4733    0.5750    0.5192       200\n",
      "           1     0.5502    0.6850    0.6102       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5876    0.5200    0.5517       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4717    0.5000    0.4854       200\n",
      "           7     0.4891    0.4500    0.4688       200\n",
      "\n",
      "    accuracy                         0.6056      1600\n",
      "   macro avg     0.6093    0.6056    0.6037      1600\n",
      "weighted avg     0.6093    0.6056    0.6037      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 60.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4754    0.5800    0.5225       200\n",
      "           1     0.5638    0.6850    0.6185       200\n",
      "           2     0.5580    0.3850    0.4556       200\n",
      "           3     0.5769    0.5250    0.5497       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4591    0.5050    0.4810       200\n",
      "           7     0.4831    0.4300    0.4550       200\n",
      "\n",
      "    accuracy                         0.6050      1600\n",
      "   macro avg     0.6086    0.6050    0.6029      1600\n",
      "weighted avg     0.6086    0.6050    0.6029      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 60.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4766    0.6100    0.5351       200\n",
      "           1     0.5679    0.6900    0.6230       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4601    0.4900    0.4746       200\n",
      "           7     0.5223    0.4100    0.4594       200\n",
      "\n",
      "    accuracy                         0.6081      1600\n",
      "   macro avg     0.6112    0.6081    0.6052      1600\n",
      "weighted avg     0.6112    0.6081    0.6052      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 60.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4752    0.5750    0.5204       200\n",
      "           1     0.5685    0.6850    0.6213       200\n",
      "           2     0.5405    0.4000    0.4598       200\n",
      "           3     0.5739    0.5050    0.5372       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4692    0.4950    0.4818       200\n",
      "           7     0.4866    0.4550    0.4703       200\n",
      "\n",
      "    accuracy                         0.6056      1600\n",
      "   macro avg     0.6083    0.6056    0.6039      1600\n",
      "weighted avg     0.6083    0.6056    0.6039      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 60.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4752    0.5750    0.5204       200\n",
      "           1     0.5756    0.6850    0.6256       200\n",
      "           2     0.5442    0.4000    0.4611       200\n",
      "           3     0.5824    0.5300    0.5550       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4916    0.4400    0.4644       200\n",
      "\n",
      "    accuracy                         0.6075      1600\n",
      "   macro avg     0.6103    0.6075    0.6058      1600\n",
      "weighted avg     0.6103    0.6075    0.6058      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=50, max_features=None, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 60.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4747    0.6100    0.5339       200\n",
      "           1     0.5656    0.6900    0.6216       200\n",
      "           2     0.5461    0.3850    0.4516       200\n",
      "           3     0.5612    0.5500    0.5556       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4667    0.4900    0.4780       200\n",
      "           7     0.5223    0.4100    0.4594       200\n",
      "\n",
      "    accuracy                         0.6081      1600\n",
      "   macro avg     0.6111    0.6081    0.6051      1600\n",
      "weighted avg     0.6111    0.6081    0.6051      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4297    0.5350    0.4766       200\n",
      "           1     0.5776    0.6700    0.6204       200\n",
      "           2     0.5497    0.4150    0.4729       200\n",
      "           3     0.5706    0.5050    0.5358       200\n",
      "           4     0.8502    0.8800    0.8649       200\n",
      "           5     0.8639    0.8250    0.8440       200\n",
      "           6     0.4697    0.4650    0.4673       200\n",
      "           7     0.4513    0.4400    0.4456       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5953    0.5919    0.5909      1600\n",
      "weighted avg     0.5953    0.5919    0.5909      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4219    0.5400    0.4737       200\n",
      "           1     0.5660    0.6650    0.6115       200\n",
      "           2     0.5168    0.3850    0.4413       200\n",
      "           3     0.5640    0.4850    0.5215       200\n",
      "           4     0.8443    0.8950    0.8689       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4822    0.4750    0.4786       200\n",
      "           7     0.4531    0.4350    0.4439       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5893    0.5863    0.5846      1600\n",
      "weighted avg     0.5893    0.5863    0.5846      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4229    0.5350    0.4724       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5203    0.3850    0.4425       200\n",
      "           3     0.5489    0.5050    0.5260       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5879    0.5856    0.5839      1600\n",
      "weighted avg     0.5879    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5450    0.4802       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5214    0.3650    0.4294       200\n",
      "           3     0.5591    0.5200    0.5389       200\n",
      "           4     0.8451    0.9000    0.8717       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4643    0.4550    0.4596       200\n",
      "           7     0.4497    0.4250    0.4370       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5887    0.5869    0.5843      1600\n",
      "weighted avg     0.5887    0.5869    0.5843      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4147    0.5350    0.4672       200\n",
      "           1     0.5678    0.6700    0.6147       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5556    0.5000    0.5263       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4767    0.4600    0.4682       200\n",
      "           7     0.4492    0.4200    0.4341       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5855    0.5831    0.5810      1600\n",
      "weighted avg     0.5855    0.5831    0.5810      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4229    0.5350    0.4724       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5203    0.3850    0.4425       200\n",
      "           3     0.5489    0.5050    0.5260       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5879    0.5856    0.5839      1600\n",
      "weighted avg     0.5879    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4208    0.5450    0.4749       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5241    0.3800    0.4406       200\n",
      "           3     0.5562    0.4950    0.5238       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8549    0.8250    0.8397       200\n",
      "           6     0.4821    0.4700    0.4759       200\n",
      "           7     0.4381    0.4250    0.4315       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5883    0.5856    0.5839      1600\n",
      "weighted avg     0.5883    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4215    0.5500    0.4772       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5168    0.3850    0.4413       200\n",
      "           3     0.5593    0.4950    0.5252       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8632    0.8200    0.8410       200\n",
      "           6     0.4792    0.4600    0.4694       200\n",
      "           7     0.4456    0.4300    0.4377       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5890    0.5862    0.5846      1600\n",
      "weighted avg     0.5890    0.5863    0.5846      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4269    0.5400    0.4768       200\n",
      "           1     0.5671    0.6550    0.6079       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5464    0.5000    0.5222       200\n",
      "           4     0.8551    0.8850    0.8698       200\n",
      "           5     0.8594    0.8250    0.8418       200\n",
      "           6     0.4794    0.4650    0.4721       200\n",
      "           7     0.4485    0.4350    0.4416       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5871    0.5850    0.5832      1600\n",
      "weighted avg     0.5871    0.5850    0.5832      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4320    0.5400    0.4800       200\n",
      "           1     0.5751    0.6700    0.6189       200\n",
      "           2     0.5503    0.4100    0.4699       200\n",
      "           3     0.5682    0.5000    0.5319       200\n",
      "           4     0.8502    0.8800    0.8649       200\n",
      "           5     0.8639    0.8250    0.8440       200\n",
      "           6     0.4650    0.4650    0.4650       200\n",
      "           7     0.4536    0.4400    0.4467       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5948    0.5913    0.5902      1600\n",
      "weighted avg     0.5948    0.5913    0.5902      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4252    0.5400    0.4758       200\n",
      "           1     0.5708    0.6650    0.6143       200\n",
      "           2     0.5263    0.4000    0.4545       200\n",
      "           3     0.5607    0.4850    0.5201       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4798    0.4750    0.4774       200\n",
      "           7     0.4579    0.4350    0.4462       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5909    0.5881    0.5865      1600\n",
      "weighted avg     0.5909    0.5881    0.5865      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4246    0.5350    0.4735       200\n",
      "           1     0.5696    0.6550    0.6093       200\n",
      "           2     0.5170    0.3800    0.4380       200\n",
      "           3     0.5519    0.5050    0.5274       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4508    0.4350    0.4427       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5879    0.5856    0.5838      1600\n",
      "weighted avg     0.5879    0.5856    0.5838      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5450    0.4791       200\n",
      "           1     0.5726    0.6700    0.6175       200\n",
      "           2     0.5139    0.3700    0.4302       200\n",
      "           3     0.5561    0.5200    0.5375       200\n",
      "           4     0.8451    0.9000    0.8717       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4615    0.4500    0.4557       200\n",
      "           7     0.4541    0.4200    0.4364       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5871    0.5856    0.5832      1600\n",
      "weighted avg     0.5871    0.5856    0.5832      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4163    0.5350    0.4683       200\n",
      "           1     0.5708    0.6650    0.6143       200\n",
      "           2     0.5068    0.3750    0.4310       200\n",
      "           3     0.5650    0.5000    0.5305       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4718    0.4600    0.4658       200\n",
      "           7     0.4474    0.4250    0.4359       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5856    0.5831    0.5812      1600\n",
      "weighted avg     0.5856    0.5831    0.5812      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4246    0.5350    0.4735       200\n",
      "           1     0.5696    0.6550    0.6093       200\n",
      "           2     0.5170    0.3800    0.4380       200\n",
      "           3     0.5519    0.5050    0.5274       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4508    0.4350    0.4427       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5879    0.5856    0.5838      1600\n",
      "weighted avg     0.5879    0.5856    0.5838      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4241    0.5450    0.4770       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5241    0.3800    0.4406       200\n",
      "           3     0.5537    0.4900    0.5199       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8549    0.8250    0.8397       200\n",
      "           6     0.4821    0.4700    0.4759       200\n",
      "           7     0.4365    0.4300    0.4332       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5882    0.5856    0.5839      1600\n",
      "weighted avg     0.5882    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4208    0.5450    0.4749       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5170    0.3800    0.4380       200\n",
      "           3     0.5543    0.4850    0.5173       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8632    0.8200    0.8410       200\n",
      "           6     0.4792    0.4600    0.4694       200\n",
      "           7     0.4422    0.4400    0.4411       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5879    0.5850    0.5834      1600\n",
      "weighted avg     0.5879    0.5850    0.5834      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4269    0.5400    0.4768       200\n",
      "           1     0.5671    0.6550    0.6079       200\n",
      "           2     0.5102    0.3750    0.4323       200\n",
      "           3     0.5464    0.5000    0.5222       200\n",
      "           4     0.8551    0.8850    0.8698       200\n",
      "           5     0.8594    0.8250    0.8418       200\n",
      "           6     0.4794    0.4650    0.4721       200\n",
      "           7     0.4508    0.4350    0.4427       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5869    0.5850    0.5832      1600\n",
      "weighted avg     0.5869    0.5850    0.5832      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4252    0.5400    0.4758       200\n",
      "           1     0.5714    0.6600    0.6125       200\n",
      "           2     0.5099    0.3850    0.4387       200\n",
      "           3     0.5426    0.5100    0.5258       200\n",
      "           4     0.8627    0.8800    0.8713       200\n",
      "           5     0.8608    0.8350    0.8477       200\n",
      "           6     0.4792    0.4600    0.4694       200\n",
      "           7     0.4462    0.4150    0.4301       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5873    0.5856    0.5839      1600\n",
      "weighted avg     0.5873    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4280    0.5500    0.4814       200\n",
      "           1     0.5739    0.6600    0.6140       200\n",
      "           2     0.5033    0.3800    0.4330       200\n",
      "           3     0.5593    0.4950    0.5252       200\n",
      "           4     0.8565    0.8950    0.8753       200\n",
      "           5     0.8684    0.8250    0.8462       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4450    0.4250    0.4348       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5889    0.5869    0.5851      1600\n",
      "weighted avg     0.5889    0.5869    0.5851      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4258    0.5450    0.4781       200\n",
      "           1     0.5739    0.6600    0.6140       200\n",
      "           2     0.5000    0.3800    0.4318       200\n",
      "           3     0.5580    0.5050    0.5302       200\n",
      "           4     0.8524    0.8950    0.8732       200\n",
      "           5     0.8730    0.8250    0.8483       200\n",
      "           6     0.4789    0.4550    0.4667       200\n",
      "           7     0.4531    0.4350    0.4439       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5894    0.5875    0.5858      1600\n",
      "weighted avg     0.5894    0.5875    0.5858      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5450    0.4791       200\n",
      "           1     0.5733    0.6650    0.6157       200\n",
      "           2     0.5033    0.3800    0.4330       200\n",
      "           3     0.5525    0.5000    0.5249       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8632    0.8200    0.8410       200\n",
      "           6     0.4762    0.4500    0.4627       200\n",
      "           7     0.4427    0.4250    0.4337       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5858    0.5844    0.5823      1600\n",
      "weighted avg     0.5858    0.5844    0.5823      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4219    0.5400    0.4737       200\n",
      "           1     0.5739    0.6600    0.6140       200\n",
      "           2     0.4967    0.3800    0.4306       200\n",
      "           3     0.5480    0.4850    0.5146       200\n",
      "           4     0.8443    0.8950    0.8689       200\n",
      "           5     0.8670    0.8150    0.8402       200\n",
      "           6     0.4737    0.4500    0.4615       200\n",
      "           7     0.4433    0.4300    0.4365       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5836    0.5819    0.5800      1600\n",
      "weighted avg     0.5836    0.5819    0.5800      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4258    0.5450    0.4781       200\n",
      "           1     0.5739    0.6600    0.6140       200\n",
      "           2     0.5000    0.3800    0.4318       200\n",
      "           3     0.5580    0.5050    0.5302       200\n",
      "           4     0.8524    0.8950    0.8732       200\n",
      "           5     0.8730    0.8250    0.8483       200\n",
      "           6     0.4789    0.4550    0.4667       200\n",
      "           7     0.4531    0.4350    0.4439       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5894    0.5875    0.5858      1600\n",
      "weighted avg     0.5894    0.5875    0.5858      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4241    0.5450    0.4770       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5099    0.3850    0.4387       200\n",
      "           3     0.5600    0.4900    0.5227       200\n",
      "           4     0.8634    0.8850    0.8741       200\n",
      "           5     0.8660    0.8400    0.8528       200\n",
      "           6     0.4767    0.4600    0.4682       200\n",
      "           7     0.4359    0.4250    0.4304       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5893    0.5869    0.5853      1600\n",
      "weighted avg     0.5893    0.5869    0.5853      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4241    0.5450    0.4770       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5099    0.3850    0.4387       200\n",
      "           3     0.5621    0.4750    0.5149       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4742    0.4600    0.4670       200\n",
      "           7     0.4400    0.4400    0.4400       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5904    0.5875    0.5860      1600\n",
      "weighted avg     0.5904    0.5875    0.5860      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4219    0.5400    0.4737       200\n",
      "           1     0.5764    0.6600    0.6154       200\n",
      "           2     0.4967    0.3800    0.4306       200\n",
      "           3     0.5587    0.5000    0.5277       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4740    0.4550    0.4643       200\n",
      "           7     0.4479    0.4300    0.4388       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5887    0.5869    0.5853      1600\n",
      "weighted avg     0.5887    0.5869    0.5853      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4328    0.5800    0.4957       200\n",
      "           1     0.5733    0.6650    0.6157       200\n",
      "           2     0.5303    0.3500    0.4217       200\n",
      "           3     0.5683    0.5200    0.5431       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4680    0.4750    0.4715       200\n",
      "           7     0.4783    0.4400    0.4583       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.5968    0.5931    0.5906      1600\n",
      "weighted avg     0.5968    0.5931    0.5906      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4318    0.5700    0.4914       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5224    0.3500    0.4192       200\n",
      "           3     0.5645    0.5250    0.5440       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4677    0.4700    0.4688       200\n",
      "           7     0.4780    0.4350    0.4555       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5953    0.5925    0.5898      1600\n",
      "weighted avg     0.5953    0.5925    0.5898      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4398    0.5850    0.5021       200\n",
      "           1     0.5720    0.6750    0.6193       200\n",
      "           2     0.5420    0.3550    0.4290       200\n",
      "           3     0.5585    0.5250    0.5412       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8608    0.8350    0.8477       200\n",
      "           6     0.4673    0.4650    0.4662       200\n",
      "           7     0.4696    0.4250    0.4462       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.5961    0.5931    0.5901      1600\n",
      "weighted avg     0.5961    0.5931    0.5901      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4345    0.5800    0.4968       200\n",
      "           1     0.5733    0.6650    0.6157       200\n",
      "           2     0.5263    0.3500    0.4204       200\n",
      "           3     0.5585    0.5250    0.5412       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8608    0.8350    0.8477       200\n",
      "           6     0.4700    0.4700    0.4700       200\n",
      "           7     0.4751    0.4300    0.4514       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5946    0.5919    0.5891      1600\n",
      "weighted avg     0.5946    0.5919    0.5891      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4340    0.5750    0.4946       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5263    0.3500    0.4204       200\n",
      "           3     0.5645    0.5250    0.5440       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4677    0.4700    0.4688       200\n",
      "           7     0.4780    0.4350    0.4555       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.5961    0.5931    0.5904      1600\n",
      "weighted avg     0.5961    0.5931    0.5904      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4398    0.5850    0.5021       200\n",
      "           1     0.5720    0.6750    0.6193       200\n",
      "           2     0.5420    0.3550    0.4290       200\n",
      "           3     0.5585    0.5250    0.5412       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8608    0.8350    0.8477       200\n",
      "           6     0.4673    0.4650    0.4662       200\n",
      "           7     0.4696    0.4250    0.4462       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.5961    0.5931    0.5901      1600\n",
      "weighted avg     0.5961    0.5931    0.5901      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4323    0.5750    0.4936       200\n",
      "           1     0.5726    0.6700    0.6175       200\n",
      "           2     0.5338    0.3550    0.4264       200\n",
      "           3     0.5497    0.5250    0.5371       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8608    0.8350    0.8477       200\n",
      "           6     0.4747    0.4700    0.4724       200\n",
      "           7     0.4749    0.4250    0.4485       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5947    0.5919    0.5890      1600\n",
      "weighted avg     0.5947    0.5919    0.5890      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4340    0.5750    0.4946       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5561    0.5200    0.5375       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4724    0.4700    0.4712       200\n",
      "           7     0.4725    0.4300    0.4503       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5954    0.5925    0.5898      1600\n",
      "weighted avg     0.5954    0.5925    0.5898      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4398    0.5850    0.5021       200\n",
      "           1     0.5720    0.6750    0.6193       200\n",
      "           2     0.5385    0.3500    0.4242       200\n",
      "           3     0.5585    0.5250    0.5412       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8608    0.8350    0.8477       200\n",
      "           6     0.4650    0.4650    0.4650       200\n",
      "           7     0.4696    0.4250    0.4462       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5954    0.5925    0.5894      1600\n",
      "weighted avg     0.5954    0.5925    0.5894      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4297    0.5350    0.4766       200\n",
      "           1     0.5776    0.6700    0.6204       200\n",
      "           2     0.5497    0.4150    0.4729       200\n",
      "           3     0.5706    0.5050    0.5358       200\n",
      "           4     0.8502    0.8800    0.8649       200\n",
      "           5     0.8639    0.8250    0.8440       200\n",
      "           6     0.4697    0.4650    0.4673       200\n",
      "           7     0.4513    0.4400    0.4456       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5953    0.5919    0.5909      1600\n",
      "weighted avg     0.5953    0.5919    0.5909      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4219    0.5400    0.4737       200\n",
      "           1     0.5660    0.6650    0.6115       200\n",
      "           2     0.5168    0.3850    0.4413       200\n",
      "           3     0.5640    0.4850    0.5215       200\n",
      "           4     0.8443    0.8950    0.8689       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4822    0.4750    0.4786       200\n",
      "           7     0.4531    0.4350    0.4439       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5893    0.5863    0.5846      1600\n",
      "weighted avg     0.5893    0.5863    0.5846      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4229    0.5350    0.4724       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5203    0.3850    0.4425       200\n",
      "           3     0.5489    0.5050    0.5260       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5879    0.5856    0.5839      1600\n",
      "weighted avg     0.5879    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5450    0.4802       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5214    0.3650    0.4294       200\n",
      "           3     0.5591    0.5200    0.5389       200\n",
      "           4     0.8451    0.9000    0.8717       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4643    0.4550    0.4596       200\n",
      "           7     0.4497    0.4250    0.4370       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5887    0.5869    0.5843      1600\n",
      "weighted avg     0.5887    0.5869    0.5843      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4147    0.5350    0.4672       200\n",
      "           1     0.5678    0.6700    0.6147       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5556    0.5000    0.5263       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4767    0.4600    0.4682       200\n",
      "           7     0.4492    0.4200    0.4341       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5855    0.5831    0.5810      1600\n",
      "weighted avg     0.5855    0.5831    0.5810      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4229    0.5350    0.4724       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5203    0.3850    0.4425       200\n",
      "           3     0.5489    0.5050    0.5260       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5879    0.5856    0.5839      1600\n",
      "weighted avg     0.5879    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4208    0.5450    0.4749       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5241    0.3800    0.4406       200\n",
      "           3     0.5562    0.4950    0.5238       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8549    0.8250    0.8397       200\n",
      "           6     0.4821    0.4700    0.4759       200\n",
      "           7     0.4381    0.4250    0.4315       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5883    0.5856    0.5839      1600\n",
      "weighted avg     0.5883    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4215    0.5500    0.4772       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5168    0.3850    0.4413       200\n",
      "           3     0.5593    0.4950    0.5252       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8632    0.8200    0.8410       200\n",
      "           6     0.4792    0.4600    0.4694       200\n",
      "           7     0.4456    0.4300    0.4377       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5890    0.5862    0.5846      1600\n",
      "weighted avg     0.5890    0.5863    0.5846      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=sqrt, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4269    0.5400    0.4768       200\n",
      "           1     0.5671    0.6550    0.6079       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5464    0.5000    0.5222       200\n",
      "           4     0.8551    0.8850    0.8698       200\n",
      "           5     0.8594    0.8250    0.8418       200\n",
      "           6     0.4794    0.4650    0.4721       200\n",
      "           7     0.4485    0.4350    0.4416       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5871    0.5850    0.5832      1600\n",
      "weighted avg     0.5871    0.5850    0.5832      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4297    0.5350    0.4766       200\n",
      "           1     0.5776    0.6700    0.6204       200\n",
      "           2     0.5497    0.4150    0.4729       200\n",
      "           3     0.5706    0.5050    0.5358       200\n",
      "           4     0.8502    0.8800    0.8649       200\n",
      "           5     0.8639    0.8250    0.8440       200\n",
      "           6     0.4697    0.4650    0.4673       200\n",
      "           7     0.4513    0.4400    0.4456       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5953    0.5919    0.5909      1600\n",
      "weighted avg     0.5953    0.5919    0.5909      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4219    0.5400    0.4737       200\n",
      "           1     0.5660    0.6650    0.6115       200\n",
      "           2     0.5168    0.3850    0.4413       200\n",
      "           3     0.5640    0.4850    0.5215       200\n",
      "           4     0.8443    0.8950    0.8689       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4822    0.4750    0.4786       200\n",
      "           7     0.4531    0.4350    0.4439       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5893    0.5863    0.5846      1600\n",
      "weighted avg     0.5893    0.5863    0.5846      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4229    0.5350    0.4724       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5203    0.3850    0.4425       200\n",
      "           3     0.5489    0.5050    0.5260       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5879    0.5856    0.5839      1600\n",
      "weighted avg     0.5879    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5450    0.4802       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5214    0.3650    0.4294       200\n",
      "           3     0.5591    0.5200    0.5389       200\n",
      "           4     0.8451    0.9000    0.8717       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4643    0.4550    0.4596       200\n",
      "           7     0.4497    0.4250    0.4370       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5887    0.5869    0.5843      1600\n",
      "weighted avg     0.5887    0.5869    0.5843      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4147    0.5350    0.4672       200\n",
      "           1     0.5678    0.6700    0.6147       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5556    0.5000    0.5263       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4767    0.4600    0.4682       200\n",
      "           7     0.4492    0.4200    0.4341       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5855    0.5831    0.5810      1600\n",
      "weighted avg     0.5855    0.5831    0.5810      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4229    0.5350    0.4724       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5203    0.3850    0.4425       200\n",
      "           3     0.5489    0.5050    0.5260       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5879    0.5856    0.5839      1600\n",
      "weighted avg     0.5879    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4208    0.5450    0.4749       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5241    0.3800    0.4406       200\n",
      "           3     0.5562    0.4950    0.5238       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8549    0.8250    0.8397       200\n",
      "           6     0.4821    0.4700    0.4759       200\n",
      "           7     0.4381    0.4250    0.4315       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5883    0.5856    0.5839      1600\n",
      "weighted avg     0.5883    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4215    0.5500    0.4772       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5168    0.3850    0.4413       200\n",
      "           3     0.5593    0.4950    0.5252       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8632    0.8200    0.8410       200\n",
      "           6     0.4792    0.4600    0.4694       200\n",
      "           7     0.4456    0.4300    0.4377       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5890    0.5862    0.5846      1600\n",
      "weighted avg     0.5890    0.5863    0.5846      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4269    0.5400    0.4768       200\n",
      "           1     0.5671    0.6550    0.6079       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5464    0.5000    0.5222       200\n",
      "           4     0.8551    0.8850    0.8698       200\n",
      "           5     0.8594    0.8250    0.8418       200\n",
      "           6     0.4794    0.4650    0.4721       200\n",
      "           7     0.4485    0.4350    0.4416       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5871    0.5850    0.5832      1600\n",
      "weighted avg     0.5871    0.5850    0.5832      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4320    0.5400    0.4800       200\n",
      "           1     0.5751    0.6700    0.6189       200\n",
      "           2     0.5503    0.4100    0.4699       200\n",
      "           3     0.5682    0.5000    0.5319       200\n",
      "           4     0.8502    0.8800    0.8649       200\n",
      "           5     0.8639    0.8250    0.8440       200\n",
      "           6     0.4650    0.4650    0.4650       200\n",
      "           7     0.4536    0.4400    0.4467       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5948    0.5913    0.5902      1600\n",
      "weighted avg     0.5948    0.5913    0.5902      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4252    0.5400    0.4758       200\n",
      "           1     0.5708    0.6650    0.6143       200\n",
      "           2     0.5263    0.4000    0.4545       200\n",
      "           3     0.5607    0.4850    0.5201       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4798    0.4750    0.4774       200\n",
      "           7     0.4579    0.4350    0.4462       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5909    0.5881    0.5865      1600\n",
      "weighted avg     0.5909    0.5881    0.5865      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4246    0.5350    0.4735       200\n",
      "           1     0.5696    0.6550    0.6093       200\n",
      "           2     0.5170    0.3800    0.4380       200\n",
      "           3     0.5519    0.5050    0.5274       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4508    0.4350    0.4427       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5879    0.5856    0.5838      1600\n",
      "weighted avg     0.5879    0.5856    0.5838      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5450    0.4791       200\n",
      "           1     0.5726    0.6700    0.6175       200\n",
      "           2     0.5139    0.3700    0.4302       200\n",
      "           3     0.5561    0.5200    0.5375       200\n",
      "           4     0.8451    0.9000    0.8717       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4615    0.4500    0.4557       200\n",
      "           7     0.4541    0.4200    0.4364       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5871    0.5856    0.5832      1600\n",
      "weighted avg     0.5871    0.5856    0.5832      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4163    0.5350    0.4683       200\n",
      "           1     0.5708    0.6650    0.6143       200\n",
      "           2     0.5068    0.3750    0.4310       200\n",
      "           3     0.5650    0.5000    0.5305       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4718    0.4600    0.4658       200\n",
      "           7     0.4474    0.4250    0.4359       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5856    0.5831    0.5812      1600\n",
      "weighted avg     0.5856    0.5831    0.5812      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4246    0.5350    0.4735       200\n",
      "           1     0.5696    0.6550    0.6093       200\n",
      "           2     0.5170    0.3800    0.4380       200\n",
      "           3     0.5519    0.5050    0.5274       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4508    0.4350    0.4427       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5879    0.5856    0.5838      1600\n",
      "weighted avg     0.5879    0.5856    0.5838      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4241    0.5450    0.4770       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5241    0.3800    0.4406       200\n",
      "           3     0.5537    0.4900    0.5199       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8549    0.8250    0.8397       200\n",
      "           6     0.4821    0.4700    0.4759       200\n",
      "           7     0.4365    0.4300    0.4332       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5882    0.5856    0.5839      1600\n",
      "weighted avg     0.5882    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4208    0.5450    0.4749       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5170    0.3800    0.4380       200\n",
      "           3     0.5543    0.4850    0.5173       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8632    0.8200    0.8410       200\n",
      "           6     0.4792    0.4600    0.4694       200\n",
      "           7     0.4422    0.4400    0.4411       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5879    0.5850    0.5834      1600\n",
      "weighted avg     0.5879    0.5850    0.5834      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4269    0.5400    0.4768       200\n",
      "           1     0.5671    0.6550    0.6079       200\n",
      "           2     0.5102    0.3750    0.4323       200\n",
      "           3     0.5464    0.5000    0.5222       200\n",
      "           4     0.8551    0.8850    0.8698       200\n",
      "           5     0.8594    0.8250    0.8418       200\n",
      "           6     0.4794    0.4650    0.4721       200\n",
      "           7     0.4508    0.4350    0.4427       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5869    0.5850    0.5832      1600\n",
      "weighted avg     0.5869    0.5850    0.5832      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4252    0.5400    0.4758       200\n",
      "           1     0.5714    0.6600    0.6125       200\n",
      "           2     0.5099    0.3850    0.4387       200\n",
      "           3     0.5426    0.5100    0.5258       200\n",
      "           4     0.8627    0.8800    0.8713       200\n",
      "           5     0.8608    0.8350    0.8477       200\n",
      "           6     0.4792    0.4600    0.4694       200\n",
      "           7     0.4462    0.4150    0.4301       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5873    0.5856    0.5839      1600\n",
      "weighted avg     0.5873    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4280    0.5500    0.4814       200\n",
      "           1     0.5739    0.6600    0.6140       200\n",
      "           2     0.5033    0.3800    0.4330       200\n",
      "           3     0.5593    0.4950    0.5252       200\n",
      "           4     0.8565    0.8950    0.8753       200\n",
      "           5     0.8684    0.8250    0.8462       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4450    0.4250    0.4348       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5889    0.5869    0.5851      1600\n",
      "weighted avg     0.5889    0.5869    0.5851      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4258    0.5450    0.4781       200\n",
      "           1     0.5739    0.6600    0.6140       200\n",
      "           2     0.5000    0.3800    0.4318       200\n",
      "           3     0.5580    0.5050    0.5302       200\n",
      "           4     0.8524    0.8950    0.8732       200\n",
      "           5     0.8730    0.8250    0.8483       200\n",
      "           6     0.4789    0.4550    0.4667       200\n",
      "           7     0.4531    0.4350    0.4439       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5894    0.5875    0.5858      1600\n",
      "weighted avg     0.5894    0.5875    0.5858      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5450    0.4791       200\n",
      "           1     0.5733    0.6650    0.6157       200\n",
      "           2     0.5033    0.3800    0.4330       200\n",
      "           3     0.5525    0.5000    0.5249       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8632    0.8200    0.8410       200\n",
      "           6     0.4762    0.4500    0.4627       200\n",
      "           7     0.4427    0.4250    0.4337       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5858    0.5844    0.5823      1600\n",
      "weighted avg     0.5858    0.5844    0.5823      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4219    0.5400    0.4737       200\n",
      "           1     0.5739    0.6600    0.6140       200\n",
      "           2     0.4967    0.3800    0.4306       200\n",
      "           3     0.5480    0.4850    0.5146       200\n",
      "           4     0.8443    0.8950    0.8689       200\n",
      "           5     0.8670    0.8150    0.8402       200\n",
      "           6     0.4737    0.4500    0.4615       200\n",
      "           7     0.4433    0.4300    0.4365       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5836    0.5819    0.5800      1600\n",
      "weighted avg     0.5836    0.5819    0.5800      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4258    0.5450    0.4781       200\n",
      "           1     0.5739    0.6600    0.6140       200\n",
      "           2     0.5000    0.3800    0.4318       200\n",
      "           3     0.5580    0.5050    0.5302       200\n",
      "           4     0.8524    0.8950    0.8732       200\n",
      "           5     0.8730    0.8250    0.8483       200\n",
      "           6     0.4789    0.4550    0.4667       200\n",
      "           7     0.4531    0.4350    0.4439       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5894    0.5875    0.5858      1600\n",
      "weighted avg     0.5894    0.5875    0.5858      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4241    0.5450    0.4770       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5099    0.3850    0.4387       200\n",
      "           3     0.5600    0.4900    0.5227       200\n",
      "           4     0.8634    0.8850    0.8741       200\n",
      "           5     0.8660    0.8400    0.8528       200\n",
      "           6     0.4767    0.4600    0.4682       200\n",
      "           7     0.4359    0.4250    0.4304       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5893    0.5869    0.5853      1600\n",
      "weighted avg     0.5893    0.5869    0.5853      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4241    0.5450    0.4770       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5099    0.3850    0.4387       200\n",
      "           3     0.5621    0.4750    0.5149       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4742    0.4600    0.4670       200\n",
      "           7     0.4400    0.4400    0.4400       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5904    0.5875    0.5860      1600\n",
      "weighted avg     0.5904    0.5875    0.5860      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4219    0.5400    0.4737       200\n",
      "           1     0.5764    0.6600    0.6154       200\n",
      "           2     0.4967    0.3800    0.4306       200\n",
      "           3     0.5587    0.5000    0.5277       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4740    0.4550    0.4643       200\n",
      "           7     0.4479    0.4300    0.4388       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5887    0.5869    0.5853      1600\n",
      "weighted avg     0.5887    0.5869    0.5853      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4328    0.5800    0.4957       200\n",
      "           1     0.5733    0.6650    0.6157       200\n",
      "           2     0.5303    0.3500    0.4217       200\n",
      "           3     0.5683    0.5200    0.5431       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4680    0.4750    0.4715       200\n",
      "           7     0.4783    0.4400    0.4583       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.5968    0.5931    0.5906      1600\n",
      "weighted avg     0.5968    0.5931    0.5906      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4318    0.5700    0.4914       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5224    0.3500    0.4192       200\n",
      "           3     0.5645    0.5250    0.5440       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4677    0.4700    0.4688       200\n",
      "           7     0.4780    0.4350    0.4555       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5953    0.5925    0.5898      1600\n",
      "weighted avg     0.5953    0.5925    0.5898      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4398    0.5850    0.5021       200\n",
      "           1     0.5720    0.6750    0.6193       200\n",
      "           2     0.5420    0.3550    0.4290       200\n",
      "           3     0.5585    0.5250    0.5412       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8608    0.8350    0.8477       200\n",
      "           6     0.4673    0.4650    0.4662       200\n",
      "           7     0.4696    0.4250    0.4462       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.5961    0.5931    0.5901      1600\n",
      "weighted avg     0.5961    0.5931    0.5901      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4345    0.5800    0.4968       200\n",
      "           1     0.5733    0.6650    0.6157       200\n",
      "           2     0.5263    0.3500    0.4204       200\n",
      "           3     0.5585    0.5250    0.5412       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8608    0.8350    0.8477       200\n",
      "           6     0.4700    0.4700    0.4700       200\n",
      "           7     0.4751    0.4300    0.4514       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5946    0.5919    0.5891      1600\n",
      "weighted avg     0.5946    0.5919    0.5891      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4340    0.5750    0.4946       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5263    0.3500    0.4204       200\n",
      "           3     0.5645    0.5250    0.5440       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4677    0.4700    0.4688       200\n",
      "           7     0.4780    0.4350    0.4555       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.5961    0.5931    0.5904      1600\n",
      "weighted avg     0.5961    0.5931    0.5904      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4398    0.5850    0.5021       200\n",
      "           1     0.5720    0.6750    0.6193       200\n",
      "           2     0.5420    0.3550    0.4290       200\n",
      "           3     0.5585    0.5250    0.5412       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8608    0.8350    0.8477       200\n",
      "           6     0.4673    0.4650    0.4662       200\n",
      "           7     0.4696    0.4250    0.4462       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.5961    0.5931    0.5901      1600\n",
      "weighted avg     0.5961    0.5931    0.5901      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4323    0.5750    0.4936       200\n",
      "           1     0.5726    0.6700    0.6175       200\n",
      "           2     0.5338    0.3550    0.4264       200\n",
      "           3     0.5497    0.5250    0.5371       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8608    0.8350    0.8477       200\n",
      "           6     0.4747    0.4700    0.4724       200\n",
      "           7     0.4749    0.4250    0.4485       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5947    0.5919    0.5890      1600\n",
      "weighted avg     0.5947    0.5919    0.5890      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4340    0.5750    0.4946       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5561    0.5200    0.5375       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4724    0.4700    0.4712       200\n",
      "           7     0.4725    0.4300    0.4503       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5954    0.5925    0.5898      1600\n",
      "weighted avg     0.5954    0.5925    0.5898      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4398    0.5850    0.5021       200\n",
      "           1     0.5720    0.6750    0.6193       200\n",
      "           2     0.5385    0.3500    0.4242       200\n",
      "           3     0.5585    0.5250    0.5412       200\n",
      "           4     0.8585    0.8800    0.8691       200\n",
      "           5     0.8608    0.8350    0.8477       200\n",
      "           6     0.4650    0.4650    0.4650       200\n",
      "           7     0.4696    0.4250    0.4462       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5954    0.5925    0.5894      1600\n",
      "weighted avg     0.5954    0.5925    0.5894      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4297    0.5350    0.4766       200\n",
      "           1     0.5776    0.6700    0.6204       200\n",
      "           2     0.5497    0.4150    0.4729       200\n",
      "           3     0.5706    0.5050    0.5358       200\n",
      "           4     0.8502    0.8800    0.8649       200\n",
      "           5     0.8639    0.8250    0.8440       200\n",
      "           6     0.4697    0.4650    0.4673       200\n",
      "           7     0.4513    0.4400    0.4456       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5953    0.5919    0.5909      1600\n",
      "weighted avg     0.5953    0.5919    0.5909      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4219    0.5400    0.4737       200\n",
      "           1     0.5660    0.6650    0.6115       200\n",
      "           2     0.5168    0.3850    0.4413       200\n",
      "           3     0.5640    0.4850    0.5215       200\n",
      "           4     0.8443    0.8950    0.8689       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4822    0.4750    0.4786       200\n",
      "           7     0.4531    0.4350    0.4439       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5893    0.5863    0.5846      1600\n",
      "weighted avg     0.5893    0.5863    0.5846      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4229    0.5350    0.4724       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5203    0.3850    0.4425       200\n",
      "           3     0.5489    0.5050    0.5260       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5879    0.5856    0.5839      1600\n",
      "weighted avg     0.5879    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5450    0.4802       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5214    0.3650    0.4294       200\n",
      "           3     0.5591    0.5200    0.5389       200\n",
      "           4     0.8451    0.9000    0.8717       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4643    0.4550    0.4596       200\n",
      "           7     0.4497    0.4250    0.4370       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5887    0.5869    0.5843      1600\n",
      "weighted avg     0.5887    0.5869    0.5843      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4147    0.5350    0.4672       200\n",
      "           1     0.5678    0.6700    0.6147       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5556    0.5000    0.5263       200\n",
      "           4     0.8404    0.8950    0.8668       200\n",
      "           5     0.8663    0.8100    0.8372       200\n",
      "           6     0.4767    0.4600    0.4682       200\n",
      "           7     0.4492    0.4200    0.4341       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5855    0.5831    0.5810      1600\n",
      "weighted avg     0.5855    0.5831    0.5810      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4229    0.5350    0.4724       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5203    0.3850    0.4425       200\n",
      "           3     0.5489    0.5050    0.5260       200\n",
      "           4     0.8411    0.9000    0.8696       200\n",
      "           5     0.8710    0.8100    0.8394       200\n",
      "           6     0.4769    0.4650    0.4709       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5879    0.5856    0.5839      1600\n",
      "weighted avg     0.5879    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4208    0.5450    0.4749       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5241    0.3800    0.4406       200\n",
      "           3     0.5562    0.4950    0.5238       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8549    0.8250    0.8397       200\n",
      "           6     0.4821    0.4700    0.4759       200\n",
      "           7     0.4381    0.4250    0.4315       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5883    0.5856    0.5839      1600\n",
      "weighted avg     0.5883    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4215    0.5500    0.4772       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5168    0.3850    0.4413       200\n",
      "           3     0.5593    0.4950    0.5252       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8632    0.8200    0.8410       200\n",
      "           6     0.4792    0.4600    0.4694       200\n",
      "           7     0.4456    0.4300    0.4377       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5890    0.5862    0.5846      1600\n",
      "weighted avg     0.5890    0.5863    0.5846      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=log2, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4269    0.5400    0.4768       200\n",
      "           1     0.5671    0.6550    0.6079       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5464    0.5000    0.5222       200\n",
      "           4     0.8551    0.8850    0.8698       200\n",
      "           5     0.8594    0.8250    0.8418       200\n",
      "           6     0.4794    0.4650    0.4721       200\n",
      "           7     0.4485    0.4350    0.4416       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5871    0.5850    0.5832      1600\n",
      "weighted avg     0.5871    0.5850    0.5832      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 57.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4337    0.5400    0.4811       200\n",
      "           1     0.5625    0.6300    0.5943       200\n",
      "           2     0.5034    0.3700    0.4265       200\n",
      "           3     0.5647    0.4800    0.5189       200\n",
      "           4     0.8944    0.8050    0.8474       200\n",
      "           5     0.8194    0.8850    0.8510       200\n",
      "           6     0.4272    0.4550    0.4407       200\n",
      "           7     0.4179    0.4200    0.4190       200\n",
      "\n",
      "    accuracy                         0.5731      1600\n",
      "   macro avg     0.5779    0.5731    0.5724      1600\n",
      "weighted avg     0.5779    0.5731    0.5724      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 57.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4325    0.5450    0.4823       200\n",
      "           1     0.5609    0.6450    0.6000       200\n",
      "           2     0.5368    0.3650    0.4345       200\n",
      "           3     0.5525    0.5000    0.5249       200\n",
      "           4     0.9006    0.8150    0.8556       200\n",
      "           5     0.8279    0.8900    0.8578       200\n",
      "           6     0.4206    0.4500    0.4348       200\n",
      "           7     0.4450    0.4250    0.4348       200\n",
      "\n",
      "    accuracy                         0.5794      1600\n",
      "   macro avg     0.5846    0.5794    0.5781      1600\n",
      "weighted avg     0.5846    0.5794    0.5781      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4274    0.5300    0.4732       200\n",
      "           1     0.5546    0.6350    0.5921       200\n",
      "           2     0.5290    0.3650    0.4320       200\n",
      "           3     0.5459    0.5050    0.5247       200\n",
      "           4     0.9050    0.8100    0.8549       200\n",
      "           5     0.8174    0.8950    0.8544       200\n",
      "           6     0.4186    0.4500    0.4337       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5769      1600\n",
      "   macro avg     0.5816    0.5769    0.5755      1600\n",
      "weighted avg     0.5816    0.5769    0.5755      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4337    0.5400    0.4811       200\n",
      "           1     0.5517    0.6400    0.5926       200\n",
      "           2     0.5324    0.3700    0.4366       200\n",
      "           3     0.5543    0.5100    0.5312       200\n",
      "           4     0.8865    0.8200    0.8519       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4128    0.4500    0.4306       200\n",
      "           7     0.4615    0.4200    0.4398       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5828    0.5781    0.5769      1600\n",
      "weighted avg     0.5828    0.5781    0.5769      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4286    0.5400    0.4779       200\n",
      "           1     0.5664    0.6400    0.6009       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5426    0.5100    0.5258       200\n",
      "           4     0.8962    0.8200    0.8564       200\n",
      "           5     0.8310    0.8850    0.8571       200\n",
      "           6     0.4155    0.4550    0.4344       200\n",
      "           7     0.4541    0.4200    0.4364       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5830    0.5781    0.5768      1600\n",
      "weighted avg     0.5830    0.5781    0.5768      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4274    0.5300    0.4732       200\n",
      "           1     0.5546    0.6350    0.5921       200\n",
      "           2     0.5290    0.3650    0.4320       200\n",
      "           3     0.5459    0.5050    0.5247       200\n",
      "           4     0.9050    0.8100    0.8549       200\n",
      "           5     0.8174    0.8950    0.8544       200\n",
      "           6     0.4186    0.4500    0.4337       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5769      1600\n",
      "   macro avg     0.5816    0.5769    0.5755      1600\n",
      "weighted avg     0.5816    0.5769    0.5755      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4286    0.5400    0.4779       200\n",
      "           1     0.5619    0.6350    0.5962       200\n",
      "           2     0.5324    0.3700    0.4366       200\n",
      "           3     0.5580    0.5050    0.5302       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8286    0.8700    0.8488       200\n",
      "           6     0.4155    0.4550    0.4344       200\n",
      "           7     0.4599    0.4300    0.4444       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5833    0.5781    0.5773      1600\n",
      "weighted avg     0.5833    0.5781    0.5773      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4240    0.5300    0.4711       200\n",
      "           1     0.5575    0.6300    0.5915       200\n",
      "           2     0.5217    0.3600    0.4260       200\n",
      "           3     0.5464    0.5000    0.5222       200\n",
      "           4     0.8913    0.8200    0.8542       200\n",
      "           5     0.8302    0.8800    0.8544       200\n",
      "           6     0.4163    0.4600    0.4371       200\n",
      "           7     0.4462    0.4150    0.4301       200\n",
      "\n",
      "    accuracy                         0.5744      1600\n",
      "   macro avg     0.5792    0.5744    0.5733      1600\n",
      "weighted avg     0.5792    0.5744    0.5733      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4274    0.5300    0.4732       200\n",
      "           1     0.5517    0.6400    0.5926       200\n",
      "           2     0.5407    0.3650    0.4358       200\n",
      "           3     0.5479    0.5150    0.5309       200\n",
      "           4     0.9101    0.8100    0.8571       200\n",
      "           5     0.8257    0.9000    0.8612       200\n",
      "           6     0.4120    0.4450    0.4279       200\n",
      "           7     0.4595    0.4250    0.4416       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5844    0.5787    0.5775      1600\n",
      "weighted avg     0.5844    0.5787    0.5775      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 57.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4337    0.5400    0.4811       200\n",
      "           1     0.5625    0.6300    0.5943       200\n",
      "           2     0.5034    0.3700    0.4265       200\n",
      "           3     0.5647    0.4800    0.5189       200\n",
      "           4     0.8944    0.8050    0.8474       200\n",
      "           5     0.8194    0.8850    0.8510       200\n",
      "           6     0.4272    0.4550    0.4407       200\n",
      "           7     0.4179    0.4200    0.4190       200\n",
      "\n",
      "    accuracy                         0.5731      1600\n",
      "   macro avg     0.5779    0.5731    0.5724      1600\n",
      "weighted avg     0.5779    0.5731    0.5724      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 57.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4325    0.5450    0.4823       200\n",
      "           1     0.5609    0.6450    0.6000       200\n",
      "           2     0.5368    0.3650    0.4345       200\n",
      "           3     0.5525    0.5000    0.5249       200\n",
      "           4     0.9006    0.8150    0.8556       200\n",
      "           5     0.8279    0.8900    0.8578       200\n",
      "           6     0.4206    0.4500    0.4348       200\n",
      "           7     0.4450    0.4250    0.4348       200\n",
      "\n",
      "    accuracy                         0.5794      1600\n",
      "   macro avg     0.5846    0.5794    0.5781      1600\n",
      "weighted avg     0.5846    0.5794    0.5781      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4274    0.5300    0.4732       200\n",
      "           1     0.5546    0.6350    0.5921       200\n",
      "           2     0.5290    0.3650    0.4320       200\n",
      "           3     0.5459    0.5050    0.5247       200\n",
      "           4     0.9050    0.8100    0.8549       200\n",
      "           5     0.8174    0.8950    0.8544       200\n",
      "           6     0.4186    0.4500    0.4337       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5769      1600\n",
      "   macro avg     0.5816    0.5769    0.5755      1600\n",
      "weighted avg     0.5816    0.5769    0.5755      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4337    0.5400    0.4811       200\n",
      "           1     0.5517    0.6400    0.5926       200\n",
      "           2     0.5324    0.3700    0.4366       200\n",
      "           3     0.5543    0.5100    0.5312       200\n",
      "           4     0.8865    0.8200    0.8519       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4128    0.4500    0.4306       200\n",
      "           7     0.4615    0.4200    0.4398       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5828    0.5781    0.5769      1600\n",
      "weighted avg     0.5828    0.5781    0.5769      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4286    0.5400    0.4779       200\n",
      "           1     0.5664    0.6400    0.6009       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5426    0.5100    0.5258       200\n",
      "           4     0.8962    0.8200    0.8564       200\n",
      "           5     0.8310    0.8850    0.8571       200\n",
      "           6     0.4155    0.4550    0.4344       200\n",
      "           7     0.4541    0.4200    0.4364       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5830    0.5781    0.5768      1600\n",
      "weighted avg     0.5830    0.5781    0.5768      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4274    0.5300    0.4732       200\n",
      "           1     0.5546    0.6350    0.5921       200\n",
      "           2     0.5290    0.3650    0.4320       200\n",
      "           3     0.5459    0.5050    0.5247       200\n",
      "           4     0.9050    0.8100    0.8549       200\n",
      "           5     0.8174    0.8950    0.8544       200\n",
      "           6     0.4186    0.4500    0.4337       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5769      1600\n",
      "   macro avg     0.5816    0.5769    0.5755      1600\n",
      "weighted avg     0.5816    0.5769    0.5755      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4286    0.5400    0.4779       200\n",
      "           1     0.5619    0.6350    0.5962       200\n",
      "           2     0.5324    0.3700    0.4366       200\n",
      "           3     0.5580    0.5050    0.5302       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8286    0.8700    0.8488       200\n",
      "           6     0.4155    0.4550    0.4344       200\n",
      "           7     0.4599    0.4300    0.4444       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5833    0.5781    0.5773      1600\n",
      "weighted avg     0.5833    0.5781    0.5773      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4240    0.5300    0.4711       200\n",
      "           1     0.5575    0.6300    0.5915       200\n",
      "           2     0.5217    0.3600    0.4260       200\n",
      "           3     0.5464    0.5000    0.5222       200\n",
      "           4     0.8913    0.8200    0.8542       200\n",
      "           5     0.8302    0.8800    0.8544       200\n",
      "           6     0.4163    0.4600    0.4371       200\n",
      "           7     0.4462    0.4150    0.4301       200\n",
      "\n",
      "    accuracy                         0.5744      1600\n",
      "   macro avg     0.5792    0.5744    0.5733      1600\n",
      "weighted avg     0.5792    0.5744    0.5733      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4274    0.5300    0.4732       200\n",
      "           1     0.5517    0.6400    0.5926       200\n",
      "           2     0.5407    0.3650    0.4358       200\n",
      "           3     0.5479    0.5150    0.5309       200\n",
      "           4     0.9101    0.8100    0.8571       200\n",
      "           5     0.8257    0.9000    0.8612       200\n",
      "           6     0.4120    0.4450    0.4279       200\n",
      "           7     0.4595    0.4250    0.4416       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5844    0.5787    0.5775      1600\n",
      "weighted avg     0.5844    0.5787    0.5775      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4343    0.5450    0.4834       200\n",
      "           1     0.5575    0.6300    0.5915       200\n",
      "           2     0.5241    0.3800    0.4406       200\n",
      "           3     0.5604    0.5100    0.5340       200\n",
      "           4     0.8994    0.8050    0.8496       200\n",
      "           5     0.8203    0.8900    0.8537       200\n",
      "           6     0.4182    0.4600    0.4381       200\n",
      "           7     0.4500    0.4050    0.4263       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5830    0.5781    0.5772      1600\n",
      "weighted avg     0.5830    0.5781    0.5772      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4382    0.5500    0.4878       200\n",
      "           1     0.5595    0.6350    0.5948       200\n",
      "           2     0.5401    0.3700    0.4392       200\n",
      "           3     0.5405    0.5000    0.5195       200\n",
      "           4     0.9244    0.7950    0.8548       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4194    0.4550    0.4365       200\n",
      "           7     0.4492    0.4200    0.4341       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5860    0.5800    0.5787      1600\n",
      "weighted avg     0.5860    0.5800    0.5787      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4390    0.5400    0.4843       200\n",
      "           1     0.5570    0.6350    0.5935       200\n",
      "           2     0.5282    0.3750    0.4386       200\n",
      "           3     0.5497    0.5250    0.5371       200\n",
      "           4     0.9249    0.8000    0.8579       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4213    0.4550    0.4375       200\n",
      "           7     0.4611    0.4150    0.4368       200\n",
      "\n",
      "    accuracy                         0.5825      1600\n",
      "   macro avg     0.5873    0.5825    0.5811      1600\n",
      "weighted avg     0.5873    0.5825    0.5811      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4337    0.5400    0.4811       200\n",
      "           1     0.5546    0.6350    0.5921       200\n",
      "           2     0.5328    0.3650    0.4332       200\n",
      "           3     0.5532    0.5200    0.5361       200\n",
      "           4     0.9191    0.7950    0.8525       200\n",
      "           5     0.8161    0.9100    0.8605       200\n",
      "           6     0.4081    0.4550    0.4303       200\n",
      "           7     0.4607    0.4100    0.4339       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5848    0.5788    0.5775      1600\n",
      "weighted avg     0.5848    0.5787    0.5775      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4315    0.5350    0.4777       200\n",
      "           1     0.5595    0.6350    0.5948       200\n",
      "           2     0.5373    0.3600    0.4311       200\n",
      "           3     0.5323    0.5350    0.5337       200\n",
      "           4     0.9244    0.7950    0.8548       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4072    0.4500    0.4276       200\n",
      "           7     0.4682    0.4050    0.4343       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5847    0.5787    0.5772      1600\n",
      "weighted avg     0.5847    0.5787    0.5772      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4390    0.5400    0.4843       200\n",
      "           1     0.5570    0.6350    0.5935       200\n",
      "           2     0.5282    0.3750    0.4386       200\n",
      "           3     0.5497    0.5250    0.5371       200\n",
      "           4     0.9249    0.8000    0.8579       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4213    0.4550    0.4375       200\n",
      "           7     0.4611    0.4150    0.4368       200\n",
      "\n",
      "    accuracy                         0.5825      1600\n",
      "   macro avg     0.5873    0.5825    0.5811      1600\n",
      "weighted avg     0.5873    0.5825    0.5811      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4355    0.5400    0.4821       200\n",
      "           1     0.5619    0.6350    0.5962       200\n",
      "           2     0.5324    0.3700    0.4366       200\n",
      "           3     0.5579    0.5300    0.5436       200\n",
      "           4     0.9244    0.7950    0.8548       200\n",
      "           5     0.8170    0.9150    0.8632       200\n",
      "           6     0.4126    0.4600    0.4350       200\n",
      "           7     0.4551    0.4050    0.4286       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5871    0.5813    0.5800      1600\n",
      "weighted avg     0.5871    0.5813    0.5800      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4315    0.5350    0.4777       200\n",
      "           1     0.5595    0.6350    0.5948       200\n",
      "           2     0.5252    0.3650    0.4307       200\n",
      "           3     0.5638    0.5300    0.5464       200\n",
      "           4     0.9244    0.7950    0.8548       200\n",
      "           5     0.8133    0.9150    0.8612       200\n",
      "           6     0.4163    0.4600    0.4371       200\n",
      "           7     0.4556    0.4100    0.4316       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5862    0.5806    0.5793      1600\n",
      "weighted avg     0.5862    0.5806    0.5793      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4360    0.5450    0.4844       200\n",
      "           1     0.5570    0.6350    0.5935       200\n",
      "           2     0.5319    0.3750    0.4399       200\n",
      "           3     0.5412    0.5250    0.5330       200\n",
      "           4     0.9249    0.8000    0.8579       200\n",
      "           5     0.8133    0.9150    0.8612       200\n",
      "           6     0.4218    0.4450    0.4331       200\n",
      "           7     0.4551    0.4050    0.4286       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5852    0.5806    0.5789      1600\n",
      "weighted avg     0.5852    0.5806    0.5789      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 56.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4158    0.5800    0.4843       200\n",
      "           1     0.5603    0.6500    0.6019       200\n",
      "           2     0.5378    0.3200    0.4013       200\n",
      "           3     0.5213    0.4900    0.5052       200\n",
      "           4     0.8859    0.8150    0.8490       200\n",
      "           5     0.8102    0.8750    0.8413       200\n",
      "           6     0.4219    0.4050    0.4133       200\n",
      "           7     0.4421    0.4200    0.4308       200\n",
      "\n",
      "    accuracy                         0.5694      1600\n",
      "   macro avg     0.5744    0.5694    0.5659      1600\n",
      "weighted avg     0.5744    0.5694    0.5659      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 57.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4301    0.5850    0.4958       200\n",
      "           1     0.5584    0.6450    0.5986       200\n",
      "           2     0.5495    0.3050    0.3923       200\n",
      "           3     0.5155    0.5000    0.5076       200\n",
      "           4     0.8859    0.8150    0.8490       200\n",
      "           5     0.8102    0.8750    0.8413       200\n",
      "           6     0.4286    0.4350    0.4318       200\n",
      "           7     0.4444    0.4200    0.4319       200\n",
      "\n",
      "    accuracy                         0.5725      1600\n",
      "   macro avg     0.5778    0.5725    0.5685      1600\n",
      "weighted avg     0.5778    0.5725    0.5685      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.12%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4265    0.5800    0.4915       200\n",
      "           1     0.5536    0.6450    0.5958       200\n",
      "           2     0.5351    0.3050    0.3885       200\n",
      "           3     0.5231    0.5100    0.5165       200\n",
      "           4     0.8859    0.8150    0.8490       200\n",
      "           5     0.8140    0.8750    0.8434       200\n",
      "           6     0.4286    0.4200    0.4242       200\n",
      "           7     0.4398    0.4200    0.4297       200\n",
      "\n",
      "    accuracy                         0.5713      1600\n",
      "   macro avg     0.5758    0.5713    0.5673      1600\n",
      "weighted avg     0.5758    0.5713    0.5673      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 57.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4301    0.5850    0.4958       200\n",
      "           1     0.5609    0.6450    0.6000       200\n",
      "           2     0.5391    0.3100    0.3937       200\n",
      "           3     0.5160    0.4850    0.5000       200\n",
      "           4     0.8859    0.8150    0.8490       200\n",
      "           5     0.8102    0.8750    0.8413       200\n",
      "           6     0.4286    0.4350    0.4318       200\n",
      "           7     0.4427    0.4250    0.4337       200\n",
      "\n",
      "    accuracy                         0.5719      1600\n",
      "   macro avg     0.5767    0.5719    0.5681      1600\n",
      "weighted avg     0.5767    0.5719    0.5681      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4301    0.5850    0.4958       200\n",
      "           1     0.5584    0.6450    0.5986       200\n",
      "           2     0.5446    0.3050    0.3910       200\n",
      "           3     0.5179    0.5050    0.5114       200\n",
      "           4     0.8859    0.8150    0.8490       200\n",
      "           5     0.8102    0.8750    0.8413       200\n",
      "           6     0.4286    0.4350    0.4318       200\n",
      "           7     0.4492    0.4200    0.4341       200\n",
      "\n",
      "    accuracy                         0.5731      1600\n",
      "   macro avg     0.5781    0.5731    0.5691      1600\n",
      "weighted avg     0.5781    0.5731    0.5691      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.12%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4265    0.5800    0.4915       200\n",
      "           1     0.5536    0.6450    0.5958       200\n",
      "           2     0.5351    0.3050    0.3885       200\n",
      "           3     0.5231    0.5100    0.5165       200\n",
      "           4     0.8859    0.8150    0.8490       200\n",
      "           5     0.8140    0.8750    0.8434       200\n",
      "           6     0.4286    0.4200    0.4242       200\n",
      "           7     0.4398    0.4200    0.4297       200\n",
      "\n",
      "    accuracy                         0.5713      1600\n",
      "   macro avg     0.5758    0.5713    0.5673      1600\n",
      "weighted avg     0.5758    0.5713    0.5673      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 57.12%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4270    0.5850    0.4937       200\n",
      "           1     0.5614    0.6400    0.5981       200\n",
      "           2     0.5299    0.3100    0.3912       200\n",
      "           3     0.5213    0.4900    0.5052       200\n",
      "           4     0.8859    0.8150    0.8490       200\n",
      "           5     0.8102    0.8750    0.8413       200\n",
      "           6     0.4257    0.4300    0.4279       200\n",
      "           7     0.4450    0.4250    0.4348       200\n",
      "\n",
      "    accuracy                         0.5713      1600\n",
      "   macro avg     0.5758    0.5713    0.5676      1600\n",
      "weighted avg     0.5758    0.5713    0.5676      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4286    0.5850    0.4947       200\n",
      "           1     0.5584    0.6450    0.5986       200\n",
      "           2     0.5391    0.3100    0.3937       200\n",
      "           3     0.5231    0.5100    0.5165       200\n",
      "           4     0.8859    0.8150    0.8490       200\n",
      "           5     0.8102    0.8750    0.8413       200\n",
      "           6     0.4300    0.4300    0.4300       200\n",
      "           7     0.4462    0.4150    0.4301       200\n",
      "\n",
      "    accuracy                         0.5731      1600\n",
      "   macro avg     0.5777    0.5731    0.5692      1600\n",
      "weighted avg     0.5777    0.5731    0.5692      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 57.12%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4265    0.5800    0.4915       200\n",
      "           1     0.5536    0.6450    0.5958       200\n",
      "           2     0.5351    0.3050    0.3885       200\n",
      "           3     0.5231    0.5100    0.5165       200\n",
      "           4     0.8859    0.8150    0.8490       200\n",
      "           5     0.8140    0.8750    0.8434       200\n",
      "           6     0.4286    0.4200    0.4242       200\n",
      "           7     0.4398    0.4200    0.4297       200\n",
      "\n",
      "    accuracy                         0.5713      1600\n",
      "   macro avg     0.5758    0.5713    0.5673      1600\n",
      "weighted avg     0.5758    0.5713    0.5673      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 57.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4337    0.5400    0.4811       200\n",
      "           1     0.5625    0.6300    0.5943       200\n",
      "           2     0.5034    0.3700    0.4265       200\n",
      "           3     0.5647    0.4800    0.5189       200\n",
      "           4     0.8944    0.8050    0.8474       200\n",
      "           5     0.8194    0.8850    0.8510       200\n",
      "           6     0.4272    0.4550    0.4407       200\n",
      "           7     0.4179    0.4200    0.4190       200\n",
      "\n",
      "    accuracy                         0.5731      1600\n",
      "   macro avg     0.5779    0.5731    0.5724      1600\n",
      "weighted avg     0.5779    0.5731    0.5724      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 57.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4325    0.5450    0.4823       200\n",
      "           1     0.5609    0.6450    0.6000       200\n",
      "           2     0.5368    0.3650    0.4345       200\n",
      "           3     0.5525    0.5000    0.5249       200\n",
      "           4     0.9006    0.8150    0.8556       200\n",
      "           5     0.8279    0.8900    0.8578       200\n",
      "           6     0.4206    0.4500    0.4348       200\n",
      "           7     0.4450    0.4250    0.4348       200\n",
      "\n",
      "    accuracy                         0.5794      1600\n",
      "   macro avg     0.5846    0.5794    0.5781      1600\n",
      "weighted avg     0.5846    0.5794    0.5781      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4274    0.5300    0.4732       200\n",
      "           1     0.5546    0.6350    0.5921       200\n",
      "           2     0.5290    0.3650    0.4320       200\n",
      "           3     0.5459    0.5050    0.5247       200\n",
      "           4     0.9050    0.8100    0.8549       200\n",
      "           5     0.8174    0.8950    0.8544       200\n",
      "           6     0.4186    0.4500    0.4337       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5769      1600\n",
      "   macro avg     0.5816    0.5769    0.5755      1600\n",
      "weighted avg     0.5816    0.5769    0.5755      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4337    0.5400    0.4811       200\n",
      "           1     0.5517    0.6400    0.5926       200\n",
      "           2     0.5324    0.3700    0.4366       200\n",
      "           3     0.5543    0.5100    0.5312       200\n",
      "           4     0.8865    0.8200    0.8519       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4128    0.4500    0.4306       200\n",
      "           7     0.4615    0.4200    0.4398       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5828    0.5781    0.5769      1600\n",
      "weighted avg     0.5828    0.5781    0.5769      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4286    0.5400    0.4779       200\n",
      "           1     0.5664    0.6400    0.6009       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5426    0.5100    0.5258       200\n",
      "           4     0.8962    0.8200    0.8564       200\n",
      "           5     0.8310    0.8850    0.8571       200\n",
      "           6     0.4155    0.4550    0.4344       200\n",
      "           7     0.4541    0.4200    0.4364       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5830    0.5781    0.5768      1600\n",
      "weighted avg     0.5830    0.5781    0.5768      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4274    0.5300    0.4732       200\n",
      "           1     0.5546    0.6350    0.5921       200\n",
      "           2     0.5290    0.3650    0.4320       200\n",
      "           3     0.5459    0.5050    0.5247       200\n",
      "           4     0.9050    0.8100    0.8549       200\n",
      "           5     0.8174    0.8950    0.8544       200\n",
      "           6     0.4186    0.4500    0.4337       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5769      1600\n",
      "   macro avg     0.5816    0.5769    0.5755      1600\n",
      "weighted avg     0.5816    0.5769    0.5755      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4286    0.5400    0.4779       200\n",
      "           1     0.5619    0.6350    0.5962       200\n",
      "           2     0.5324    0.3700    0.4366       200\n",
      "           3     0.5580    0.5050    0.5302       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8286    0.8700    0.8488       200\n",
      "           6     0.4155    0.4550    0.4344       200\n",
      "           7     0.4599    0.4300    0.4444       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5833    0.5781    0.5773      1600\n",
      "weighted avg     0.5833    0.5781    0.5773      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4240    0.5300    0.4711       200\n",
      "           1     0.5575    0.6300    0.5915       200\n",
      "           2     0.5217    0.3600    0.4260       200\n",
      "           3     0.5464    0.5000    0.5222       200\n",
      "           4     0.8913    0.8200    0.8542       200\n",
      "           5     0.8302    0.8800    0.8544       200\n",
      "           6     0.4163    0.4600    0.4371       200\n",
      "           7     0.4462    0.4150    0.4301       200\n",
      "\n",
      "    accuracy                         0.5744      1600\n",
      "   macro avg     0.5792    0.5744    0.5733      1600\n",
      "weighted avg     0.5792    0.5744    0.5733      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=5, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4274    0.5300    0.4732       200\n",
      "           1     0.5517    0.6400    0.5926       200\n",
      "           2     0.5407    0.3650    0.4358       200\n",
      "           3     0.5479    0.5150    0.5309       200\n",
      "           4     0.9101    0.8100    0.8571       200\n",
      "           5     0.8257    0.9000    0.8612       200\n",
      "           6     0.4120    0.4450    0.4279       200\n",
      "           7     0.4595    0.4250    0.4416       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5844    0.5787    0.5775      1600\n",
      "weighted avg     0.5844    0.5787    0.5775      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4242    0.5600    0.4828       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5108    0.3550    0.4189       200\n",
      "           3     0.5746    0.5200    0.5459       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4577    0.4600    0.4589       200\n",
      "           7     0.4468    0.4200    0.4330       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5901    0.5869    0.5849      1600\n",
      "weighted avg     0.5901    0.5869    0.5849      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4111    0.5550    0.4723       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5698    0.5100    0.5383       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4495    0.4450    0.4472       200\n",
      "           7     0.4362    0.4100    0.4227       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5856    0.5819    0.5799      1600\n",
      "weighted avg     0.5856    0.5819    0.5799      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4157    0.5550    0.4754       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5147    0.3500    0.4167       200\n",
      "           3     0.5668    0.5300    0.5478       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4554    0.4600    0.4577       200\n",
      "           7     0.4556    0.4100    0.4316       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5906    0.5875    0.5852      1600\n",
      "weighted avg     0.5906    0.5875    0.5852      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4164    0.5600    0.4776       200\n",
      "           1     0.5733    0.6450    0.6071       200\n",
      "           2     0.4966    0.3600    0.4174       200\n",
      "           3     0.5690    0.4950    0.5294       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4550    0.4550    0.4550       200\n",
      "           7     0.4286    0.4050    0.4165       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5847    0.5813    0.5796      1600\n",
      "weighted avg     0.5847    0.5813    0.5796      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4189    0.5550    0.4774       200\n",
      "           1     0.5785    0.6450    0.6099       200\n",
      "           2     0.5000    0.3650    0.4220       200\n",
      "           3     0.5843    0.5200    0.5503       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4510    0.4600    0.4554       200\n",
      "           7     0.4462    0.4150    0.4301       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5897    0.5862    0.5849      1600\n",
      "weighted avg     0.5897    0.5863    0.5849      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4157    0.5550    0.4754       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5147    0.3500    0.4167       200\n",
      "           3     0.5668    0.5300    0.5478       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4554    0.4600    0.4577       200\n",
      "           7     0.4556    0.4100    0.4316       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5906    0.5875    0.5852      1600\n",
      "weighted avg     0.5906    0.5875    0.5852      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4195    0.5600    0.4797       200\n",
      "           1     0.5708    0.6450    0.6056       200\n",
      "           2     0.5034    0.3650    0.4232       200\n",
      "           3     0.5714    0.5000    0.5333       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4488    0.4600    0.4543       200\n",
      "           7     0.4432    0.4100    0.4260       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5875    0.5837    0.5823      1600\n",
      "weighted avg     0.5875    0.5837    0.5823      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4173    0.5550    0.4764       200\n",
      "           1     0.5733    0.6450    0.6071       200\n",
      "           2     0.5034    0.3650    0.4232       200\n",
      "           3     0.5611    0.5050    0.5316       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4519    0.4700    0.4608       200\n",
      "           7     0.4551    0.4050    0.4286       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5876    0.5844    0.5827      1600\n",
      "weighted avg     0.5876    0.5844    0.5827      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4216    0.5650    0.4829       200\n",
      "           1     0.5771    0.6550    0.6136       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5730    0.5300    0.5506       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4527    0.4550    0.4539       200\n",
      "           7     0.4481    0.4100    0.4282       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5898    0.5869    0.5847      1600\n",
      "weighted avg     0.5898    0.5869    0.5847      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4242    0.5600    0.4828       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5108    0.3550    0.4189       200\n",
      "           3     0.5746    0.5200    0.5459       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4577    0.4600    0.4589       200\n",
      "           7     0.4468    0.4200    0.4330       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5901    0.5869    0.5849      1600\n",
      "weighted avg     0.5901    0.5869    0.5849      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4111    0.5550    0.4723       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5698    0.5100    0.5383       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4495    0.4450    0.4472       200\n",
      "           7     0.4362    0.4100    0.4227       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5856    0.5819    0.5799      1600\n",
      "weighted avg     0.5856    0.5819    0.5799      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4157    0.5550    0.4754       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5147    0.3500    0.4167       200\n",
      "           3     0.5668    0.5300    0.5478       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4554    0.4600    0.4577       200\n",
      "           7     0.4556    0.4100    0.4316       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5906    0.5875    0.5852      1600\n",
      "weighted avg     0.5906    0.5875    0.5852      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4164    0.5600    0.4776       200\n",
      "           1     0.5733    0.6450    0.6071       200\n",
      "           2     0.4966    0.3600    0.4174       200\n",
      "           3     0.5690    0.4950    0.5294       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4550    0.4550    0.4550       200\n",
      "           7     0.4286    0.4050    0.4165       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5847    0.5813    0.5796      1600\n",
      "weighted avg     0.5847    0.5813    0.5796      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4189    0.5550    0.4774       200\n",
      "           1     0.5785    0.6450    0.6099       200\n",
      "           2     0.5000    0.3650    0.4220       200\n",
      "           3     0.5843    0.5200    0.5503       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4510    0.4600    0.4554       200\n",
      "           7     0.4462    0.4150    0.4301       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5897    0.5862    0.5849      1600\n",
      "weighted avg     0.5897    0.5863    0.5849      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4157    0.5550    0.4754       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5147    0.3500    0.4167       200\n",
      "           3     0.5668    0.5300    0.5478       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4554    0.4600    0.4577       200\n",
      "           7     0.4556    0.4100    0.4316       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5906    0.5875    0.5852      1600\n",
      "weighted avg     0.5906    0.5875    0.5852      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4195    0.5600    0.4797       200\n",
      "           1     0.5708    0.6450    0.6056       200\n",
      "           2     0.5034    0.3650    0.4232       200\n",
      "           3     0.5714    0.5000    0.5333       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4488    0.4600    0.4543       200\n",
      "           7     0.4432    0.4100    0.4260       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5875    0.5837    0.5823      1600\n",
      "weighted avg     0.5875    0.5837    0.5823      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4173    0.5550    0.4764       200\n",
      "           1     0.5733    0.6450    0.6071       200\n",
      "           2     0.5034    0.3650    0.4232       200\n",
      "           3     0.5611    0.5050    0.5316       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4519    0.4700    0.4608       200\n",
      "           7     0.4551    0.4050    0.4286       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5876    0.5844    0.5827      1600\n",
      "weighted avg     0.5876    0.5844    0.5827      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4216    0.5650    0.4829       200\n",
      "           1     0.5771    0.6550    0.6136       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5730    0.5300    0.5506       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4527    0.4550    0.4539       200\n",
      "           7     0.4481    0.4100    0.4282       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5898    0.5869    0.5847      1600\n",
      "weighted avg     0.5898    0.5869    0.5847      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4176    0.5450    0.4729       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5699    0.5300    0.5492       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4577    0.4600    0.4589       200\n",
      "           7     0.4415    0.4150    0.4278       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5889    0.5862    0.5842      1600\n",
      "weighted avg     0.5889    0.5863    0.5842      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4096    0.5550    0.4713       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5108    0.3550    0.4189       200\n",
      "           3     0.5698    0.5100    0.5383       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4495    0.4450    0.4472       200\n",
      "           7     0.4409    0.4100    0.4249       200\n",
      "\n",
      "    accuracy                         0.5825      1600\n",
      "   macro avg     0.5864    0.5825    0.5807      1600\n",
      "weighted avg     0.5864    0.5825    0.5807      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4142    0.5550    0.4744       200\n",
      "           1     0.5808    0.6650    0.6200       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5761    0.5300    0.5521       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4527    0.4550    0.4539       200\n",
      "           7     0.4536    0.4150    0.4334       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5918    0.5881    0.5862      1600\n",
      "weighted avg     0.5918    0.5881    0.5862      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4205    0.5550    0.4784       200\n",
      "           1     0.5708    0.6450    0.6056       200\n",
      "           2     0.5000    0.3650    0.4220       200\n",
      "           3     0.5829    0.5100    0.5440       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4554    0.4600    0.4577       200\n",
      "           7     0.4392    0.4150    0.4267       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5884    0.5850    0.5836      1600\n",
      "weighted avg     0.5884    0.5850    0.5836      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4135    0.5500    0.4721       200\n",
      "           1     0.5804    0.6500    0.6132       200\n",
      "           2     0.5034    0.3650    0.4232       200\n",
      "           3     0.5795    0.5100    0.5426       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4510    0.4600    0.4554       200\n",
      "           7     0.4439    0.4150    0.4289       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5888    0.5850    0.5837      1600\n",
      "weighted avg     0.5888    0.5850    0.5837      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4142    0.5550    0.4744       200\n",
      "           1     0.5808    0.6650    0.6200       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5761    0.5300    0.5521       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4527    0.4550    0.4539       200\n",
      "           7     0.4536    0.4150    0.4334       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5918    0.5881    0.5862      1600\n",
      "weighted avg     0.5918    0.5881    0.5862      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4195    0.5600    0.4797       200\n",
      "           1     0.5708    0.6450    0.6056       200\n",
      "           2     0.5034    0.3650    0.4232       200\n",
      "           3     0.5698    0.5100    0.5383       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4488    0.4600    0.4543       200\n",
      "           7     0.4586    0.4150    0.4357       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5892    0.5856    0.5841      1600\n",
      "weighted avg     0.5892    0.5856    0.5841      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4157    0.5550    0.4754       200\n",
      "           1     0.5708    0.6450    0.6056       200\n",
      "           2     0.5000    0.3600    0.4186       200\n",
      "           3     0.5642    0.5050    0.5330       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4563    0.4700    0.4631       200\n",
      "           7     0.4556    0.4100    0.4316       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5877    0.5844    0.5827      1600\n",
      "weighted avg     0.5877    0.5844    0.5827      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4157    0.5550    0.4754       200\n",
      "           1     0.5808    0.6650    0.6200       200\n",
      "           2     0.5145    0.3550    0.4201       200\n",
      "           3     0.5761    0.5300    0.5521       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4527    0.4550    0.4539       200\n",
      "           7     0.4536    0.4150    0.4334       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5915    0.5881    0.5861      1600\n",
      "weighted avg     0.5915    0.5881    0.5861      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4054    0.6000    0.4839       200\n",
      "           1     0.5819    0.6750    0.6250       200\n",
      "           2     0.5816    0.2850    0.3826       200\n",
      "           3     0.5347    0.5400    0.5373       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8660    0.8400    0.8528       200\n",
      "           6     0.4293    0.4250    0.4271       200\n",
      "           7     0.4463    0.3950    0.4191       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5896    0.5806    0.5758      1600\n",
      "weighted avg     0.5896    0.5806    0.5758      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4014    0.5900    0.4777       200\n",
      "           1     0.5696    0.6750    0.6178       200\n",
      "           2     0.5684    0.2700    0.3661       200\n",
      "           3     0.5317    0.5450    0.5383       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8660    0.8400    0.8528       200\n",
      "           6     0.4365    0.4300    0.4332       200\n",
      "           7     0.4686    0.4100    0.4373       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5893    0.5806    0.5752      1600\n",
      "weighted avg     0.5893    0.5806    0.5752      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4054    0.6000    0.4839       200\n",
      "           1     0.5708    0.6850    0.6227       200\n",
      "           2     0.6023    0.2650    0.3681       200\n",
      "           3     0.5243    0.5400    0.5320       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8660    0.8400    0.8528       200\n",
      "           6     0.4330    0.4200    0.4264       200\n",
      "           7     0.4581    0.4100    0.4327       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5915    0.5806    0.5746      1600\n",
      "weighted avg     0.5915    0.5806    0.5746      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4040    0.6000    0.4829       200\n",
      "           1     0.5769    0.6750    0.6221       200\n",
      "           2     0.5670    0.2750    0.3704       200\n",
      "           3     0.5441    0.5550    0.5495       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8660    0.8400    0.8528       200\n",
      "           6     0.4343    0.4300    0.4322       200\n",
      "           7     0.4566    0.3950    0.4236       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5901    0.5819    0.5765      1600\n",
      "weighted avg     0.5901    0.5819    0.5765      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4014    0.5900    0.4777       200\n",
      "           1     0.5696    0.6750    0.6178       200\n",
      "           2     0.5745    0.2700    0.3673       200\n",
      "           3     0.5243    0.5400    0.5320       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8660    0.8400    0.8528       200\n",
      "           6     0.4365    0.4300    0.4332       200\n",
      "           7     0.4629    0.4050    0.4320       200\n",
      "\n",
      "    accuracy                         0.5794      1600\n",
      "   macro avg     0.5884    0.5794    0.5739      1600\n",
      "weighted avg     0.5884    0.5794    0.5739      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4054    0.6000    0.4839       200\n",
      "           1     0.5708    0.6850    0.6227       200\n",
      "           2     0.6023    0.2650    0.3681       200\n",
      "           3     0.5243    0.5400    0.5320       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8660    0.8400    0.8528       200\n",
      "           6     0.4330    0.4200    0.4264       200\n",
      "           7     0.4581    0.4100    0.4327       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5915    0.5806    0.5746      1600\n",
      "weighted avg     0.5915    0.5806    0.5746      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4040    0.6000    0.4829       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5670    0.2750    0.3704       200\n",
      "           3     0.5340    0.5500    0.5419       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8660    0.8400    0.8528       200\n",
      "           6     0.4365    0.4300    0.4332       200\n",
      "           7     0.4678    0.4000    0.4313       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5902    0.5819    0.5764      1600\n",
      "weighted avg     0.5902    0.5819    0.5764      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4034    0.5950    0.4808       200\n",
      "           1     0.5696    0.6750    0.6178       200\n",
      "           2     0.5806    0.2700    0.3686       200\n",
      "           3     0.5291    0.5450    0.5369       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8660    0.8400    0.8528       200\n",
      "           6     0.4365    0.4300    0.4332       200\n",
      "           7     0.4743    0.4150    0.4427       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5914    0.5819    0.5764      1600\n",
      "weighted avg     0.5914    0.5819    0.5764      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4068    0.6000    0.4848       200\n",
      "           1     0.5708    0.6850    0.6227       200\n",
      "           2     0.6023    0.2650    0.3681       200\n",
      "           3     0.5217    0.5400    0.5307       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8660    0.8400    0.8528       200\n",
      "           6     0.4330    0.4200    0.4264       200\n",
      "           7     0.4581    0.4100    0.4327       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5913    0.5806    0.5746      1600\n",
      "weighted avg     0.5913    0.5806    0.5746      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4242    0.5600    0.4828       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5108    0.3550    0.4189       200\n",
      "           3     0.5746    0.5200    0.5459       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4577    0.4600    0.4589       200\n",
      "           7     0.4468    0.4200    0.4330       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5901    0.5869    0.5849      1600\n",
      "weighted avg     0.5901    0.5869    0.5849      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4111    0.5550    0.4723       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5698    0.5100    0.5383       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4495    0.4450    0.4472       200\n",
      "           7     0.4362    0.4100    0.4227       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5856    0.5819    0.5799      1600\n",
      "weighted avg     0.5856    0.5819    0.5799      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4157    0.5550    0.4754       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5147    0.3500    0.4167       200\n",
      "           3     0.5668    0.5300    0.5478       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4554    0.4600    0.4577       200\n",
      "           7     0.4556    0.4100    0.4316       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5906    0.5875    0.5852      1600\n",
      "weighted avg     0.5906    0.5875    0.5852      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4164    0.5600    0.4776       200\n",
      "           1     0.5733    0.6450    0.6071       200\n",
      "           2     0.4966    0.3600    0.4174       200\n",
      "           3     0.5690    0.4950    0.5294       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4550    0.4550    0.4550       200\n",
      "           7     0.4286    0.4050    0.4165       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5847    0.5813    0.5796      1600\n",
      "weighted avg     0.5847    0.5813    0.5796      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4189    0.5550    0.4774       200\n",
      "           1     0.5785    0.6450    0.6099       200\n",
      "           2     0.5000    0.3650    0.4220       200\n",
      "           3     0.5843    0.5200    0.5503       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4510    0.4600    0.4554       200\n",
      "           7     0.4462    0.4150    0.4301       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5897    0.5862    0.5849      1600\n",
      "weighted avg     0.5897    0.5863    0.5849      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4157    0.5550    0.4754       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5147    0.3500    0.4167       200\n",
      "           3     0.5668    0.5300    0.5478       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4554    0.4600    0.4577       200\n",
      "           7     0.4556    0.4100    0.4316       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5906    0.5875    0.5852      1600\n",
      "weighted avg     0.5906    0.5875    0.5852      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4195    0.5600    0.4797       200\n",
      "           1     0.5708    0.6450    0.6056       200\n",
      "           2     0.5034    0.3650    0.4232       200\n",
      "           3     0.5714    0.5000    0.5333       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4488    0.4600    0.4543       200\n",
      "           7     0.4432    0.4100    0.4260       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5875    0.5837    0.5823      1600\n",
      "weighted avg     0.5875    0.5837    0.5823      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4173    0.5550    0.4764       200\n",
      "           1     0.5733    0.6450    0.6071       200\n",
      "           2     0.5034    0.3650    0.4232       200\n",
      "           3     0.5611    0.5050    0.5316       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4519    0.4700    0.4608       200\n",
      "           7     0.4551    0.4050    0.4286       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5876    0.5844    0.5827      1600\n",
      "weighted avg     0.5876    0.5844    0.5827      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=8, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4216    0.5650    0.4829       200\n",
      "           1     0.5771    0.6550    0.6136       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5730    0.5300    0.5506       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4527    0.4550    0.4539       200\n",
      "           7     0.4481    0.4100    0.4282       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5898    0.5869    0.5847      1600\n",
      "weighted avg     0.5898    0.5869    0.5847      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4208    0.5450    0.4749       200\n",
      "           1     0.5841    0.6600    0.6197       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5673    0.4850    0.5229       200\n",
      "           4     0.8706    0.8750    0.8728       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4476    0.4700    0.4585       200\n",
      "           7     0.4619    0.4550    0.4584       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5891    0.5856    0.5839      1600\n",
      "weighted avg     0.5891    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4237    0.5550    0.4805       200\n",
      "           1     0.5841    0.6600    0.6197       200\n",
      "           2     0.5185    0.3500    0.4179       200\n",
      "           3     0.5729    0.5500    0.5612       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8492    0.8450    0.8471       200\n",
      "           6     0.4563    0.4700    0.4631       200\n",
      "           7     0.4807    0.4350    0.4567       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5950    0.5919    0.5898      1600\n",
      "weighted avg     0.5950    0.5919    0.5898      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4237    0.5550    0.4805       200\n",
      "           1     0.5956    0.6700    0.6306       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5736    0.5650    0.5693       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4608    0.4700    0.4653       200\n",
      "           7     0.4802    0.4250    0.4509       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5970    0.5944    0.5922      1600\n",
      "weighted avg     0.5970    0.5944    0.5922      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4192    0.5450    0.4739       200\n",
      "           1     0.5911    0.6650    0.6259       200\n",
      "           2     0.5255    0.3600    0.4273       200\n",
      "           3     0.5829    0.5100    0.5440       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8492    0.8450    0.8471       200\n",
      "           6     0.4476    0.4700    0.4585       200\n",
      "           7     0.4691    0.4550    0.4619       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5943    0.5900    0.5886      1600\n",
      "weighted avg     0.5943    0.5900    0.5886      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4237    0.5550    0.4805       200\n",
      "           1     0.5848    0.6550    0.6179       200\n",
      "           2     0.5259    0.3550    0.4239       200\n",
      "           3     0.5608    0.5300    0.5450       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8492    0.8450    0.8471       200\n",
      "           6     0.4510    0.4600    0.4554       200\n",
      "           7     0.4628    0.4350    0.4485       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5916    0.5881    0.5863      1600\n",
      "weighted avg     0.5916    0.5881    0.5863      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4237    0.5550    0.4805       200\n",
      "           1     0.5956    0.6700    0.6306       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5736    0.5650    0.5693       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4608    0.4700    0.4653       200\n",
      "           7     0.4802    0.4250    0.4509       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5970    0.5944    0.5922      1600\n",
      "weighted avg     0.5970    0.5944    0.5922      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5991    0.6650    0.6303       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5683    0.5200    0.5431       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8492    0.8450    0.8471       200\n",
      "           6     0.4541    0.4700    0.4619       200\n",
      "           7     0.4635    0.4450    0.4541       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5940    0.5906    0.5888      1600\n",
      "weighted avg     0.5940    0.5906    0.5888      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5973    0.6600    0.6271       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5648    0.5450    0.5547       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4585    0.4700    0.4642       200\n",
      "           7     0.4652    0.4350    0.4496       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5951    0.5919    0.5902      1600\n",
      "weighted avg     0.5951    0.5919    0.5902      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4215    0.5500    0.4772       200\n",
      "           1     0.6009    0.6700    0.6336       200\n",
      "           2     0.5108    0.3550    0.4189       200\n",
      "           3     0.5714    0.5600    0.5657       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4608    0.4700    0.4653       200\n",
      "           7     0.4804    0.4300    0.4538       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5962    0.5938    0.5917      1600\n",
      "weighted avg     0.5962    0.5938    0.5917      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4208    0.5450    0.4749       200\n",
      "           1     0.5841    0.6600    0.6197       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5673    0.4850    0.5229       200\n",
      "           4     0.8706    0.8750    0.8728       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4476    0.4700    0.4585       200\n",
      "           7     0.4619    0.4550    0.4584       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5891    0.5856    0.5839      1600\n",
      "weighted avg     0.5891    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4237    0.5550    0.4805       200\n",
      "           1     0.5841    0.6600    0.6197       200\n",
      "           2     0.5185    0.3500    0.4179       200\n",
      "           3     0.5729    0.5500    0.5612       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8492    0.8450    0.8471       200\n",
      "           6     0.4563    0.4700    0.4631       200\n",
      "           7     0.4807    0.4350    0.4567       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5950    0.5919    0.5898      1600\n",
      "weighted avg     0.5950    0.5919    0.5898      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4237    0.5550    0.4805       200\n",
      "           1     0.5956    0.6700    0.6306       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5736    0.5650    0.5693       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4608    0.4700    0.4653       200\n",
      "           7     0.4802    0.4250    0.4509       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5970    0.5944    0.5922      1600\n",
      "weighted avg     0.5970    0.5944    0.5922      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4192    0.5450    0.4739       200\n",
      "           1     0.5911    0.6650    0.6259       200\n",
      "           2     0.5255    0.3600    0.4273       200\n",
      "           3     0.5829    0.5100    0.5440       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8492    0.8450    0.8471       200\n",
      "           6     0.4476    0.4700    0.4585       200\n",
      "           7     0.4691    0.4550    0.4619       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5943    0.5900    0.5886      1600\n",
      "weighted avg     0.5943    0.5900    0.5886      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4237    0.5550    0.4805       200\n",
      "           1     0.5848    0.6550    0.6179       200\n",
      "           2     0.5259    0.3550    0.4239       200\n",
      "           3     0.5608    0.5300    0.5450       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8492    0.8450    0.8471       200\n",
      "           6     0.4510    0.4600    0.4554       200\n",
      "           7     0.4628    0.4350    0.4485       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5916    0.5881    0.5863      1600\n",
      "weighted avg     0.5916    0.5881    0.5863      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4237    0.5550    0.4805       200\n",
      "           1     0.5956    0.6700    0.6306       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5736    0.5650    0.5693       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4608    0.4700    0.4653       200\n",
      "           7     0.4802    0.4250    0.4509       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5970    0.5944    0.5922      1600\n",
      "weighted avg     0.5970    0.5944    0.5922      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5991    0.6650    0.6303       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5683    0.5200    0.5431       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8492    0.8450    0.8471       200\n",
      "           6     0.4541    0.4700    0.4619       200\n",
      "           7     0.4635    0.4450    0.4541       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5940    0.5906    0.5888      1600\n",
      "weighted avg     0.5940    0.5906    0.5888      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5973    0.6600    0.6271       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5648    0.5450    0.5547       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4585    0.4700    0.4642       200\n",
      "           7     0.4652    0.4350    0.4496       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5951    0.5919    0.5902      1600\n",
      "weighted avg     0.5951    0.5919    0.5902      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4215    0.5500    0.4772       200\n",
      "           1     0.6009    0.6700    0.6336       200\n",
      "           2     0.5108    0.3550    0.4189       200\n",
      "           3     0.5714    0.5600    0.5657       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4608    0.4700    0.4653       200\n",
      "           7     0.4804    0.4300    0.4538       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5962    0.5938    0.5917      1600\n",
      "weighted avg     0.5962    0.5938    0.5917      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4247    0.5500    0.4793       200\n",
      "           1     0.5833    0.6650    0.6215       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5789    0.4950    0.5337       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8492    0.8450    0.8471       200\n",
      "           6     0.4476    0.4700    0.4585       200\n",
      "           7     0.4667    0.4550    0.4608       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5910    0.5875    0.5856      1600\n",
      "weighted avg     0.5910    0.5875    0.5856      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4237    0.5550    0.4805       200\n",
      "           1     0.5841    0.6600    0.6197       200\n",
      "           2     0.5185    0.3500    0.4179       200\n",
      "           3     0.5684    0.5400    0.5538       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8492    0.8450    0.8471       200\n",
      "           6     0.4563    0.4700    0.4631       200\n",
      "           7     0.4754    0.4350    0.4543       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5938    0.5906    0.5886      1600\n",
      "weighted avg     0.5938    0.5906    0.5886      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4237    0.5550    0.4805       200\n",
      "           1     0.5956    0.6700    0.6306       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5707    0.5650    0.5678       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4631    0.4700    0.4665       200\n",
      "           7     0.4802    0.4250    0.4509       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5969    0.5944    0.5921      1600\n",
      "weighted avg     0.5969    0.5944    0.5921      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4192    0.5450    0.4739       200\n",
      "           1     0.5938    0.6650    0.6274       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5763    0.5100    0.5411       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8492    0.8450    0.8471       200\n",
      "           6     0.4519    0.4700    0.4608       200\n",
      "           7     0.4663    0.4500    0.4580       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5931    0.5894    0.5879      1600\n",
      "weighted avg     0.5931    0.5894    0.5879      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5867    0.6600    0.6212       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5632    0.5350    0.5487       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8492    0.8450    0.8471       200\n",
      "           6     0.4510    0.4600    0.4554       200\n",
      "           7     0.4628    0.4350    0.4485       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5928    0.5894    0.5875      1600\n",
      "weighted avg     0.5928    0.5894    0.5875      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4237    0.5550    0.4805       200\n",
      "           1     0.5956    0.6700    0.6306       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5707    0.5650    0.5678       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4631    0.4700    0.4665       200\n",
      "           7     0.4802    0.4250    0.4509       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5969    0.5944    0.5921      1600\n",
      "weighted avg     0.5969    0.5944    0.5921      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5964    0.6650    0.6288       200\n",
      "           2     0.5147    0.3500    0.4167       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8492    0.8450    0.8471       200\n",
      "           6     0.4541    0.4700    0.4619       200\n",
      "           7     0.4579    0.4350    0.4462       200\n",
      "\n",
      "    accuracy                         0.5887      1600\n",
      "   macro avg     0.5916    0.5888    0.5867      1600\n",
      "weighted avg     0.5916    0.5887    0.5867      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5919    0.6600    0.6241       200\n",
      "           2     0.5259    0.3550    0.4239       200\n",
      "           3     0.5648    0.5450    0.5547       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4563    0.4700    0.4631       200\n",
      "           7     0.4649    0.4300    0.4468       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5946    0.5913    0.5894      1600\n",
      "weighted avg     0.5946    0.5913    0.5894      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4215    0.5500    0.4772       200\n",
      "           1     0.5982    0.6700    0.6321       200\n",
      "           2     0.5145    0.3550    0.4201       200\n",
      "           3     0.5714    0.5600    0.5657       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4608    0.4700    0.4653       200\n",
      "           7     0.4804    0.4300    0.4538       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5963    0.5938    0.5917      1600\n",
      "weighted avg     0.5963    0.5938    0.5917      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4040    0.6000    0.4829       200\n",
      "           1     0.5643    0.6800    0.6168       200\n",
      "           2     0.5600    0.2800    0.3733       200\n",
      "           3     0.5385    0.5600    0.5490       200\n",
      "           4     0.8713    0.8800    0.8756       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4427    0.4250    0.4337       200\n",
      "           7     0.4877    0.3950    0.4365       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5903    0.5831    0.5771      1600\n",
      "weighted avg     0.5903    0.5831    0.5771      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4040    0.6000    0.4829       200\n",
      "           1     0.5661    0.6850    0.6199       200\n",
      "           2     0.5670    0.2750    0.3704       200\n",
      "           3     0.5362    0.5550    0.5455       200\n",
      "           4     0.8713    0.8800    0.8756       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4503    0.4300    0.4399       200\n",
      "           7     0.4880    0.4050    0.4426       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5921    0.5844    0.5783      1600\n",
      "weighted avg     0.5921    0.5844    0.5783      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4020    0.5950    0.4798       200\n",
      "           1     0.5622    0.7000    0.6236       200\n",
      "           2     0.6136    0.2700    0.3750       200\n",
      "           3     0.5327    0.5700    0.5507       200\n",
      "           4     0.8713    0.8800    0.8756       200\n",
      "           5     0.8450    0.8450    0.8450       200\n",
      "           6     0.4550    0.4300    0.4422       200\n",
      "           7     0.4938    0.4000    0.4420       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5970    0.5862    0.5792      1600\n",
      "weighted avg     0.5970    0.5863    0.5792      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4040    0.6000    0.4829       200\n",
      "           1     0.5615    0.6850    0.6171       200\n",
      "           2     0.5600    0.2800    0.3733       200\n",
      "           3     0.5545    0.5600    0.5572       200\n",
      "           4     0.8713    0.8800    0.8756       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4479    0.4300    0.4388       200\n",
      "           7     0.4970    0.4100    0.4493       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5937    0.5862    0.5804      1600\n",
      "weighted avg     0.5937    0.5863    0.5804      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4040    0.6000    0.4829       200\n",
      "           1     0.5661    0.6850    0.6199       200\n",
      "           2     0.5670    0.2750    0.3704       200\n",
      "           3     0.5337    0.5550    0.5441       200\n",
      "           4     0.8713    0.8800    0.8756       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4503    0.4300    0.4399       200\n",
      "           7     0.4909    0.4050    0.4438       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5921    0.5844    0.5782      1600\n",
      "weighted avg     0.5921    0.5844    0.5782      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4020    0.5950    0.4798       200\n",
      "           1     0.5622    0.7000    0.6236       200\n",
      "           2     0.6136    0.2700    0.3750       200\n",
      "           3     0.5327    0.5700    0.5507       200\n",
      "           4     0.8713    0.8800    0.8756       200\n",
      "           5     0.8450    0.8450    0.8450       200\n",
      "           6     0.4550    0.4300    0.4422       200\n",
      "           7     0.4938    0.4000    0.4420       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5970    0.5862    0.5792      1600\n",
      "weighted avg     0.5970    0.5863    0.5792      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4054    0.6000    0.4839       200\n",
      "           1     0.5638    0.6850    0.6185       200\n",
      "           2     0.5670    0.2750    0.3704       200\n",
      "           3     0.5415    0.5550    0.5481       200\n",
      "           4     0.8713    0.8800    0.8756       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4372    0.4350    0.4361       200\n",
      "           7     0.4813    0.3850    0.4278       200\n",
      "\n",
      "    accuracy                         0.5825      1600\n",
      "   macro avg     0.5901    0.5825    0.5762      1600\n",
      "weighted avg     0.5901    0.5825    0.5762      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4034    0.5950    0.4808       200\n",
      "           1     0.5726    0.6900    0.6259       200\n",
      "           2     0.5789    0.2750    0.3729       200\n",
      "           3     0.5359    0.5600    0.5477       200\n",
      "           4     0.8713    0.8800    0.8756       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4541    0.4450    0.4495       200\n",
      "           7     0.4878    0.4000    0.4396       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5947    0.5863    0.5801      1600\n",
      "weighted avg     0.5947    0.5863    0.5801      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4020    0.5950    0.4798       200\n",
      "           1     0.5622    0.7000    0.6236       200\n",
      "           2     0.6136    0.2700    0.3750       200\n",
      "           3     0.5308    0.5600    0.5450       200\n",
      "           4     0.8713    0.8800    0.8756       200\n",
      "           5     0.8450    0.8450    0.8450       200\n",
      "           6     0.4579    0.4350    0.4462       200\n",
      "           7     0.4878    0.4000    0.4396       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5963    0.5856    0.5787      1600\n",
      "weighted avg     0.5963    0.5856    0.5787      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4208    0.5450    0.4749       200\n",
      "           1     0.5841    0.6600    0.6197       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5673    0.4850    0.5229       200\n",
      "           4     0.8706    0.8750    0.8728       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4476    0.4700    0.4585       200\n",
      "           7     0.4619    0.4550    0.4584       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5891    0.5856    0.5839      1600\n",
      "weighted avg     0.5891    0.5856    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4237    0.5550    0.4805       200\n",
      "           1     0.5841    0.6600    0.6197       200\n",
      "           2     0.5185    0.3500    0.4179       200\n",
      "           3     0.5729    0.5500    0.5612       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8492    0.8450    0.8471       200\n",
      "           6     0.4563    0.4700    0.4631       200\n",
      "           7     0.4807    0.4350    0.4567       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5950    0.5919    0.5898      1600\n",
      "weighted avg     0.5950    0.5919    0.5898      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4237    0.5550    0.4805       200\n",
      "           1     0.5956    0.6700    0.6306       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5736    0.5650    0.5693       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4608    0.4700    0.4653       200\n",
      "           7     0.4802    0.4250    0.4509       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5970    0.5944    0.5922      1600\n",
      "weighted avg     0.5970    0.5944    0.5922      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4192    0.5450    0.4739       200\n",
      "           1     0.5911    0.6650    0.6259       200\n",
      "           2     0.5255    0.3600    0.4273       200\n",
      "           3     0.5829    0.5100    0.5440       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8492    0.8450    0.8471       200\n",
      "           6     0.4476    0.4700    0.4585       200\n",
      "           7     0.4691    0.4550    0.4619       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5943    0.5900    0.5886      1600\n",
      "weighted avg     0.5943    0.5900    0.5886      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4237    0.5550    0.4805       200\n",
      "           1     0.5848    0.6550    0.6179       200\n",
      "           2     0.5259    0.3550    0.4239       200\n",
      "           3     0.5608    0.5300    0.5450       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8492    0.8450    0.8471       200\n",
      "           6     0.4510    0.4600    0.4554       200\n",
      "           7     0.4628    0.4350    0.4485       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5916    0.5881    0.5863      1600\n",
      "weighted avg     0.5916    0.5881    0.5863      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4237    0.5550    0.4805       200\n",
      "           1     0.5956    0.6700    0.6306       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5736    0.5650    0.5693       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4608    0.4700    0.4653       200\n",
      "           7     0.4802    0.4250    0.4509       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5970    0.5944    0.5922      1600\n",
      "weighted avg     0.5970    0.5944    0.5922      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5991    0.6650    0.6303       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5683    0.5200    0.5431       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8492    0.8450    0.8471       200\n",
      "           6     0.4541    0.4700    0.4619       200\n",
      "           7     0.4635    0.4450    0.4541       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5940    0.5906    0.5888      1600\n",
      "weighted avg     0.5940    0.5906    0.5888      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5973    0.6600    0.6271       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5648    0.5450    0.5547       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4585    0.4700    0.4642       200\n",
      "           7     0.4652    0.4350    0.4496       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5951    0.5919    0.5902      1600\n",
      "weighted avg     0.5951    0.5919    0.5902      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=12, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4215    0.5500    0.4772       200\n",
      "           1     0.6009    0.6700    0.6336       200\n",
      "           2     0.5108    0.3550    0.4189       200\n",
      "           3     0.5714    0.5600    0.5657       200\n",
      "           4     0.8700    0.8700    0.8700       200\n",
      "           5     0.8535    0.8450    0.8492       200\n",
      "           6     0.4608    0.4700    0.4653       200\n",
      "           7     0.4804    0.4300    0.4538       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5962    0.5938    0.5917      1600\n",
      "weighted avg     0.5962    0.5938    0.5917      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 60.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4723    0.5550    0.5103       200\n",
      "           1     0.5923    0.6900    0.6374       200\n",
      "           2     0.5405    0.4000    0.4598       200\n",
      "           3     0.5926    0.4800    0.5304       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4583    0.4950    0.4760       200\n",
      "           7     0.4498    0.4700    0.4597       200\n",
      "\n",
      "    accuracy                         0.6025      1600\n",
      "   macro avg     0.6061    0.6025    0.6012      1600\n",
      "weighted avg     0.6061    0.6025    0.6012      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 60.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4752    0.5750    0.5204       200\n",
      "           1     0.5774    0.6900    0.6287       200\n",
      "           2     0.5442    0.4000    0.4611       200\n",
      "           3     0.5611    0.5050    0.5316       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4591    0.5050    0.4810       200\n",
      "           7     0.4773    0.4200    0.4468       200\n",
      "\n",
      "    accuracy                         0.6031      1600\n",
      "   macro avg     0.6052    0.6031    0.6010      1600\n",
      "weighted avg     0.6052    0.6031    0.6010      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 60.62%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4741    0.5950    0.5277       200\n",
      "           1     0.5865    0.6950    0.6362       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5579    0.5300    0.5436       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4587    0.5000    0.4785       200\n",
      "           7     0.5127    0.4050    0.4525       200\n",
      "\n",
      "    accuracy                         0.6062      1600\n",
      "   macro avg     0.6079    0.6062    0.6034      1600\n",
      "weighted avg     0.6079    0.6062    0.6034      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4721    0.5500    0.5081       200\n",
      "           1     0.5823    0.6900    0.6316       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5765    0.4900    0.5297       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4636    0.5100    0.4857       200\n",
      "           7     0.4639    0.4500    0.4569       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6040    0.6019    0.6001      1600\n",
      "weighted avg     0.6040    0.6019    0.6001      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 60.12%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5823    0.6900    0.6316       200\n",
      "           2     0.5333    0.4000    0.4571       200\n",
      "           3     0.5650    0.5000    0.5305       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4591    0.5050    0.4810       200\n",
      "           7     0.4641    0.4200    0.4409       200\n",
      "\n",
      "    accuracy                         0.6012      1600\n",
      "   macro avg     0.6027    0.6013    0.5992      1600\n",
      "weighted avg     0.6027    0.6012    0.5992      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 60.62%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4741    0.5950    0.5277       200\n",
      "           1     0.5865    0.6950    0.6362       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5579    0.5300    0.5436       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4587    0.5000    0.4785       200\n",
      "           7     0.5127    0.4050    0.4525       200\n",
      "\n",
      "    accuracy                         0.6062      1600\n",
      "   macro avg     0.6079    0.6062    0.6034      1600\n",
      "weighted avg     0.6079    0.6062    0.6034      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 60.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4721    0.5500    0.5081       200\n",
      "           1     0.5781    0.6850    0.6270       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5698    0.5100    0.5383       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4840    0.4550    0.4691       200\n",
      "\n",
      "    accuracy                         0.6025      1600\n",
      "   macro avg     0.6042    0.6025    0.6008      1600\n",
      "weighted avg     0.6042    0.6025    0.6008      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 60.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5880    0.6850    0.6328       200\n",
      "           2     0.5369    0.4000    0.4585       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4595    0.5100    0.4834       200\n",
      "           7     0.4859    0.4300    0.4562       200\n",
      "\n",
      "    accuracy                         0.6050      1600\n",
      "   macro avg     0.6067    0.6050    0.6031      1600\n",
      "weighted avg     0.6067    0.6050    0.6031      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 60.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4741    0.5950    0.5277       200\n",
      "           1     0.5840    0.6950    0.6347       200\n",
      "           2     0.5338    0.3950    0.4540       200\n",
      "           3     0.5550    0.5300    0.5422       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4587    0.5000    0.4785       200\n",
      "           7     0.5096    0.4000    0.4482       200\n",
      "\n",
      "    accuracy                         0.6056      1600\n",
      "   macro avg     0.6073    0.6056    0.6027      1600\n",
      "weighted avg     0.6073    0.6056    0.6027      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 60.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4723    0.5550    0.5103       200\n",
      "           1     0.5923    0.6900    0.6374       200\n",
      "           2     0.5405    0.4000    0.4598       200\n",
      "           3     0.5926    0.4800    0.5304       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4583    0.4950    0.4760       200\n",
      "           7     0.4498    0.4700    0.4597       200\n",
      "\n",
      "    accuracy                         0.6025      1600\n",
      "   macro avg     0.6061    0.6025    0.6012      1600\n",
      "weighted avg     0.6061    0.6025    0.6012      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 60.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4752    0.5750    0.5204       200\n",
      "           1     0.5774    0.6900    0.6287       200\n",
      "           2     0.5442    0.4000    0.4611       200\n",
      "           3     0.5611    0.5050    0.5316       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4591    0.5050    0.4810       200\n",
      "           7     0.4773    0.4200    0.4468       200\n",
      "\n",
      "    accuracy                         0.6031      1600\n",
      "   macro avg     0.6052    0.6031    0.6010      1600\n",
      "weighted avg     0.6052    0.6031    0.6010      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 60.62%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4741    0.5950    0.5277       200\n",
      "           1     0.5865    0.6950    0.6362       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5579    0.5300    0.5436       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4587    0.5000    0.4785       200\n",
      "           7     0.5127    0.4050    0.4525       200\n",
      "\n",
      "    accuracy                         0.6062      1600\n",
      "   macro avg     0.6079    0.6062    0.6034      1600\n",
      "weighted avg     0.6079    0.6062    0.6034      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4721    0.5500    0.5081       200\n",
      "           1     0.5823    0.6900    0.6316       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5765    0.4900    0.5297       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4636    0.5100    0.4857       200\n",
      "           7     0.4639    0.4500    0.4569       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6040    0.6019    0.6001      1600\n",
      "weighted avg     0.6040    0.6019    0.6001      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 60.12%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5823    0.6900    0.6316       200\n",
      "           2     0.5333    0.4000    0.4571       200\n",
      "           3     0.5650    0.5000    0.5305       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4591    0.5050    0.4810       200\n",
      "           7     0.4641    0.4200    0.4409       200\n",
      "\n",
      "    accuracy                         0.6012      1600\n",
      "   macro avg     0.6027    0.6013    0.5992      1600\n",
      "weighted avg     0.6027    0.6012    0.5992      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 60.62%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4741    0.5950    0.5277       200\n",
      "           1     0.5865    0.6950    0.6362       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5579    0.5300    0.5436       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4587    0.5000    0.4785       200\n",
      "           7     0.5127    0.4050    0.4525       200\n",
      "\n",
      "    accuracy                         0.6062      1600\n",
      "   macro avg     0.6079    0.6062    0.6034      1600\n",
      "weighted avg     0.6079    0.6062    0.6034      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 60.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4721    0.5500    0.5081       200\n",
      "           1     0.5781    0.6850    0.6270       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5698    0.5100    0.5383       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4840    0.4550    0.4691       200\n",
      "\n",
      "    accuracy                         0.6025      1600\n",
      "   macro avg     0.6042    0.6025    0.6008      1600\n",
      "weighted avg     0.6042    0.6025    0.6008      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 60.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5880    0.6850    0.6328       200\n",
      "           2     0.5369    0.4000    0.4585       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4595    0.5100    0.4834       200\n",
      "           7     0.4859    0.4300    0.4562       200\n",
      "\n",
      "    accuracy                         0.6050      1600\n",
      "   macro avg     0.6067    0.6050    0.6031      1600\n",
      "weighted avg     0.6067    0.6050    0.6031      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 60.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4741    0.5950    0.5277       200\n",
      "           1     0.5840    0.6950    0.6347       200\n",
      "           2     0.5338    0.3950    0.4540       200\n",
      "           3     0.5550    0.5300    0.5422       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4587    0.5000    0.4785       200\n",
      "           7     0.5096    0.4000    0.4482       200\n",
      "\n",
      "    accuracy                         0.6056      1600\n",
      "   macro avg     0.6073    0.6056    0.6027      1600\n",
      "weighted avg     0.6073    0.6056    0.6027      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5887    0.6800    0.6311       200\n",
      "           2     0.5329    0.4050    0.4602       200\n",
      "           3     0.5843    0.4850    0.5301       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4605    0.4950    0.4771       200\n",
      "           7     0.4505    0.4550    0.4527       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6049    0.6019    0.6007      1600\n",
      "weighted avg     0.6049    0.6019    0.6007      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 60.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4792    0.5750    0.5227       200\n",
      "           1     0.5750    0.6900    0.6273       200\n",
      "           2     0.5374    0.3950    0.4553       200\n",
      "           3     0.5611    0.5050    0.5316       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4591    0.5050    0.4810       200\n",
      "           7     0.4746    0.4200    0.4456       200\n",
      "\n",
      "    accuracy                         0.6025      1600\n",
      "   macro avg     0.6042    0.6025    0.6002      1600\n",
      "weighted avg     0.6042    0.6025    0.6002      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 60.62%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4741    0.5950    0.5277       200\n",
      "           1     0.5865    0.6950    0.6362       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5579    0.5300    0.5436       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4587    0.5000    0.4785       200\n",
      "           7     0.5127    0.4050    0.4525       200\n",
      "\n",
      "    accuracy                         0.6062      1600\n",
      "   macro avg     0.6079    0.6062    0.6034      1600\n",
      "weighted avg     0.6079    0.6062    0.6034      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 60.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4721    0.5500    0.5081       200\n",
      "           1     0.5823    0.6900    0.6316       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5731    0.4900    0.5283       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4592    0.4500    0.4545       200\n",
      "\n",
      "    accuracy                         0.6000      1600\n",
      "   macro avg     0.6020    0.6000    0.5983      1600\n",
      "weighted avg     0.6020    0.6000    0.5983      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 60.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5823    0.6900    0.6316       200\n",
      "           2     0.5333    0.4000    0.4571       200\n",
      "           3     0.5618    0.5000    0.5291       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4566    0.5000    0.4773       200\n",
      "           7     0.4641    0.4200    0.4409       200\n",
      "\n",
      "    accuracy                         0.6006      1600\n",
      "   macro avg     0.6020    0.6006    0.5985      1600\n",
      "weighted avg     0.6020    0.6006    0.5985      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 60.62%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4741    0.5950    0.5277       200\n",
      "           1     0.5865    0.6950    0.6362       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5579    0.5300    0.5436       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4587    0.5000    0.4785       200\n",
      "           7     0.5127    0.4050    0.4525       200\n",
      "\n",
      "    accuracy                         0.6062      1600\n",
      "   macro avg     0.6079    0.6062    0.6034      1600\n",
      "weighted avg     0.6079    0.6062    0.6034      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 60.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4721    0.5500    0.5081       200\n",
      "           1     0.5805    0.6850    0.6284       200\n",
      "           2     0.5267    0.3950    0.4514       200\n",
      "           3     0.5698    0.5100    0.5383       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4840    0.4550    0.4691       200\n",
      "\n",
      "    accuracy                         0.6025      1600\n",
      "   macro avg     0.6041    0.6025    0.6008      1600\n",
      "weighted avg     0.6041    0.6025    0.6008      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 60.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5880    0.6850    0.6328       200\n",
      "           2     0.5333    0.4000    0.4571       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4615    0.5100    0.4846       200\n",
      "           7     0.4859    0.4300    0.4562       200\n",
      "\n",
      "    accuracy                         0.6050      1600\n",
      "   macro avg     0.6065    0.6050    0.6031      1600\n",
      "weighted avg     0.6065    0.6050    0.6031      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 60.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4741    0.5950    0.5277       200\n",
      "           1     0.5840    0.6950    0.6347       200\n",
      "           2     0.5338    0.3950    0.4540       200\n",
      "           3     0.5550    0.5300    0.5422       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4587    0.5000    0.4785       200\n",
      "           7     0.5096    0.4000    0.4482       200\n",
      "\n",
      "    accuracy                         0.6056      1600\n",
      "   macro avg     0.6073    0.6056    0.6027      1600\n",
      "weighted avg     0.6073    0.6056    0.6027      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4321    0.6200    0.5092       200\n",
      "           1     0.5600    0.7000    0.6222       200\n",
      "           2     0.5926    0.2400    0.3416       200\n",
      "           3     0.5311    0.5550    0.5428       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4512    0.4850    0.4675       200\n",
      "           7     0.5125    0.4100    0.4556       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.6023    0.5925    0.5841      1600\n",
      "weighted avg     0.6023    0.5925    0.5841      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4355    0.6250    0.5133       200\n",
      "           1     0.5663    0.7050    0.6281       200\n",
      "           2     0.6000    0.2400    0.3429       200\n",
      "           3     0.5190    0.5450    0.5317       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4491    0.4850    0.4663       200\n",
      "           7     0.5000    0.4000    0.4444       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.6011    0.5913    0.5826      1600\n",
      "weighted avg     0.6011    0.5913    0.5826      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4355    0.6250    0.5133       200\n",
      "           1     0.5573    0.7050    0.6225       200\n",
      "           2     0.5897    0.2300    0.3309       200\n",
      "           3     0.5213    0.5500    0.5353       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4465    0.4800    0.4627       200\n",
      "           7     0.5063    0.4000    0.4469       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5994    0.5900    0.5807      1600\n",
      "weighted avg     0.5994    0.5900    0.5807      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4340    0.6250    0.5123       200\n",
      "           1     0.5663    0.7050    0.6281       200\n",
      "           2     0.6000    0.2400    0.3429       200\n",
      "           3     0.5291    0.5450    0.5369       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4554    0.4850    0.4697       200\n",
      "           7     0.5000    0.4150    0.4536       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.6029    0.5931    0.5847      1600\n",
      "weighted avg     0.6029    0.5931    0.5847      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4340    0.6250    0.5123       200\n",
      "           1     0.5663    0.7050    0.6281       200\n",
      "           2     0.6000    0.2400    0.3429       200\n",
      "           3     0.5215    0.5450    0.5330       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4491    0.4850    0.4663       200\n",
      "           7     0.5000    0.4000    0.4444       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.6012    0.5913    0.5826      1600\n",
      "weighted avg     0.6012    0.5913    0.5826      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4355    0.6250    0.5133       200\n",
      "           1     0.5573    0.7050    0.6225       200\n",
      "           2     0.5897    0.2300    0.3309       200\n",
      "           3     0.5213    0.5500    0.5353       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4465    0.4800    0.4627       200\n",
      "           7     0.5063    0.4000    0.4469       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5994    0.5900    0.5807      1600\n",
      "weighted avg     0.5994    0.5900    0.5807      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4340    0.6250    0.5123       200\n",
      "           1     0.5685    0.7050    0.6295       200\n",
      "           2     0.6000    0.2400    0.3429       200\n",
      "           3     0.5268    0.5400    0.5333       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4533    0.4850    0.4686       200\n",
      "           7     0.5030    0.4200    0.4578       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.6031    0.5931    0.5848      1600\n",
      "weighted avg     0.6031    0.5931    0.5848      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4355    0.6250    0.5133       200\n",
      "           1     0.5685    0.7050    0.6295       200\n",
      "           2     0.6000    0.2400    0.3429       200\n",
      "           3     0.5192    0.5400    0.5294       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4533    0.4850    0.4686       200\n",
      "           7     0.5030    0.4150    0.4548       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.6023    0.5925    0.5841      1600\n",
      "weighted avg     0.6023    0.5925    0.5841      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4355    0.6250    0.5133       200\n",
      "           1     0.5573    0.7050    0.6225       200\n",
      "           2     0.5897    0.2300    0.3309       200\n",
      "           3     0.5213    0.5500    0.5353       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4486    0.4800    0.4638       200\n",
      "           7     0.5031    0.4000    0.4457       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5993    0.5900    0.5807      1600\n",
      "weighted avg     0.5993    0.5900    0.5807      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 60.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4723    0.5550    0.5103       200\n",
      "           1     0.5923    0.6900    0.6374       200\n",
      "           2     0.5405    0.4000    0.4598       200\n",
      "           3     0.5926    0.4800    0.5304       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4583    0.4950    0.4760       200\n",
      "           7     0.4498    0.4700    0.4597       200\n",
      "\n",
      "    accuracy                         0.6025      1600\n",
      "   macro avg     0.6061    0.6025    0.6012      1600\n",
      "weighted avg     0.6061    0.6025    0.6012      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 60.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4772    0.5750    0.5215       200\n",
      "           1     0.5774    0.6900    0.6287       200\n",
      "           2     0.5442    0.4000    0.4611       200\n",
      "           3     0.5611    0.5050    0.5316       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4591    0.5050    0.4810       200\n",
      "           7     0.4746    0.4200    0.4456       200\n",
      "\n",
      "    accuracy                         0.6031      1600\n",
      "   macro avg     0.6051    0.6031    0.6010      1600\n",
      "weighted avg     0.6051    0.6031    0.6010      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 60.62%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4741    0.5950    0.5277       200\n",
      "           1     0.5865    0.6950    0.6362       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5579    0.5300    0.5436       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4587    0.5000    0.4785       200\n",
      "           7     0.5127    0.4050    0.4525       200\n",
      "\n",
      "    accuracy                         0.6062      1600\n",
      "   macro avg     0.6079    0.6062    0.6034      1600\n",
      "weighted avg     0.6079    0.6062    0.6034      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4721    0.5500    0.5081       200\n",
      "           1     0.5823    0.6900    0.6316       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5765    0.4900    0.5297       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4636    0.5100    0.4857       200\n",
      "           7     0.4639    0.4500    0.4569       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6040    0.6019    0.6001      1600\n",
      "weighted avg     0.6040    0.6019    0.6001      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 60.12%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5823    0.6900    0.6316       200\n",
      "           2     0.5333    0.4000    0.4571       200\n",
      "           3     0.5650    0.5000    0.5305       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4591    0.5050    0.4810       200\n",
      "           7     0.4641    0.4200    0.4409       200\n",
      "\n",
      "    accuracy                         0.6012      1600\n",
      "   macro avg     0.6027    0.6013    0.5992      1600\n",
      "weighted avg     0.6027    0.6012    0.5992      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 60.62%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4741    0.5950    0.5277       200\n",
      "           1     0.5865    0.6950    0.6362       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5579    0.5300    0.5436       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4587    0.5000    0.4785       200\n",
      "           7     0.5127    0.4050    0.4525       200\n",
      "\n",
      "    accuracy                         0.6062      1600\n",
      "   macro avg     0.6079    0.6062    0.6034      1600\n",
      "weighted avg     0.6079    0.6062    0.6034      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 60.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4721    0.5500    0.5081       200\n",
      "           1     0.5781    0.6850    0.6270       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5698    0.5100    0.5383       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4840    0.4550    0.4691       200\n",
      "\n",
      "    accuracy                         0.6025      1600\n",
      "   macro avg     0.6042    0.6025    0.6008      1600\n",
      "weighted avg     0.6042    0.6025    0.6008      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 60.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5880    0.6850    0.6328       200\n",
      "           2     0.5369    0.4000    0.4585       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4595    0.5100    0.4834       200\n",
      "           7     0.4859    0.4300    0.4562       200\n",
      "\n",
      "    accuracy                         0.6050      1600\n",
      "   macro avg     0.6067    0.6050    0.6031      1600\n",
      "weighted avg     0.6067    0.6050    0.6031      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=100, max_features=None, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 60.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4741    0.5950    0.5277       200\n",
      "           1     0.5840    0.6950    0.6347       200\n",
      "           2     0.5338    0.3950    0.4540       200\n",
      "           3     0.5550    0.5300    0.5422       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4587    0.5000    0.4785       200\n",
      "           7     0.5096    0.4000    0.4482       200\n",
      "\n",
      "    accuracy                         0.6056      1600\n",
      "   macro avg     0.6073    0.6056    0.6027      1600\n",
      "weighted avg     0.6073    0.6056    0.6027      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4324    0.5600    0.4880       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5306    0.3900    0.4496       200\n",
      "           3     0.5625    0.5400    0.5510       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4866    0.4550    0.4703       200\n",
      "           7     0.4628    0.4350    0.4485       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.5959    0.5931    0.5914      1600\n",
      "weighted avg     0.5959    0.5931    0.5914      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5600    0.4848       200\n",
      "           1     0.5785    0.6450    0.6099       200\n",
      "           2     0.5166    0.3900    0.4444       200\n",
      "           3     0.5508    0.5150    0.5323       200\n",
      "           4     0.8836    0.8350    0.8586       200\n",
      "           5     0.8278    0.8650    0.8460       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4474    0.4250    0.4359       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5892    0.5863    0.5850      1600\n",
      "weighted avg     0.5892    0.5863    0.5850      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5746    0.6550    0.6121       200\n",
      "           2     0.5034    0.3750    0.4298       200\n",
      "           3     0.5514    0.5100    0.5299       200\n",
      "           4     0.8718    0.8500    0.8608       200\n",
      "           5     0.8374    0.8500    0.8437       200\n",
      "           6     0.4894    0.4600    0.4742       200\n",
      "           7     0.4583    0.4400    0.4490       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5896    0.5875    0.5858      1600\n",
      "weighted avg     0.5896    0.5875    0.5858      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5310    0.3850    0.4464       200\n",
      "           3     0.5508    0.5150    0.5323       200\n",
      "           4     0.8737    0.8300    0.8513       200\n",
      "           5     0.8143    0.8550    0.8341       200\n",
      "           6     0.4712    0.4500    0.4604       200\n",
      "           7     0.4468    0.4200    0.4330       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5858    0.5831    0.5813      1600\n",
      "weighted avg     0.5858    0.5831    0.5813      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4192    0.5450    0.4739       200\n",
      "           1     0.5759    0.6450    0.6085       200\n",
      "           2     0.5099    0.3850    0.4387       200\n",
      "           3     0.5479    0.5150    0.5309       200\n",
      "           4     0.8723    0.8200    0.8454       200\n",
      "           5     0.8075    0.8600    0.8329       200\n",
      "           6     0.4789    0.4550    0.4667       200\n",
      "           7     0.4570    0.4250    0.4404       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5836    0.5813    0.5797      1600\n",
      "weighted avg     0.5836    0.5813    0.5797      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5746    0.6550    0.6121       200\n",
      "           2     0.5034    0.3750    0.4298       200\n",
      "           3     0.5514    0.5100    0.5299       200\n",
      "           4     0.8718    0.8500    0.8608       200\n",
      "           5     0.8374    0.8500    0.8437       200\n",
      "           6     0.4894    0.4600    0.4742       200\n",
      "           7     0.4583    0.4400    0.4490       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5896    0.5875    0.5858      1600\n",
      "weighted avg     0.5896    0.5875    0.5858      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5647    0.6550    0.6065       200\n",
      "           2     0.5352    0.3800    0.4444       200\n",
      "           3     0.5543    0.5100    0.5312       200\n",
      "           4     0.8743    0.8350    0.8542       200\n",
      "           5     0.8182    0.8550    0.8362       200\n",
      "           6     0.4789    0.4550    0.4667       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5877    0.5844    0.5826      1600\n",
      "weighted avg     0.5877    0.5844    0.5826      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5739    0.6600    0.6140       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5495    0.5000    0.5236       200\n",
      "           4     0.8796    0.8400    0.8593       200\n",
      "           5     0.8269    0.8600    0.8431       200\n",
      "           6     0.4817    0.4600    0.4706       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5876    0.5850    0.5832      1600\n",
      "weighted avg     0.5876    0.5850    0.5832      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5550    0.4847       200\n",
      "           1     0.5696    0.6550    0.6093       200\n",
      "           2     0.5103    0.3700    0.4290       200\n",
      "           3     0.5430    0.5050    0.5233       200\n",
      "           4     0.8763    0.8500    0.8629       200\n",
      "           5     0.8341    0.8550    0.8444       200\n",
      "           6     0.4866    0.4550    0.4703       200\n",
      "           7     0.4513    0.4400    0.4456       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5877    0.5856    0.5837      1600\n",
      "weighted avg     0.5877    0.5856    0.5837      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4324    0.5600    0.4880       200\n",
      "           1     0.5739    0.6600    0.6140       200\n",
      "           2     0.5274    0.3850    0.4451       200\n",
      "           3     0.5585    0.5250    0.5412       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4813    0.4500    0.4651       200\n",
      "           7     0.4555    0.4350    0.4450       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5928    0.5900    0.5882      1600\n",
      "weighted avg     0.5928    0.5900    0.5882      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5600    0.4848       200\n",
      "           1     0.5811    0.6450    0.6114       200\n",
      "           2     0.5166    0.3900    0.4444       200\n",
      "           3     0.5508    0.5150    0.5323       200\n",
      "           4     0.8789    0.8350    0.8564       200\n",
      "           5     0.8278    0.8650    0.8460       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4474    0.4250    0.4359       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5889    0.5863    0.5849      1600\n",
      "weighted avg     0.5889    0.5863    0.5849      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5746    0.6550    0.6121       200\n",
      "           2     0.5102    0.3750    0.4323       200\n",
      "           3     0.5479    0.5150    0.5309       200\n",
      "           4     0.8718    0.8500    0.8608       200\n",
      "           5     0.8374    0.8500    0.8437       200\n",
      "           6     0.4894    0.4600    0.4742       200\n",
      "           7     0.4607    0.4400    0.4501       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5903    0.5881    0.5864      1600\n",
      "weighted avg     0.5903    0.5881    0.5864      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5099    0.3850    0.4387       200\n",
      "           3     0.5591    0.5200    0.5389       200\n",
      "           4     0.8737    0.8300    0.8513       200\n",
      "           5     0.8143    0.8550    0.8341       200\n",
      "           6     0.4764    0.4550    0.4655       200\n",
      "           7     0.4541    0.4200    0.4364       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5864    0.5844    0.5826      1600\n",
      "weighted avg     0.5864    0.5844    0.5826      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4192    0.5450    0.4739       200\n",
      "           1     0.5708    0.6450    0.6056       200\n",
      "           2     0.5033    0.3800    0.4330       200\n",
      "           3     0.5455    0.5100    0.5271       200\n",
      "           4     0.8723    0.8200    0.8454       200\n",
      "           5     0.8075    0.8600    0.8329       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4570    0.4250    0.4404       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5821    0.5800    0.5783      1600\n",
      "weighted avg     0.5821    0.5800    0.5783      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5746    0.6550    0.6121       200\n",
      "           2     0.5102    0.3750    0.4323       200\n",
      "           3     0.5479    0.5150    0.5309       200\n",
      "           4     0.8718    0.8500    0.8608       200\n",
      "           5     0.8374    0.8500    0.8437       200\n",
      "           6     0.4894    0.4600    0.4742       200\n",
      "           7     0.4607    0.4400    0.4501       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5903    0.5881    0.5864      1600\n",
      "weighted avg     0.5903    0.5881    0.5864      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5647    0.6550    0.6065       200\n",
      "           2     0.5319    0.3750    0.4399       200\n",
      "           3     0.5519    0.5050    0.5274       200\n",
      "           4     0.8743    0.8350    0.8542       200\n",
      "           5     0.8182    0.8550    0.8362       200\n",
      "           6     0.4789    0.4550    0.4667       200\n",
      "           7     0.4456    0.4300    0.4377       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5864    0.5831    0.5813      1600\n",
      "weighted avg     0.5864    0.5831    0.5813      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5739    0.6600    0.6140       200\n",
      "           2     0.5139    0.3700    0.4302       200\n",
      "           3     0.5489    0.5050    0.5260       200\n",
      "           4     0.8796    0.8400    0.8593       200\n",
      "           5     0.8269    0.8600    0.8431       200\n",
      "           6     0.4817    0.4600    0.4706       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5876    0.5850    0.5831      1600\n",
      "weighted avg     0.5876    0.5850    0.5831      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4346    0.5650    0.4913       200\n",
      "           1     0.5696    0.6550    0.6093       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5430    0.5050    0.5233       200\n",
      "           4     0.8763    0.8500    0.8629       200\n",
      "           5     0.8382    0.8550    0.8465       200\n",
      "           6     0.4866    0.4550    0.4703       200\n",
      "           7     0.4560    0.4400    0.4478       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5898    0.5875    0.5856      1600\n",
      "weighted avg     0.5898    0.5875    0.5856      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5696    0.6550    0.6093       200\n",
      "           2     0.5135    0.3800    0.4368       200\n",
      "           3     0.5430    0.5050    0.5233       200\n",
      "           4     0.8936    0.8400    0.8660       200\n",
      "           5     0.8333    0.8750    0.8537       200\n",
      "           6     0.4839    0.4500    0.4663       200\n",
      "           7     0.4450    0.4250    0.4348       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5884    0.5856    0.5840      1600\n",
      "weighted avg     0.5884    0.5856    0.5840      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4269    0.5550    0.4826       200\n",
      "           1     0.5733    0.6450    0.6071       200\n",
      "           2     0.5168    0.3850    0.4413       200\n",
      "           3     0.5622    0.5200    0.5403       200\n",
      "           4     0.8756    0.8450    0.8601       200\n",
      "           5     0.8301    0.8550    0.8424       200\n",
      "           6     0.4840    0.4550    0.4691       200\n",
      "           7     0.4381    0.4250    0.4315       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5884    0.5856    0.5843      1600\n",
      "weighted avg     0.5884    0.5856    0.5843      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5550    0.4847       200\n",
      "           1     0.5822    0.6550    0.6165       200\n",
      "           2     0.5101    0.3800    0.4355       200\n",
      "           3     0.5521    0.5300    0.5408       200\n",
      "           4     0.8718    0.8500    0.8608       200\n",
      "           5     0.8382    0.8550    0.8465       200\n",
      "           6     0.4813    0.4500    0.4651       200\n",
      "           7     0.4526    0.4300    0.4410       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5898    0.5881    0.5864      1600\n",
      "weighted avg     0.5898    0.5881    0.5864      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4286    0.5550    0.4837       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5270    0.3900    0.4483       200\n",
      "           3     0.5430    0.5050    0.5233       200\n",
      "           4     0.8743    0.8350    0.8542       200\n",
      "           5     0.8230    0.8600    0.8411       200\n",
      "           6     0.4813    0.4500    0.4651       200\n",
      "           7     0.4427    0.4250    0.4337       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5874    0.5850    0.5833      1600\n",
      "weighted avg     0.5874    0.5850    0.5833      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4269    0.5550    0.4826       200\n",
      "           1     0.5848    0.6550    0.6179       200\n",
      "           2     0.5203    0.3850    0.4425       200\n",
      "           3     0.5445    0.5200    0.5320       200\n",
      "           4     0.8615    0.8400    0.8506       200\n",
      "           5     0.8284    0.8450    0.8366       200\n",
      "           6     0.4813    0.4500    0.4651       200\n",
      "           7     0.4450    0.4250    0.4348       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5866    0.5844    0.5828      1600\n",
      "weighted avg     0.5866    0.5844    0.5828      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5550    0.4847       200\n",
      "           1     0.5822    0.6550    0.6165       200\n",
      "           2     0.5101    0.3800    0.4355       200\n",
      "           3     0.5521    0.5300    0.5408       200\n",
      "           4     0.8718    0.8500    0.8608       200\n",
      "           5     0.8382    0.8550    0.8465       200\n",
      "           6     0.4813    0.4500    0.4651       200\n",
      "           7     0.4526    0.4300    0.4410       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5898    0.5881    0.5864      1600\n",
      "weighted avg     0.5898    0.5881    0.5864      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4242    0.5600    0.4828       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5068    0.3750    0.4310       200\n",
      "           3     0.5635    0.5100    0.5354       200\n",
      "           4     0.8693    0.8650    0.8672       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4421    0.4200    0.4308       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5900    0.5875    0.5857      1600\n",
      "weighted avg     0.5900    0.5875    0.5857      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5815    0.6600    0.6183       200\n",
      "           2     0.5067    0.3800    0.4343       200\n",
      "           3     0.5531    0.4950    0.5224       200\n",
      "           4     0.8713    0.8800    0.8756       200\n",
      "           5     0.8629    0.8500    0.8564       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4359    0.4250    0.4304       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5898    0.5875    0.5859      1600\n",
      "weighted avg     0.5898    0.5875    0.5859      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5848    0.6550    0.6179       200\n",
      "           2     0.5033    0.3800    0.4330       200\n",
      "           3     0.5622    0.5200    0.5403       200\n",
      "           4     0.8724    0.8550    0.8636       200\n",
      "           5     0.8424    0.8550    0.8486       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4555    0.4350    0.4450       200\n",
      "\n",
      "    accuracy                         0.5887      1600\n",
      "   macro avg     0.5909    0.5887    0.5872      1600\n",
      "weighted avg     0.5909    0.5887    0.5872      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4239    0.5850    0.4916       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5312    0.3400    0.4146       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4667    0.4550    0.4608       200\n",
      "           7     0.4574    0.4300    0.4433       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5900    0.5856    0.5829      1600\n",
      "weighted avg     0.5900    0.5856    0.5829      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4244    0.5750    0.4883       200\n",
      "           1     0.5801    0.6700    0.6218       200\n",
      "           2     0.5397    0.3400    0.4172       200\n",
      "           3     0.5436    0.5300    0.5367       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4635    0.4450    0.4541       200\n",
      "           7     0.4570    0.4250    0.4404       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5902    0.5863    0.5832      1600\n",
      "weighted avg     0.5902    0.5863    0.5832      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4280    0.5800    0.4926       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5433    0.3450    0.4220       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4635    0.4450    0.4541       200\n",
      "           7     0.4521    0.4250    0.4381       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5903    0.5863    0.5834      1600\n",
      "weighted avg     0.5903    0.5863    0.5834      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4255    0.5850    0.4926       200\n",
      "           1     0.5808    0.6650    0.6200       200\n",
      "           2     0.5440    0.3400    0.4185       200\n",
      "           3     0.5417    0.5200    0.5306       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4663    0.4500    0.4580       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5907    0.5863    0.5833      1600\n",
      "weighted avg     0.5907    0.5863    0.5833      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4249    0.5800    0.4905       200\n",
      "           1     0.5826    0.6700    0.6233       200\n",
      "           2     0.5397    0.3400    0.4172       200\n",
      "           3     0.5412    0.5250    0.5330       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4635    0.4450    0.4541       200\n",
      "           7     0.4570    0.4250    0.4404       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5903    0.5863    0.5832      1600\n",
      "weighted avg     0.5903    0.5863    0.5832      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4280    0.5800    0.4926       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5433    0.3450    0.4220       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4635    0.4450    0.4541       200\n",
      "           7     0.4521    0.4250    0.4381       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5903    0.5863    0.5834      1600\n",
      "weighted avg     0.5903    0.5863    0.5834      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4255    0.5850    0.4926       200\n",
      "           1     0.5808    0.6650    0.6200       200\n",
      "           2     0.5397    0.3400    0.4172       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4663    0.4500    0.4580       200\n",
      "           7     0.4595    0.4250    0.4416       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5911    0.5869    0.5839      1600\n",
      "weighted avg     0.5911    0.5869    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4265    0.5800    0.4915       200\n",
      "           1     0.5808    0.6650    0.6200       200\n",
      "           2     0.5440    0.3400    0.4185       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4663    0.4500    0.4580       200\n",
      "           7     0.4497    0.4250    0.4370       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5906    0.5863    0.5834      1600\n",
      "weighted avg     0.5906    0.5863    0.5834      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4280    0.5800    0.4926       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5433    0.3450    0.4220       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4635    0.4450    0.4541       200\n",
      "           7     0.4521    0.4250    0.4381       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5903    0.5863    0.5834      1600\n",
      "weighted avg     0.5903    0.5863    0.5834      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4324    0.5600    0.4880       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5306    0.3900    0.4496       200\n",
      "           3     0.5625    0.5400    0.5510       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4866    0.4550    0.4703       200\n",
      "           7     0.4628    0.4350    0.4485       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.5959    0.5931    0.5914      1600\n",
      "weighted avg     0.5959    0.5931    0.5914      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5600    0.4848       200\n",
      "           1     0.5785    0.6450    0.6099       200\n",
      "           2     0.5166    0.3900    0.4444       200\n",
      "           3     0.5508    0.5150    0.5323       200\n",
      "           4     0.8836    0.8350    0.8586       200\n",
      "           5     0.8278    0.8650    0.8460       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4474    0.4250    0.4359       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5892    0.5863    0.5850      1600\n",
      "weighted avg     0.5892    0.5863    0.5850      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5746    0.6550    0.6121       200\n",
      "           2     0.5034    0.3750    0.4298       200\n",
      "           3     0.5514    0.5100    0.5299       200\n",
      "           4     0.8718    0.8500    0.8608       200\n",
      "           5     0.8374    0.8500    0.8437       200\n",
      "           6     0.4894    0.4600    0.4742       200\n",
      "           7     0.4583    0.4400    0.4490       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5896    0.5875    0.5858      1600\n",
      "weighted avg     0.5896    0.5875    0.5858      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5310    0.3850    0.4464       200\n",
      "           3     0.5508    0.5150    0.5323       200\n",
      "           4     0.8737    0.8300    0.8513       200\n",
      "           5     0.8143    0.8550    0.8341       200\n",
      "           6     0.4712    0.4500    0.4604       200\n",
      "           7     0.4468    0.4200    0.4330       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5858    0.5831    0.5813      1600\n",
      "weighted avg     0.5858    0.5831    0.5813      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4192    0.5450    0.4739       200\n",
      "           1     0.5759    0.6450    0.6085       200\n",
      "           2     0.5099    0.3850    0.4387       200\n",
      "           3     0.5479    0.5150    0.5309       200\n",
      "           4     0.8723    0.8200    0.8454       200\n",
      "           5     0.8075    0.8600    0.8329       200\n",
      "           6     0.4789    0.4550    0.4667       200\n",
      "           7     0.4570    0.4250    0.4404       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5836    0.5813    0.5797      1600\n",
      "weighted avg     0.5836    0.5813    0.5797      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5746    0.6550    0.6121       200\n",
      "           2     0.5034    0.3750    0.4298       200\n",
      "           3     0.5514    0.5100    0.5299       200\n",
      "           4     0.8718    0.8500    0.8608       200\n",
      "           5     0.8374    0.8500    0.8437       200\n",
      "           6     0.4894    0.4600    0.4742       200\n",
      "           7     0.4583    0.4400    0.4490       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5896    0.5875    0.5858      1600\n",
      "weighted avg     0.5896    0.5875    0.5858      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5647    0.6550    0.6065       200\n",
      "           2     0.5352    0.3800    0.4444       200\n",
      "           3     0.5543    0.5100    0.5312       200\n",
      "           4     0.8743    0.8350    0.8542       200\n",
      "           5     0.8182    0.8550    0.8362       200\n",
      "           6     0.4789    0.4550    0.4667       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5877    0.5844    0.5826      1600\n",
      "weighted avg     0.5877    0.5844    0.5826      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5739    0.6600    0.6140       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5495    0.5000    0.5236       200\n",
      "           4     0.8796    0.8400    0.8593       200\n",
      "           5     0.8269    0.8600    0.8431       200\n",
      "           6     0.4817    0.4600    0.4706       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5876    0.5850    0.5832      1600\n",
      "weighted avg     0.5876    0.5850    0.5832      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=sqrt, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5550    0.4847       200\n",
      "           1     0.5696    0.6550    0.6093       200\n",
      "           2     0.5103    0.3700    0.4290       200\n",
      "           3     0.5430    0.5050    0.5233       200\n",
      "           4     0.8763    0.8500    0.8629       200\n",
      "           5     0.8341    0.8550    0.8444       200\n",
      "           6     0.4866    0.4550    0.4703       200\n",
      "           7     0.4513    0.4400    0.4456       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5877    0.5856    0.5837      1600\n",
      "weighted avg     0.5877    0.5856    0.5837      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4324    0.5600    0.4880       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5306    0.3900    0.4496       200\n",
      "           3     0.5625    0.5400    0.5510       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4866    0.4550    0.4703       200\n",
      "           7     0.4628    0.4350    0.4485       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.5959    0.5931    0.5914      1600\n",
      "weighted avg     0.5959    0.5931    0.5914      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5600    0.4848       200\n",
      "           1     0.5785    0.6450    0.6099       200\n",
      "           2     0.5166    0.3900    0.4444       200\n",
      "           3     0.5508    0.5150    0.5323       200\n",
      "           4     0.8836    0.8350    0.8586       200\n",
      "           5     0.8278    0.8650    0.8460       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4474    0.4250    0.4359       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5892    0.5863    0.5850      1600\n",
      "weighted avg     0.5892    0.5863    0.5850      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5746    0.6550    0.6121       200\n",
      "           2     0.5034    0.3750    0.4298       200\n",
      "           3     0.5514    0.5100    0.5299       200\n",
      "           4     0.8718    0.8500    0.8608       200\n",
      "           5     0.8374    0.8500    0.8437       200\n",
      "           6     0.4894    0.4600    0.4742       200\n",
      "           7     0.4583    0.4400    0.4490       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5896    0.5875    0.5858      1600\n",
      "weighted avg     0.5896    0.5875    0.5858      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5310    0.3850    0.4464       200\n",
      "           3     0.5508    0.5150    0.5323       200\n",
      "           4     0.8737    0.8300    0.8513       200\n",
      "           5     0.8143    0.8550    0.8341       200\n",
      "           6     0.4712    0.4500    0.4604       200\n",
      "           7     0.4468    0.4200    0.4330       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5858    0.5831    0.5813      1600\n",
      "weighted avg     0.5858    0.5831    0.5813      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4192    0.5450    0.4739       200\n",
      "           1     0.5759    0.6450    0.6085       200\n",
      "           2     0.5099    0.3850    0.4387       200\n",
      "           3     0.5479    0.5150    0.5309       200\n",
      "           4     0.8723    0.8200    0.8454       200\n",
      "           5     0.8075    0.8600    0.8329       200\n",
      "           6     0.4789    0.4550    0.4667       200\n",
      "           7     0.4570    0.4250    0.4404       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5836    0.5813    0.5797      1600\n",
      "weighted avg     0.5836    0.5813    0.5797      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5746    0.6550    0.6121       200\n",
      "           2     0.5034    0.3750    0.4298       200\n",
      "           3     0.5514    0.5100    0.5299       200\n",
      "           4     0.8718    0.8500    0.8608       200\n",
      "           5     0.8374    0.8500    0.8437       200\n",
      "           6     0.4894    0.4600    0.4742       200\n",
      "           7     0.4583    0.4400    0.4490       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5896    0.5875    0.5858      1600\n",
      "weighted avg     0.5896    0.5875    0.5858      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5647    0.6550    0.6065       200\n",
      "           2     0.5352    0.3800    0.4444       200\n",
      "           3     0.5543    0.5100    0.5312       200\n",
      "           4     0.8743    0.8350    0.8542       200\n",
      "           5     0.8182    0.8550    0.8362       200\n",
      "           6     0.4789    0.4550    0.4667       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5877    0.5844    0.5826      1600\n",
      "weighted avg     0.5877    0.5844    0.5826      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5739    0.6600    0.6140       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5495    0.5000    0.5236       200\n",
      "           4     0.8796    0.8400    0.8593       200\n",
      "           5     0.8269    0.8600    0.8431       200\n",
      "           6     0.4817    0.4600    0.4706       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5876    0.5850    0.5832      1600\n",
      "weighted avg     0.5876    0.5850    0.5832      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5550    0.4847       200\n",
      "           1     0.5696    0.6550    0.6093       200\n",
      "           2     0.5103    0.3700    0.4290       200\n",
      "           3     0.5430    0.5050    0.5233       200\n",
      "           4     0.8763    0.8500    0.8629       200\n",
      "           5     0.8341    0.8550    0.8444       200\n",
      "           6     0.4866    0.4550    0.4703       200\n",
      "           7     0.4513    0.4400    0.4456       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5877    0.5856    0.5837      1600\n",
      "weighted avg     0.5877    0.5856    0.5837      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4324    0.5600    0.4880       200\n",
      "           1     0.5739    0.6600    0.6140       200\n",
      "           2     0.5274    0.3850    0.4451       200\n",
      "           3     0.5585    0.5250    0.5412       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4813    0.4500    0.4651       200\n",
      "           7     0.4555    0.4350    0.4450       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5928    0.5900    0.5882      1600\n",
      "weighted avg     0.5928    0.5900    0.5882      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5600    0.4848       200\n",
      "           1     0.5811    0.6450    0.6114       200\n",
      "           2     0.5166    0.3900    0.4444       200\n",
      "           3     0.5508    0.5150    0.5323       200\n",
      "           4     0.8789    0.8350    0.8564       200\n",
      "           5     0.8278    0.8650    0.8460       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4474    0.4250    0.4359       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5889    0.5863    0.5849      1600\n",
      "weighted avg     0.5889    0.5863    0.5849      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5746    0.6550    0.6121       200\n",
      "           2     0.5102    0.3750    0.4323       200\n",
      "           3     0.5479    0.5150    0.5309       200\n",
      "           4     0.8718    0.8500    0.8608       200\n",
      "           5     0.8374    0.8500    0.8437       200\n",
      "           6     0.4894    0.4600    0.4742       200\n",
      "           7     0.4607    0.4400    0.4501       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5903    0.5881    0.5864      1600\n",
      "weighted avg     0.5903    0.5881    0.5864      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5099    0.3850    0.4387       200\n",
      "           3     0.5591    0.5200    0.5389       200\n",
      "           4     0.8737    0.8300    0.8513       200\n",
      "           5     0.8143    0.8550    0.8341       200\n",
      "           6     0.4764    0.4550    0.4655       200\n",
      "           7     0.4541    0.4200    0.4364       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5864    0.5844    0.5826      1600\n",
      "weighted avg     0.5864    0.5844    0.5826      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4192    0.5450    0.4739       200\n",
      "           1     0.5708    0.6450    0.6056       200\n",
      "           2     0.5033    0.3800    0.4330       200\n",
      "           3     0.5455    0.5100    0.5271       200\n",
      "           4     0.8723    0.8200    0.8454       200\n",
      "           5     0.8075    0.8600    0.8329       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4570    0.4250    0.4404       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5821    0.5800    0.5783      1600\n",
      "weighted avg     0.5821    0.5800    0.5783      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5746    0.6550    0.6121       200\n",
      "           2     0.5102    0.3750    0.4323       200\n",
      "           3     0.5479    0.5150    0.5309       200\n",
      "           4     0.8718    0.8500    0.8608       200\n",
      "           5     0.8374    0.8500    0.8437       200\n",
      "           6     0.4894    0.4600    0.4742       200\n",
      "           7     0.4607    0.4400    0.4501       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5903    0.5881    0.5864      1600\n",
      "weighted avg     0.5903    0.5881    0.5864      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5647    0.6550    0.6065       200\n",
      "           2     0.5319    0.3750    0.4399       200\n",
      "           3     0.5519    0.5050    0.5274       200\n",
      "           4     0.8743    0.8350    0.8542       200\n",
      "           5     0.8182    0.8550    0.8362       200\n",
      "           6     0.4789    0.4550    0.4667       200\n",
      "           7     0.4456    0.4300    0.4377       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5864    0.5831    0.5813      1600\n",
      "weighted avg     0.5864    0.5831    0.5813      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5739    0.6600    0.6140       200\n",
      "           2     0.5139    0.3700    0.4302       200\n",
      "           3     0.5489    0.5050    0.5260       200\n",
      "           4     0.8796    0.8400    0.8593       200\n",
      "           5     0.8269    0.8600    0.8431       200\n",
      "           6     0.4817    0.4600    0.4706       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5876    0.5850    0.5831      1600\n",
      "weighted avg     0.5876    0.5850    0.5831      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4346    0.5650    0.4913       200\n",
      "           1     0.5696    0.6550    0.6093       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5430    0.5050    0.5233       200\n",
      "           4     0.8763    0.8500    0.8629       200\n",
      "           5     0.8382    0.8550    0.8465       200\n",
      "           6     0.4866    0.4550    0.4703       200\n",
      "           7     0.4560    0.4400    0.4478       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5898    0.5875    0.5856      1600\n",
      "weighted avg     0.5898    0.5875    0.5856      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5696    0.6550    0.6093       200\n",
      "           2     0.5135    0.3800    0.4368       200\n",
      "           3     0.5430    0.5050    0.5233       200\n",
      "           4     0.8936    0.8400    0.8660       200\n",
      "           5     0.8333    0.8750    0.8537       200\n",
      "           6     0.4839    0.4500    0.4663       200\n",
      "           7     0.4450    0.4250    0.4348       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5884    0.5856    0.5840      1600\n",
      "weighted avg     0.5884    0.5856    0.5840      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4269    0.5550    0.4826       200\n",
      "           1     0.5733    0.6450    0.6071       200\n",
      "           2     0.5168    0.3850    0.4413       200\n",
      "           3     0.5622    0.5200    0.5403       200\n",
      "           4     0.8756    0.8450    0.8601       200\n",
      "           5     0.8301    0.8550    0.8424       200\n",
      "           6     0.4840    0.4550    0.4691       200\n",
      "           7     0.4381    0.4250    0.4315       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5884    0.5856    0.5843      1600\n",
      "weighted avg     0.5884    0.5856    0.5843      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5550    0.4847       200\n",
      "           1     0.5822    0.6550    0.6165       200\n",
      "           2     0.5101    0.3800    0.4355       200\n",
      "           3     0.5521    0.5300    0.5408       200\n",
      "           4     0.8718    0.8500    0.8608       200\n",
      "           5     0.8382    0.8550    0.8465       200\n",
      "           6     0.4813    0.4500    0.4651       200\n",
      "           7     0.4526    0.4300    0.4410       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5898    0.5881    0.5864      1600\n",
      "weighted avg     0.5898    0.5881    0.5864      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4286    0.5550    0.4837       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5270    0.3900    0.4483       200\n",
      "           3     0.5430    0.5050    0.5233       200\n",
      "           4     0.8743    0.8350    0.8542       200\n",
      "           5     0.8230    0.8600    0.8411       200\n",
      "           6     0.4813    0.4500    0.4651       200\n",
      "           7     0.4427    0.4250    0.4337       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5874    0.5850    0.5833      1600\n",
      "weighted avg     0.5874    0.5850    0.5833      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4269    0.5550    0.4826       200\n",
      "           1     0.5848    0.6550    0.6179       200\n",
      "           2     0.5203    0.3850    0.4425       200\n",
      "           3     0.5445    0.5200    0.5320       200\n",
      "           4     0.8615    0.8400    0.8506       200\n",
      "           5     0.8284    0.8450    0.8366       200\n",
      "           6     0.4813    0.4500    0.4651       200\n",
      "           7     0.4450    0.4250    0.4348       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5866    0.5844    0.5828      1600\n",
      "weighted avg     0.5866    0.5844    0.5828      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5550    0.4847       200\n",
      "           1     0.5822    0.6550    0.6165       200\n",
      "           2     0.5101    0.3800    0.4355       200\n",
      "           3     0.5521    0.5300    0.5408       200\n",
      "           4     0.8718    0.8500    0.8608       200\n",
      "           5     0.8382    0.8550    0.8465       200\n",
      "           6     0.4813    0.4500    0.4651       200\n",
      "           7     0.4526    0.4300    0.4410       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5898    0.5881    0.5864      1600\n",
      "weighted avg     0.5898    0.5881    0.5864      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4242    0.5600    0.4828       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5068    0.3750    0.4310       200\n",
      "           3     0.5635    0.5100    0.5354       200\n",
      "           4     0.8693    0.8650    0.8672       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4421    0.4200    0.4308       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5900    0.5875    0.5857      1600\n",
      "weighted avg     0.5900    0.5875    0.5857      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5815    0.6600    0.6183       200\n",
      "           2     0.5067    0.3800    0.4343       200\n",
      "           3     0.5531    0.4950    0.5224       200\n",
      "           4     0.8713    0.8800    0.8756       200\n",
      "           5     0.8629    0.8500    0.8564       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4359    0.4250    0.4304       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5898    0.5875    0.5859      1600\n",
      "weighted avg     0.5898    0.5875    0.5859      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5848    0.6550    0.6179       200\n",
      "           2     0.5033    0.3800    0.4330       200\n",
      "           3     0.5622    0.5200    0.5403       200\n",
      "           4     0.8724    0.8550    0.8636       200\n",
      "           5     0.8424    0.8550    0.8486       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4555    0.4350    0.4450       200\n",
      "\n",
      "    accuracy                         0.5887      1600\n",
      "   macro avg     0.5909    0.5887    0.5872      1600\n",
      "weighted avg     0.5909    0.5887    0.5872      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4239    0.5850    0.4916       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5312    0.3400    0.4146       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4667    0.4550    0.4608       200\n",
      "           7     0.4574    0.4300    0.4433       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5900    0.5856    0.5829      1600\n",
      "weighted avg     0.5900    0.5856    0.5829      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4244    0.5750    0.4883       200\n",
      "           1     0.5801    0.6700    0.6218       200\n",
      "           2     0.5397    0.3400    0.4172       200\n",
      "           3     0.5436    0.5300    0.5367       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4635    0.4450    0.4541       200\n",
      "           7     0.4570    0.4250    0.4404       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5902    0.5863    0.5832      1600\n",
      "weighted avg     0.5902    0.5863    0.5832      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4280    0.5800    0.4926       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5433    0.3450    0.4220       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4635    0.4450    0.4541       200\n",
      "           7     0.4521    0.4250    0.4381       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5903    0.5863    0.5834      1600\n",
      "weighted avg     0.5903    0.5863    0.5834      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4255    0.5850    0.4926       200\n",
      "           1     0.5808    0.6650    0.6200       200\n",
      "           2     0.5440    0.3400    0.4185       200\n",
      "           3     0.5417    0.5200    0.5306       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4663    0.4500    0.4580       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5907    0.5863    0.5833      1600\n",
      "weighted avg     0.5907    0.5863    0.5833      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4249    0.5800    0.4905       200\n",
      "           1     0.5826    0.6700    0.6233       200\n",
      "           2     0.5397    0.3400    0.4172       200\n",
      "           3     0.5412    0.5250    0.5330       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4635    0.4450    0.4541       200\n",
      "           7     0.4570    0.4250    0.4404       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5903    0.5863    0.5832      1600\n",
      "weighted avg     0.5903    0.5863    0.5832      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4280    0.5800    0.4926       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5433    0.3450    0.4220       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4635    0.4450    0.4541       200\n",
      "           7     0.4521    0.4250    0.4381       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5903    0.5863    0.5834      1600\n",
      "weighted avg     0.5903    0.5863    0.5834      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4255    0.5850    0.4926       200\n",
      "           1     0.5808    0.6650    0.6200       200\n",
      "           2     0.5397    0.3400    0.4172       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4663    0.4500    0.4580       200\n",
      "           7     0.4595    0.4250    0.4416       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5911    0.5869    0.5839      1600\n",
      "weighted avg     0.5911    0.5869    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4265    0.5800    0.4915       200\n",
      "           1     0.5808    0.6650    0.6200       200\n",
      "           2     0.5440    0.3400    0.4185       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4663    0.4500    0.4580       200\n",
      "           7     0.4497    0.4250    0.4370       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5906    0.5863    0.5834      1600\n",
      "weighted avg     0.5906    0.5863    0.5834      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4280    0.5800    0.4926       200\n",
      "           1     0.5783    0.6650    0.6186       200\n",
      "           2     0.5433    0.3450    0.4220       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4635    0.4450    0.4541       200\n",
      "           7     0.4521    0.4250    0.4381       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5903    0.5863    0.5834      1600\n",
      "weighted avg     0.5903    0.5863    0.5834      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4324    0.5600    0.4880       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5306    0.3900    0.4496       200\n",
      "           3     0.5625    0.5400    0.5510       200\n",
      "           4     0.8877    0.8300    0.8579       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4866    0.4550    0.4703       200\n",
      "           7     0.4628    0.4350    0.4485       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.5959    0.5931    0.5914      1600\n",
      "weighted avg     0.5959    0.5931    0.5914      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5600    0.4848       200\n",
      "           1     0.5785    0.6450    0.6099       200\n",
      "           2     0.5166    0.3900    0.4444       200\n",
      "           3     0.5508    0.5150    0.5323       200\n",
      "           4     0.8836    0.8350    0.8586       200\n",
      "           5     0.8278    0.8650    0.8460       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4474    0.4250    0.4359       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5892    0.5863    0.5850      1600\n",
      "weighted avg     0.5892    0.5863    0.5850      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5746    0.6550    0.6121       200\n",
      "           2     0.5034    0.3750    0.4298       200\n",
      "           3     0.5514    0.5100    0.5299       200\n",
      "           4     0.8718    0.8500    0.8608       200\n",
      "           5     0.8374    0.8500    0.8437       200\n",
      "           6     0.4894    0.4600    0.4742       200\n",
      "           7     0.4583    0.4400    0.4490       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5896    0.5875    0.5858      1600\n",
      "weighted avg     0.5896    0.5875    0.5858      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5310    0.3850    0.4464       200\n",
      "           3     0.5508    0.5150    0.5323       200\n",
      "           4     0.8737    0.8300    0.8513       200\n",
      "           5     0.8143    0.8550    0.8341       200\n",
      "           6     0.4712    0.4500    0.4604       200\n",
      "           7     0.4468    0.4200    0.4330       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5858    0.5831    0.5813      1600\n",
      "weighted avg     0.5858    0.5831    0.5813      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4192    0.5450    0.4739       200\n",
      "           1     0.5759    0.6450    0.6085       200\n",
      "           2     0.5099    0.3850    0.4387       200\n",
      "           3     0.5479    0.5150    0.5309       200\n",
      "           4     0.8723    0.8200    0.8454       200\n",
      "           5     0.8075    0.8600    0.8329       200\n",
      "           6     0.4789    0.4550    0.4667       200\n",
      "           7     0.4570    0.4250    0.4404       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5836    0.5813    0.5797      1600\n",
      "weighted avg     0.5836    0.5813    0.5797      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5746    0.6550    0.6121       200\n",
      "           2     0.5034    0.3750    0.4298       200\n",
      "           3     0.5514    0.5100    0.5299       200\n",
      "           4     0.8718    0.8500    0.8608       200\n",
      "           5     0.8374    0.8500    0.8437       200\n",
      "           6     0.4894    0.4600    0.4742       200\n",
      "           7     0.4583    0.4400    0.4490       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5896    0.5875    0.5858      1600\n",
      "weighted avg     0.5896    0.5875    0.5858      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5647    0.6550    0.6065       200\n",
      "           2     0.5352    0.3800    0.4444       200\n",
      "           3     0.5543    0.5100    0.5312       200\n",
      "           4     0.8743    0.8350    0.8542       200\n",
      "           5     0.8182    0.8550    0.8362       200\n",
      "           6     0.4789    0.4550    0.4667       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5877    0.5844    0.5826      1600\n",
      "weighted avg     0.5877    0.5844    0.5826      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4253    0.5550    0.4816       200\n",
      "           1     0.5739    0.6600    0.6140       200\n",
      "           2     0.5137    0.3750    0.4335       200\n",
      "           3     0.5495    0.5000    0.5236       200\n",
      "           4     0.8796    0.8400    0.8593       200\n",
      "           5     0.8269    0.8600    0.8431       200\n",
      "           6     0.4817    0.4600    0.4706       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5876    0.5850    0.5832      1600\n",
      "weighted avg     0.5876    0.5850    0.5832      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=log2, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5550    0.4847       200\n",
      "           1     0.5696    0.6550    0.6093       200\n",
      "           2     0.5103    0.3700    0.4290       200\n",
      "           3     0.5430    0.5050    0.5233       200\n",
      "           4     0.8763    0.8500    0.8629       200\n",
      "           5     0.8341    0.8550    0.8444       200\n",
      "           6     0.4866    0.4550    0.4703       200\n",
      "           7     0.4513    0.4400    0.4456       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5877    0.5856    0.5837      1600\n",
      "weighted avg     0.5877    0.5856    0.5837      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 57.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5550    0.4847       200\n",
      "           1     0.5600    0.6300    0.5929       200\n",
      "           2     0.5103    0.3700    0.4290       200\n",
      "           3     0.5543    0.5100    0.5312       200\n",
      "           4     0.8777    0.8250    0.8505       200\n",
      "           5     0.8278    0.8650    0.8460       200\n",
      "           6     0.4300    0.4450    0.4373       200\n",
      "           7     0.4511    0.4150    0.4323       200\n",
      "\n",
      "    accuracy                         0.5769      1600\n",
      "   macro avg     0.5802    0.5769    0.5755      1600\n",
      "weighted avg     0.5802    0.5769    0.5755      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4346    0.5650    0.4913       200\n",
      "           1     0.5676    0.6300    0.5972       200\n",
      "           2     0.5324    0.3700    0.4366       200\n",
      "           3     0.5426    0.5100    0.5258       200\n",
      "           4     0.8919    0.8250    0.8571       200\n",
      "           5     0.8302    0.8800    0.8544       200\n",
      "           6     0.4238    0.4450    0.4341       200\n",
      "           7     0.4565    0.4200    0.4375       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5849    0.5806    0.5792      1600\n",
      "weighted avg     0.5849    0.5806    0.5792      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5676    0.6300    0.5972       200\n",
      "           2     0.5252    0.3650    0.4307       200\n",
      "           3     0.5361    0.5200    0.5279       200\n",
      "           4     0.8824    0.8250    0.8527       200\n",
      "           5     0.8208    0.8700    0.8447       200\n",
      "           6     0.4335    0.4400    0.4367       200\n",
      "           7     0.4641    0.4200    0.4409       200\n",
      "\n",
      "    accuracy                         0.5794      1600\n",
      "   macro avg     0.5826    0.5794    0.5775      1600\n",
      "weighted avg     0.5826    0.5794    0.5775      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4346    0.5650    0.4913       200\n",
      "           1     0.5614    0.6400    0.5981       200\n",
      "           2     0.5368    0.3650    0.4345       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8871    0.8250    0.8549       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4231    0.4400    0.4314       200\n",
      "           7     0.4595    0.4250    0.4416       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5850    0.5806    0.5790      1600\n",
      "weighted avg     0.5850    0.5806    0.5790      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5650    0.6300    0.5957       200\n",
      "           2     0.5255    0.3600    0.4273       200\n",
      "           3     0.5393    0.5150    0.5269       200\n",
      "           4     0.8865    0.8200    0.8519       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4258    0.4450    0.4352       200\n",
      "           7     0.4615    0.4200    0.4398       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5830    0.5787    0.5772      1600\n",
      "weighted avg     0.5830    0.5787    0.5772      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5676    0.6300    0.5972       200\n",
      "           2     0.5252    0.3650    0.4307       200\n",
      "           3     0.5361    0.5200    0.5279       200\n",
      "           4     0.8824    0.8250    0.8527       200\n",
      "           5     0.8208    0.8700    0.8447       200\n",
      "           6     0.4335    0.4400    0.4367       200\n",
      "           7     0.4641    0.4200    0.4409       200\n",
      "\n",
      "    accuracy                         0.5794      1600\n",
      "   macro avg     0.5826    0.5794    0.5775      1600\n",
      "weighted avg     0.5826    0.5794    0.5775      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5600    0.4859       200\n",
      "           1     0.5747    0.6350    0.6033       200\n",
      "           2     0.5357    0.3750    0.4412       200\n",
      "           3     0.5538    0.5150    0.5337       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8286    0.8700    0.8488       200\n",
      "           6     0.4231    0.4400    0.4314       200\n",
      "           7     0.4521    0.4250    0.4381       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5848    0.5800    0.5790      1600\n",
      "weighted avg     0.5848    0.5800    0.5790      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4297    0.5650    0.4881       200\n",
      "           1     0.5676    0.6300    0.5972       200\n",
      "           2     0.5252    0.3650    0.4307       200\n",
      "           3     0.5450    0.5150    0.5296       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8286    0.8700    0.8488       200\n",
      "           6     0.4183    0.4350    0.4265       200\n",
      "           7     0.4590    0.4200    0.4386       200\n",
      "\n",
      "    accuracy                         0.5775      1600\n",
      "   macro avg     0.5819    0.5775    0.5761      1600\n",
      "weighted avg     0.5819    0.5775    0.5761      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5714    0.6400    0.6038       200\n",
      "           2     0.5286    0.3700    0.4353       200\n",
      "           3     0.5464    0.5300    0.5381       200\n",
      "           4     0.8865    0.8200    0.8519       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4265    0.4350    0.4307       200\n",
      "           7     0.4667    0.4200    0.4421       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5858    0.5819    0.5803      1600\n",
      "weighted avg     0.5858    0.5819    0.5803      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 57.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5550    0.4847       200\n",
      "           1     0.5600    0.6300    0.5929       200\n",
      "           2     0.5103    0.3700    0.4290       200\n",
      "           3     0.5543    0.5100    0.5312       200\n",
      "           4     0.8777    0.8250    0.8505       200\n",
      "           5     0.8278    0.8650    0.8460       200\n",
      "           6     0.4300    0.4450    0.4373       200\n",
      "           7     0.4511    0.4150    0.4323       200\n",
      "\n",
      "    accuracy                         0.5769      1600\n",
      "   macro avg     0.5802    0.5769    0.5755      1600\n",
      "weighted avg     0.5802    0.5769    0.5755      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4346    0.5650    0.4913       200\n",
      "           1     0.5676    0.6300    0.5972       200\n",
      "           2     0.5324    0.3700    0.4366       200\n",
      "           3     0.5426    0.5100    0.5258       200\n",
      "           4     0.8919    0.8250    0.8571       200\n",
      "           5     0.8302    0.8800    0.8544       200\n",
      "           6     0.4238    0.4450    0.4341       200\n",
      "           7     0.4565    0.4200    0.4375       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5849    0.5806    0.5792      1600\n",
      "weighted avg     0.5849    0.5806    0.5792      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5676    0.6300    0.5972       200\n",
      "           2     0.5252    0.3650    0.4307       200\n",
      "           3     0.5361    0.5200    0.5279       200\n",
      "           4     0.8824    0.8250    0.8527       200\n",
      "           5     0.8208    0.8700    0.8447       200\n",
      "           6     0.4335    0.4400    0.4367       200\n",
      "           7     0.4641    0.4200    0.4409       200\n",
      "\n",
      "    accuracy                         0.5794      1600\n",
      "   macro avg     0.5826    0.5794    0.5775      1600\n",
      "weighted avg     0.5826    0.5794    0.5775      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4346    0.5650    0.4913       200\n",
      "           1     0.5614    0.6400    0.5981       200\n",
      "           2     0.5368    0.3650    0.4345       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8871    0.8250    0.8549       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4231    0.4400    0.4314       200\n",
      "           7     0.4595    0.4250    0.4416       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5850    0.5806    0.5790      1600\n",
      "weighted avg     0.5850    0.5806    0.5790      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5650    0.6300    0.5957       200\n",
      "           2     0.5255    0.3600    0.4273       200\n",
      "           3     0.5393    0.5150    0.5269       200\n",
      "           4     0.8865    0.8200    0.8519       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4258    0.4450    0.4352       200\n",
      "           7     0.4615    0.4200    0.4398       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5830    0.5787    0.5772      1600\n",
      "weighted avg     0.5830    0.5787    0.5772      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5676    0.6300    0.5972       200\n",
      "           2     0.5252    0.3650    0.4307       200\n",
      "           3     0.5361    0.5200    0.5279       200\n",
      "           4     0.8824    0.8250    0.8527       200\n",
      "           5     0.8208    0.8700    0.8447       200\n",
      "           6     0.4335    0.4400    0.4367       200\n",
      "           7     0.4641    0.4200    0.4409       200\n",
      "\n",
      "    accuracy                         0.5794      1600\n",
      "   macro avg     0.5826    0.5794    0.5775      1600\n",
      "weighted avg     0.5826    0.5794    0.5775      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5600    0.4859       200\n",
      "           1     0.5747    0.6350    0.6033       200\n",
      "           2     0.5357    0.3750    0.4412       200\n",
      "           3     0.5538    0.5150    0.5337       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8286    0.8700    0.8488       200\n",
      "           6     0.4231    0.4400    0.4314       200\n",
      "           7     0.4521    0.4250    0.4381       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5848    0.5800    0.5790      1600\n",
      "weighted avg     0.5848    0.5800    0.5790      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4297    0.5650    0.4881       200\n",
      "           1     0.5676    0.6300    0.5972       200\n",
      "           2     0.5252    0.3650    0.4307       200\n",
      "           3     0.5450    0.5150    0.5296       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8286    0.8700    0.8488       200\n",
      "           6     0.4183    0.4350    0.4265       200\n",
      "           7     0.4590    0.4200    0.4386       200\n",
      "\n",
      "    accuracy                         0.5775      1600\n",
      "   macro avg     0.5819    0.5775    0.5761      1600\n",
      "weighted avg     0.5819    0.5775    0.5761      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5714    0.6400    0.6038       200\n",
      "           2     0.5286    0.3700    0.4353       200\n",
      "           3     0.5464    0.5300    0.5381       200\n",
      "           4     0.8865    0.8200    0.8519       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4265    0.4350    0.4307       200\n",
      "           7     0.4667    0.4200    0.4421       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5858    0.5819    0.5803      1600\n",
      "weighted avg     0.5858    0.5819    0.5803      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4402    0.5700    0.4967       200\n",
      "           1     0.5619    0.6350    0.5962       200\n",
      "           2     0.5248    0.3700    0.4340       200\n",
      "           3     0.5596    0.5400    0.5496       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8286    0.8700    0.8488       200\n",
      "           6     0.4327    0.4500    0.4412       200\n",
      "           7     0.4633    0.4100    0.4350       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5866    0.5831    0.5814      1600\n",
      "weighted avg     0.5866    0.5831    0.5814      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4380    0.5650    0.4934       200\n",
      "           1     0.5644    0.6350    0.5976       200\n",
      "           2     0.5290    0.3650    0.4320       200\n",
      "           3     0.5503    0.5200    0.5347       200\n",
      "           4     0.8919    0.8250    0.8571       200\n",
      "           5     0.8302    0.8800    0.8544       200\n",
      "           6     0.4238    0.4450    0.4341       200\n",
      "           7     0.4590    0.4200    0.4386       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5858    0.5819    0.5803      1600\n",
      "weighted avg     0.5858    0.5819    0.5803      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4346    0.5650    0.4913       200\n",
      "           1     0.5753    0.6300    0.6014       200\n",
      "           2     0.5248    0.3700    0.4340       200\n",
      "           3     0.5354    0.5300    0.5327       200\n",
      "           4     0.9016    0.8250    0.8616       200\n",
      "           5     0.8241    0.8900    0.8558       200\n",
      "           6     0.4335    0.4400    0.4367       200\n",
      "           7     0.4611    0.4150    0.4368       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5863    0.5831    0.5813      1600\n",
      "weighted avg     0.5863    0.5831    0.5813      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5650    0.4860       200\n",
      "           1     0.5740    0.6400    0.6052       200\n",
      "           2     0.5328    0.3650    0.4332       200\n",
      "           3     0.5450    0.5150    0.5296       200\n",
      "           4     0.8865    0.8200    0.8519       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4258    0.4450    0.4352       200\n",
      "           7     0.4641    0.4200    0.4409       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5855    0.5806    0.5792      1600\n",
      "weighted avg     0.5855    0.5806    0.5792      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4297    0.5650    0.4881       200\n",
      "           1     0.5695    0.6350    0.6005       200\n",
      "           2     0.5252    0.3650    0.4307       200\n",
      "           3     0.5521    0.5300    0.5408       200\n",
      "           4     0.8865    0.8200    0.8519       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4258    0.4450    0.4352       200\n",
      "           7     0.4663    0.4150    0.4392       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5856    0.5812    0.5797      1600\n",
      "weighted avg     0.5856    0.5813    0.5797      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4346    0.5650    0.4913       200\n",
      "           1     0.5753    0.6300    0.6014       200\n",
      "           2     0.5248    0.3700    0.4340       200\n",
      "           3     0.5354    0.5300    0.5327       200\n",
      "           4     0.9016    0.8250    0.8616       200\n",
      "           5     0.8241    0.8900    0.8558       200\n",
      "           6     0.4335    0.4400    0.4367       200\n",
      "           7     0.4611    0.4150    0.4368       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5863    0.5831    0.5813      1600\n",
      "weighted avg     0.5863    0.5831    0.5813      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5845    0.6400    0.6110       200\n",
      "           2     0.5248    0.3700    0.4340       200\n",
      "           3     0.5464    0.5300    0.5381       200\n",
      "           4     0.8919    0.8250    0.8571       200\n",
      "           5     0.8302    0.8800    0.8544       200\n",
      "           6     0.4251    0.4400    0.4324       200\n",
      "           7     0.4611    0.4150    0.4368       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5869    0.5831    0.5816      1600\n",
      "weighted avg     0.5869    0.5831    0.5816      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5727    0.6300    0.6000       200\n",
      "           2     0.5211    0.3700    0.4327       200\n",
      "           3     0.5515    0.5350    0.5431       200\n",
      "           4     0.9011    0.8200    0.8586       200\n",
      "           5     0.8318    0.8900    0.8599       200\n",
      "           6     0.4251    0.4400    0.4324       200\n",
      "           7     0.4693    0.4200    0.4433       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5880    0.5837    0.5824      1600\n",
      "weighted avg     0.5880    0.5837    0.5824      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4297    0.5650    0.4881       200\n",
      "           1     0.5773    0.6350    0.6048       200\n",
      "           2     0.5286    0.3700    0.4353       200\n",
      "           3     0.5330    0.5250    0.5290       200\n",
      "           4     0.9011    0.8200    0.8586       200\n",
      "           5     0.8241    0.8900    0.8558       200\n",
      "           6     0.4314    0.4400    0.4356       200\n",
      "           7     0.4607    0.4100    0.4339       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5857    0.5819    0.5801      1600\n",
      "weighted avg     0.5857    0.5819    0.5801      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 57.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4345    0.5800    0.4968       200\n",
      "           1     0.5584    0.6450    0.5986       200\n",
      "           2     0.5391    0.3100    0.3937       200\n",
      "           3     0.5100    0.5100    0.5100       200\n",
      "           4     0.8824    0.8250    0.8527       200\n",
      "           5     0.8093    0.8700    0.8386       200\n",
      "           6     0.4271    0.4250    0.4261       200\n",
      "           7     0.4462    0.4150    0.4301       200\n",
      "\n",
      "    accuracy                         0.5725      1600\n",
      "   macro avg     0.5759    0.5725    0.5683      1600\n",
      "weighted avg     0.5759    0.5725    0.5683      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 57.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4345    0.5800    0.4968       200\n",
      "           1     0.5536    0.6450    0.5958       200\n",
      "           2     0.5596    0.3050    0.3948       200\n",
      "           3     0.5073    0.5200    0.5136       200\n",
      "           4     0.8824    0.8250    0.8527       200\n",
      "           5     0.8093    0.8700    0.8386       200\n",
      "           6     0.4293    0.4250    0.4271       200\n",
      "           7     0.4516    0.4200    0.4352       200\n",
      "\n",
      "    accuracy                         0.5737      1600\n",
      "   macro avg     0.5785    0.5737    0.5693      1600\n",
      "weighted avg     0.5785    0.5737    0.5693      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4307    0.5750    0.4925       200\n",
      "           1     0.5579    0.6500    0.6005       200\n",
      "           2     0.5660    0.3000    0.3922       200\n",
      "           3     0.5097    0.5250    0.5172       200\n",
      "           4     0.8824    0.8250    0.8527       200\n",
      "           5     0.8131    0.8700    0.8406       200\n",
      "           6     0.4343    0.4300    0.4322       200\n",
      "           7     0.4497    0.4250    0.4370       200\n",
      "\n",
      "    accuracy                         0.5750      1600\n",
      "   macro avg     0.5805    0.5750    0.5706      1600\n",
      "weighted avg     0.5805    0.5750    0.5706      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 57.12%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4328    0.5800    0.4957       200\n",
      "           1     0.5584    0.6450    0.5986       200\n",
      "           2     0.5398    0.3050    0.3898       200\n",
      "           3     0.5051    0.5000    0.5025       200\n",
      "           4     0.8824    0.8250    0.8527       200\n",
      "           5     0.8131    0.8700    0.8406       200\n",
      "           6     0.4250    0.4250    0.4250       200\n",
      "           7     0.4444    0.4200    0.4319       200\n",
      "\n",
      "    accuracy                         0.5713      1600\n",
      "   macro avg     0.5751    0.5713    0.5671      1600\n",
      "weighted avg     0.5751    0.5713    0.5671      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4345    0.5800    0.4968       200\n",
      "           1     0.5536    0.6450    0.5958       200\n",
      "           2     0.5596    0.3050    0.3948       200\n",
      "           3     0.5147    0.5250    0.5198       200\n",
      "           4     0.8824    0.8250    0.8527       200\n",
      "           5     0.8093    0.8700    0.8386       200\n",
      "           6     0.4271    0.4250    0.4261       200\n",
      "           7     0.4516    0.4200    0.4352       200\n",
      "\n",
      "    accuracy                         0.5744      1600\n",
      "   macro avg     0.5791    0.5744    0.5700      1600\n",
      "weighted avg     0.5791    0.5744    0.5700      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4307    0.5750    0.4925       200\n",
      "           1     0.5579    0.6500    0.6005       200\n",
      "           2     0.5660    0.3000    0.3922       200\n",
      "           3     0.5097    0.5250    0.5172       200\n",
      "           4     0.8824    0.8250    0.8527       200\n",
      "           5     0.8131    0.8700    0.8406       200\n",
      "           6     0.4343    0.4300    0.4322       200\n",
      "           7     0.4497    0.4250    0.4370       200\n",
      "\n",
      "    accuracy                         0.5750      1600\n",
      "   macro avg     0.5805    0.5750    0.5706      1600\n",
      "weighted avg     0.5805    0.5750    0.5706      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 57.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4328    0.5800    0.4957       200\n",
      "           1     0.5584    0.6450    0.5986       200\n",
      "           2     0.5446    0.3050    0.3910       200\n",
      "           3     0.5152    0.5100    0.5126       200\n",
      "           4     0.8824    0.8250    0.8527       200\n",
      "           5     0.8131    0.8700    0.8406       200\n",
      "           6     0.4271    0.4250    0.4261       200\n",
      "           7     0.4450    0.4250    0.4348       200\n",
      "\n",
      "    accuracy                         0.5731      1600\n",
      "   macro avg     0.5773    0.5731    0.5690      1600\n",
      "weighted avg     0.5773    0.5731    0.5690      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4345    0.5800    0.4968       200\n",
      "           1     0.5560    0.6450    0.5972       200\n",
      "           2     0.5596    0.3050    0.3948       200\n",
      "           3     0.5146    0.5300    0.5222       200\n",
      "           4     0.8824    0.8250    0.8527       200\n",
      "           5     0.8131    0.8700    0.8406       200\n",
      "           6     0.4315    0.4250    0.4282       200\n",
      "           7     0.4574    0.4300    0.4433       200\n",
      "\n",
      "    accuracy                         0.5763      1600\n",
      "   macro avg     0.5811    0.5762    0.5720      1600\n",
      "weighted avg     0.5811    0.5763    0.5720      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 57.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4307    0.5750    0.4925       200\n",
      "           1     0.5579    0.6500    0.6005       200\n",
      "           2     0.5660    0.3000    0.3922       200\n",
      "           3     0.5146    0.5300    0.5222       200\n",
      "           4     0.8824    0.8250    0.8527       200\n",
      "           5     0.8131    0.8700    0.8406       200\n",
      "           6     0.4343    0.4300    0.4322       200\n",
      "           7     0.4550    0.4300    0.4422       200\n",
      "\n",
      "    accuracy                         0.5763      1600\n",
      "   macro avg     0.5818    0.5762    0.5719      1600\n",
      "weighted avg     0.5818    0.5763    0.5719      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 57.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5550    0.4847       200\n",
      "           1     0.5600    0.6300    0.5929       200\n",
      "           2     0.5103    0.3700    0.4290       200\n",
      "           3     0.5543    0.5100    0.5312       200\n",
      "           4     0.8777    0.8250    0.8505       200\n",
      "           5     0.8278    0.8650    0.8460       200\n",
      "           6     0.4300    0.4450    0.4373       200\n",
      "           7     0.4511    0.4150    0.4323       200\n",
      "\n",
      "    accuracy                         0.5769      1600\n",
      "   macro avg     0.5802    0.5769    0.5755      1600\n",
      "weighted avg     0.5802    0.5769    0.5755      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4346    0.5650    0.4913       200\n",
      "           1     0.5676    0.6300    0.5972       200\n",
      "           2     0.5324    0.3700    0.4366       200\n",
      "           3     0.5426    0.5100    0.5258       200\n",
      "           4     0.8919    0.8250    0.8571       200\n",
      "           5     0.8302    0.8800    0.8544       200\n",
      "           6     0.4238    0.4450    0.4341       200\n",
      "           7     0.4565    0.4200    0.4375       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5849    0.5806    0.5792      1600\n",
      "weighted avg     0.5849    0.5806    0.5792      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5676    0.6300    0.5972       200\n",
      "           2     0.5252    0.3650    0.4307       200\n",
      "           3     0.5361    0.5200    0.5279       200\n",
      "           4     0.8824    0.8250    0.8527       200\n",
      "           5     0.8208    0.8700    0.8447       200\n",
      "           6     0.4335    0.4400    0.4367       200\n",
      "           7     0.4641    0.4200    0.4409       200\n",
      "\n",
      "    accuracy                         0.5794      1600\n",
      "   macro avg     0.5826    0.5794    0.5775      1600\n",
      "weighted avg     0.5826    0.5794    0.5775      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4346    0.5650    0.4913       200\n",
      "           1     0.5614    0.6400    0.5981       200\n",
      "           2     0.5368    0.3650    0.4345       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8871    0.8250    0.8549       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4231    0.4400    0.4314       200\n",
      "           7     0.4595    0.4250    0.4416       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5850    0.5806    0.5790      1600\n",
      "weighted avg     0.5850    0.5806    0.5790      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5650    0.6300    0.5957       200\n",
      "           2     0.5255    0.3600    0.4273       200\n",
      "           3     0.5393    0.5150    0.5269       200\n",
      "           4     0.8865    0.8200    0.8519       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4258    0.4450    0.4352       200\n",
      "           7     0.4615    0.4200    0.4398       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5830    0.5787    0.5772      1600\n",
      "weighted avg     0.5830    0.5787    0.5772      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5676    0.6300    0.5972       200\n",
      "           2     0.5252    0.3650    0.4307       200\n",
      "           3     0.5361    0.5200    0.5279       200\n",
      "           4     0.8824    0.8250    0.8527       200\n",
      "           5     0.8208    0.8700    0.8447       200\n",
      "           6     0.4335    0.4400    0.4367       200\n",
      "           7     0.4641    0.4200    0.4409       200\n",
      "\n",
      "    accuracy                         0.5794      1600\n",
      "   macro avg     0.5826    0.5794    0.5775      1600\n",
      "weighted avg     0.5826    0.5794    0.5775      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5600    0.4859       200\n",
      "           1     0.5747    0.6350    0.6033       200\n",
      "           2     0.5357    0.3750    0.4412       200\n",
      "           3     0.5538    0.5150    0.5337       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8286    0.8700    0.8488       200\n",
      "           6     0.4231    0.4400    0.4314       200\n",
      "           7     0.4521    0.4250    0.4381       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5848    0.5800    0.5790      1600\n",
      "weighted avg     0.5848    0.5800    0.5790      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4297    0.5650    0.4881       200\n",
      "           1     0.5676    0.6300    0.5972       200\n",
      "           2     0.5252    0.3650    0.4307       200\n",
      "           3     0.5450    0.5150    0.5296       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8286    0.8700    0.8488       200\n",
      "           6     0.4183    0.4350    0.4265       200\n",
      "           7     0.4590    0.4200    0.4386       200\n",
      "\n",
      "    accuracy                         0.5775      1600\n",
      "   macro avg     0.5819    0.5775    0.5761      1600\n",
      "weighted avg     0.5819    0.5775    0.5761      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=5, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5714    0.6400    0.6038       200\n",
      "           2     0.5286    0.3700    0.4353       200\n",
      "           3     0.5464    0.5300    0.5381       200\n",
      "           4     0.8865    0.8200    0.8519       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4265    0.4350    0.4307       200\n",
      "           7     0.4667    0.4200    0.4421       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5858    0.5819    0.5803      1600\n",
      "weighted avg     0.5858    0.5819    0.5803      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4344    0.5300    0.4775       200\n",
      "           1     0.5652    0.6500    0.6047       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5691    0.5350    0.5515       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8629    0.8500    0.8564       200\n",
      "           6     0.4408    0.4650    0.4526       200\n",
      "           7     0.4439    0.4150    0.4289       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5869    0.5856    0.5835      1600\n",
      "weighted avg     0.5869    0.5856    0.5835      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4187    0.5150    0.4619       200\n",
      "           1     0.5652    0.6500    0.6047       200\n",
      "           2     0.5108    0.3550    0.4189       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8743    0.8350    0.8542       200\n",
      "           6     0.4372    0.4700    0.4530       200\n",
      "           7     0.4541    0.4200    0.4364       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5859    0.5837    0.5817      1600\n",
      "weighted avg     0.5859    0.5837    0.5817      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4183    0.5250    0.4656       200\n",
      "           1     0.5830    0.6500    0.6147       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8743    0.8350    0.8542       200\n",
      "           6     0.4387    0.4650    0.4515       200\n",
      "           7     0.4607    0.4100    0.4339       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5885    0.5869    0.5847      1600\n",
      "weighted avg     0.5885    0.5869    0.5847      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4298    0.5200    0.4706       200\n",
      "           1     0.5603    0.6500    0.6019       200\n",
      "           2     0.5035    0.3600    0.4198       200\n",
      "           3     0.5642    0.5050    0.5330       200\n",
      "           4     0.8647    0.8950    0.8796       200\n",
      "           5     0.8705    0.8400    0.8550       200\n",
      "           6     0.4419    0.4750    0.4578       200\n",
      "           7     0.4286    0.4050    0.4165       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5829    0.5813    0.5793      1600\n",
      "weighted avg     0.5829    0.5813    0.5793      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4251    0.5250    0.4698       200\n",
      "           1     0.5752    0.6500    0.6103       200\n",
      "           2     0.5000    0.3550    0.4152       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8789    0.8350    0.8564       200\n",
      "           6     0.4398    0.4750    0.4567       200\n",
      "           7     0.4432    0.4100    0.4260       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5862    0.5844    0.5824      1600\n",
      "weighted avg     0.5862    0.5844    0.5824      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4183    0.5250    0.4656       200\n",
      "           1     0.5830    0.6500    0.6147       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8743    0.8350    0.8542       200\n",
      "           6     0.4387    0.4650    0.4515       200\n",
      "           7     0.4607    0.4100    0.4339       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5885    0.5869    0.5847      1600\n",
      "weighted avg     0.5885    0.5869    0.5847      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4190    0.5300    0.4680       200\n",
      "           1     0.5683    0.6450    0.6042       200\n",
      "           2     0.5000    0.3550    0.4152       200\n",
      "           3     0.5714    0.5200    0.5445       200\n",
      "           4     0.8647    0.8950    0.8796       200\n",
      "           5     0.8750    0.8400    0.8571       200\n",
      "           6     0.4507    0.4800    0.4649       200\n",
      "           7     0.4402    0.4050    0.4219       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5862    0.5837    0.5819      1600\n",
      "weighted avg     0.5862    0.5837    0.5819      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4180    0.5350    0.4693       200\n",
      "           1     0.5785    0.6450    0.6099       200\n",
      "           2     0.4965    0.3550    0.4140       200\n",
      "           3     0.5668    0.5300    0.5478       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8789    0.8350    0.8564       200\n",
      "           6     0.4481    0.4750    0.4612       200\n",
      "           7     0.4581    0.4100    0.4327       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5884    0.5863    0.5843      1600\n",
      "weighted avg     0.5884    0.5863    0.5843      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4150    0.5250    0.4636       200\n",
      "           1     0.5830    0.6500    0.6147       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5677    0.5450    0.5561       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8743    0.8350    0.8542       200\n",
      "           6     0.4408    0.4650    0.4526       200\n",
      "           7     0.4556    0.4100    0.4316       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5882    0.5863    0.5842      1600\n",
      "weighted avg     0.5882    0.5863    0.5842      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4344    0.5300    0.4775       200\n",
      "           1     0.5652    0.6500    0.6047       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5691    0.5350    0.5515       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8629    0.8500    0.8564       200\n",
      "           6     0.4408    0.4650    0.4526       200\n",
      "           7     0.4439    0.4150    0.4289       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5869    0.5856    0.5835      1600\n",
      "weighted avg     0.5869    0.5856    0.5835      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4187    0.5150    0.4619       200\n",
      "           1     0.5652    0.6500    0.6047       200\n",
      "           2     0.5108    0.3550    0.4189       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8743    0.8350    0.8542       200\n",
      "           6     0.4372    0.4700    0.4530       200\n",
      "           7     0.4541    0.4200    0.4364       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5859    0.5837    0.5817      1600\n",
      "weighted avg     0.5859    0.5837    0.5817      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4183    0.5250    0.4656       200\n",
      "           1     0.5830    0.6500    0.6147       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8743    0.8350    0.8542       200\n",
      "           6     0.4387    0.4650    0.4515       200\n",
      "           7     0.4607    0.4100    0.4339       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5885    0.5869    0.5847      1600\n",
      "weighted avg     0.5885    0.5869    0.5847      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4298    0.5200    0.4706       200\n",
      "           1     0.5603    0.6500    0.6019       200\n",
      "           2     0.5035    0.3600    0.4198       200\n",
      "           3     0.5642    0.5050    0.5330       200\n",
      "           4     0.8647    0.8950    0.8796       200\n",
      "           5     0.8705    0.8400    0.8550       200\n",
      "           6     0.4419    0.4750    0.4578       200\n",
      "           7     0.4286    0.4050    0.4165       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5829    0.5813    0.5793      1600\n",
      "weighted avg     0.5829    0.5813    0.5793      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4251    0.5250    0.4698       200\n",
      "           1     0.5752    0.6500    0.6103       200\n",
      "           2     0.5000    0.3550    0.4152       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8789    0.8350    0.8564       200\n",
      "           6     0.4398    0.4750    0.4567       200\n",
      "           7     0.4432    0.4100    0.4260       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5862    0.5844    0.5824      1600\n",
      "weighted avg     0.5862    0.5844    0.5824      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4183    0.5250    0.4656       200\n",
      "           1     0.5830    0.6500    0.6147       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8743    0.8350    0.8542       200\n",
      "           6     0.4387    0.4650    0.4515       200\n",
      "           7     0.4607    0.4100    0.4339       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5885    0.5869    0.5847      1600\n",
      "weighted avg     0.5885    0.5869    0.5847      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4190    0.5300    0.4680       200\n",
      "           1     0.5683    0.6450    0.6042       200\n",
      "           2     0.5000    0.3550    0.4152       200\n",
      "           3     0.5714    0.5200    0.5445       200\n",
      "           4     0.8647    0.8950    0.8796       200\n",
      "           5     0.8750    0.8400    0.8571       200\n",
      "           6     0.4507    0.4800    0.4649       200\n",
      "           7     0.4402    0.4050    0.4219       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5862    0.5837    0.5819      1600\n",
      "weighted avg     0.5862    0.5837    0.5819      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4180    0.5350    0.4693       200\n",
      "           1     0.5785    0.6450    0.6099       200\n",
      "           2     0.4965    0.3550    0.4140       200\n",
      "           3     0.5668    0.5300    0.5478       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8789    0.8350    0.8564       200\n",
      "           6     0.4481    0.4750    0.4612       200\n",
      "           7     0.4581    0.4100    0.4327       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5884    0.5863    0.5843      1600\n",
      "weighted avg     0.5884    0.5863    0.5843      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4150    0.5250    0.4636       200\n",
      "           1     0.5830    0.6500    0.6147       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5677    0.5450    0.5561       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8743    0.8350    0.8542       200\n",
      "           6     0.4408    0.4650    0.4526       200\n",
      "           7     0.4556    0.4100    0.4316       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5882    0.5863    0.5842      1600\n",
      "weighted avg     0.5882    0.5863    0.5842      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4234    0.5250    0.4688       200\n",
      "           1     0.5804    0.6500    0.6132       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5561    0.5450    0.5505       200\n",
      "           4     0.8647    0.8950    0.8796       200\n",
      "           5     0.8705    0.8400    0.8550       200\n",
      "           6     0.4419    0.4750    0.4578       200\n",
      "           7     0.4438    0.3950    0.4180       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5873    0.5856    0.5835      1600\n",
      "weighted avg     0.5873    0.5856    0.5835      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4234    0.5250    0.4688       200\n",
      "           1     0.5658    0.6450    0.6028       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5645    0.5250    0.5440       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8789    0.8350    0.8564       200\n",
      "           6     0.4372    0.4700    0.4530       200\n",
      "           7     0.4536    0.4150    0.4334       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5866    0.5844    0.5824      1600\n",
      "weighted avg     0.5866    0.5844    0.5824      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4143    0.5200    0.4612       200\n",
      "           1     0.5830    0.6500    0.6147       200\n",
      "           2     0.5108    0.3550    0.4189       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8743    0.8350    0.8542       200\n",
      "           6     0.4360    0.4600    0.4477       200\n",
      "           7     0.4667    0.4200    0.4421       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5889    0.5869    0.5848      1600\n",
      "weighted avg     0.5889    0.5869    0.5848      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4292    0.5150    0.4682       200\n",
      "           1     0.5603    0.6500    0.6019       200\n",
      "           2     0.5070    0.3600    0.4211       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8647    0.8950    0.8796       200\n",
      "           5     0.8660    0.8400    0.8528       200\n",
      "           6     0.4419    0.4750    0.4578       200\n",
      "           7     0.4409    0.4100    0.4249       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5844    0.5831    0.5810      1600\n",
      "weighted avg     0.5844    0.5831    0.5810      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4234    0.5250    0.4688       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5035    0.3550    0.4164       200\n",
      "           3     0.5707    0.5250    0.5469       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8789    0.8350    0.8564       200\n",
      "           6     0.4424    0.4800    0.4604       200\n",
      "           7     0.4481    0.4100    0.4282       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5877    0.5856    0.5836      1600\n",
      "weighted avg     0.5877    0.5856    0.5836      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4143    0.5200    0.4612       200\n",
      "           1     0.5830    0.6500    0.6147       200\n",
      "           2     0.5108    0.3550    0.4189       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8743    0.8350    0.8542       200\n",
      "           6     0.4360    0.4600    0.4477       200\n",
      "           7     0.4667    0.4200    0.4421       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5889    0.5869    0.5848      1600\n",
      "weighted avg     0.5889    0.5869    0.5848      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4153    0.5150    0.4598       200\n",
      "           1     0.5683    0.6450    0.6042       200\n",
      "           2     0.5035    0.3550    0.4164       200\n",
      "           3     0.5628    0.5150    0.5379       200\n",
      "           4     0.8647    0.8950    0.8796       200\n",
      "           5     0.8705    0.8400    0.8550       200\n",
      "           6     0.4424    0.4800    0.4604       200\n",
      "           7     0.4511    0.4150    0.4323       200\n",
      "\n",
      "    accuracy                         0.5825      1600\n",
      "   macro avg     0.5848    0.5825    0.5807      1600\n",
      "weighted avg     0.5848    0.5825    0.5807      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4183    0.5250    0.4656       200\n",
      "           1     0.5822    0.6550    0.6165       200\n",
      "           2     0.5035    0.3550    0.4164       200\n",
      "           3     0.5645    0.5250    0.5440       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8789    0.8350    0.8564       200\n",
      "           6     0.4404    0.4800    0.4593       200\n",
      "           7     0.4581    0.4100    0.4327       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5885    0.5862    0.5842      1600\n",
      "weighted avg     0.5885    0.5863    0.5842      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4127    0.5200    0.4602       200\n",
      "           1     0.5811    0.6450    0.6114       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5677    0.5450    0.5561       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8743    0.8350    0.8542       200\n",
      "           6     0.4387    0.4650    0.4515       200\n",
      "           7     0.4586    0.4150    0.4357       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5878    0.5856    0.5837      1600\n",
      "weighted avg     0.5878    0.5856    0.5837      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4183    0.5500    0.4752       200\n",
      "           1     0.5801    0.6700    0.6218       200\n",
      "           2     0.5556    0.3000    0.3896       200\n",
      "           3     0.5327    0.5300    0.5313       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4279    0.4750    0.4502       200\n",
      "           7     0.4551    0.4050    0.4286       200\n",
      "\n",
      "    accuracy                         0.5825      1600\n",
      "   macro avg     0.5880    0.5825    0.5786      1600\n",
      "weighted avg     0.5880    0.5825    0.5786      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4192    0.5450    0.4739       200\n",
      "           1     0.5702    0.6700    0.6161       200\n",
      "           2     0.5686    0.2900    0.3841       200\n",
      "           3     0.5286    0.5550    0.5415       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4273    0.4700    0.4476       200\n",
      "           7     0.4655    0.4050    0.4332       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5892    0.5831    0.5785      1600\n",
      "weighted avg     0.5892    0.5831    0.5785      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4208    0.5450    0.4749       200\n",
      "           1     0.5678    0.6700    0.6147       200\n",
      "           2     0.5743    0.2900    0.3854       200\n",
      "           3     0.5283    0.5600    0.5437       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4201    0.4600    0.4391       200\n",
      "           7     0.4598    0.4000    0.4278       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5882    0.5819    0.5772      1600\n",
      "weighted avg     0.5882    0.5819    0.5772      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4167    0.5500    0.4741       200\n",
      "           1     0.5903    0.6700    0.6276       200\n",
      "           2     0.5566    0.2950    0.3856       200\n",
      "           3     0.5366    0.5500    0.5432       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4279    0.4750    0.4502       200\n",
      "           7     0.4576    0.4050    0.4297       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5900    0.5844    0.5803      1600\n",
      "weighted avg     0.5900    0.5844    0.5803      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4167    0.5500    0.4741       200\n",
      "           1     0.5852    0.6700    0.6247       200\n",
      "           2     0.5673    0.2950    0.3882       200\n",
      "           3     0.5311    0.5550    0.5428       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4273    0.4700    0.4476       200\n",
      "           7     0.4629    0.4050    0.4320       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5906    0.5844    0.5802      1600\n",
      "weighted avg     0.5906    0.5844    0.5802      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4208    0.5450    0.4749       200\n",
      "           1     0.5678    0.6700    0.6147       200\n",
      "           2     0.5743    0.2900    0.3854       200\n",
      "           3     0.5283    0.5600    0.5437       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4201    0.4600    0.4391       200\n",
      "           7     0.4598    0.4000    0.4278       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5882    0.5819    0.5772      1600\n",
      "weighted avg     0.5882    0.5819    0.5772      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4167    0.5500    0.4741       200\n",
      "           1     0.5903    0.6700    0.6276       200\n",
      "           2     0.5566    0.2950    0.3856       200\n",
      "           3     0.5362    0.5550    0.5455       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4279    0.4750    0.4502       200\n",
      "           7     0.4629    0.4050    0.4320       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5906    0.5850    0.5809      1600\n",
      "weighted avg     0.5906    0.5850    0.5809      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4167    0.5500    0.4741       200\n",
      "           1     0.5877    0.6700    0.6262       200\n",
      "           2     0.5728    0.2950    0.3894       200\n",
      "           3     0.5381    0.5650    0.5512       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4318    0.4750    0.4524       200\n",
      "           7     0.4659    0.4100    0.4362       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5934    0.5869    0.5827      1600\n",
      "weighted avg     0.5934    0.5869    0.5827      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4208    0.5450    0.4749       200\n",
      "           1     0.5678    0.6700    0.6147       200\n",
      "           2     0.5700    0.2850    0.3800       200\n",
      "           3     0.5238    0.5500    0.5366       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4201    0.4600    0.4391       200\n",
      "           7     0.4520    0.4000    0.4244       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5861    0.5800    0.5752      1600\n",
      "weighted avg     0.5861    0.5800    0.5752      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4344    0.5300    0.4775       200\n",
      "           1     0.5652    0.6500    0.6047       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5691    0.5350    0.5515       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8629    0.8500    0.8564       200\n",
      "           6     0.4408    0.4650    0.4526       200\n",
      "           7     0.4439    0.4150    0.4289       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5869    0.5856    0.5835      1600\n",
      "weighted avg     0.5869    0.5856    0.5835      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4187    0.5150    0.4619       200\n",
      "           1     0.5652    0.6500    0.6047       200\n",
      "           2     0.5108    0.3550    0.4189       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8743    0.8350    0.8542       200\n",
      "           6     0.4372    0.4700    0.4530       200\n",
      "           7     0.4541    0.4200    0.4364       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5859    0.5837    0.5817      1600\n",
      "weighted avg     0.5859    0.5837    0.5817      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4183    0.5250    0.4656       200\n",
      "           1     0.5830    0.6500    0.6147       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8743    0.8350    0.8542       200\n",
      "           6     0.4387    0.4650    0.4515       200\n",
      "           7     0.4607    0.4100    0.4339       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5885    0.5869    0.5847      1600\n",
      "weighted avg     0.5885    0.5869    0.5847      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4298    0.5200    0.4706       200\n",
      "           1     0.5603    0.6500    0.6019       200\n",
      "           2     0.5035    0.3600    0.4198       200\n",
      "           3     0.5642    0.5050    0.5330       200\n",
      "           4     0.8647    0.8950    0.8796       200\n",
      "           5     0.8705    0.8400    0.8550       200\n",
      "           6     0.4419    0.4750    0.4578       200\n",
      "           7     0.4286    0.4050    0.4165       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5829    0.5813    0.5793      1600\n",
      "weighted avg     0.5829    0.5813    0.5793      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4251    0.5250    0.4698       200\n",
      "           1     0.5752    0.6500    0.6103       200\n",
      "           2     0.5000    0.3550    0.4152       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8789    0.8350    0.8564       200\n",
      "           6     0.4398    0.4750    0.4567       200\n",
      "           7     0.4432    0.4100    0.4260       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5862    0.5844    0.5824      1600\n",
      "weighted avg     0.5862    0.5844    0.5824      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4183    0.5250    0.4656       200\n",
      "           1     0.5830    0.6500    0.6147       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8743    0.8350    0.8542       200\n",
      "           6     0.4387    0.4650    0.4515       200\n",
      "           7     0.4607    0.4100    0.4339       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5885    0.5869    0.5847      1600\n",
      "weighted avg     0.5885    0.5869    0.5847      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4190    0.5300    0.4680       200\n",
      "           1     0.5683    0.6450    0.6042       200\n",
      "           2     0.5000    0.3550    0.4152       200\n",
      "           3     0.5714    0.5200    0.5445       200\n",
      "           4     0.8647    0.8950    0.8796       200\n",
      "           5     0.8750    0.8400    0.8571       200\n",
      "           6     0.4507    0.4800    0.4649       200\n",
      "           7     0.4402    0.4050    0.4219       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5862    0.5837    0.5819      1600\n",
      "weighted avg     0.5862    0.5837    0.5819      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4180    0.5350    0.4693       200\n",
      "           1     0.5785    0.6450    0.6099       200\n",
      "           2     0.4965    0.3550    0.4140       200\n",
      "           3     0.5668    0.5300    0.5478       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8789    0.8350    0.8564       200\n",
      "           6     0.4481    0.4750    0.4612       200\n",
      "           7     0.4581    0.4100    0.4327       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5884    0.5863    0.5843      1600\n",
      "weighted avg     0.5884    0.5863    0.5843      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=8, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4150    0.5250    0.4636       200\n",
      "           1     0.5830    0.6500    0.6147       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5677    0.5450    0.5561       200\n",
      "           4     0.8619    0.9050    0.8829       200\n",
      "           5     0.8743    0.8350    0.8542       200\n",
      "           6     0.4408    0.4650    0.4526       200\n",
      "           7     0.4556    0.4100    0.4316       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5882    0.5863    0.5842      1600\n",
      "weighted avg     0.5882    0.5863    0.5842      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4330    0.5650    0.4902       200\n",
      "           1     0.5856    0.6500    0.6161       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5805    0.5050    0.5401       200\n",
      "           4     0.8756    0.8800    0.8778       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4563    0.4700    0.4631       200\n",
      "           7     0.4623    0.4600    0.4612       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5943    0.5913    0.5894      1600\n",
      "weighted avg     0.5943    0.5913    0.5894      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5893    0.6600    0.6226       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5824    0.5300    0.5550       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8500    0.8500    0.8500       200\n",
      "           6     0.4677    0.4700    0.4688       200\n",
      "           7     0.4592    0.4500    0.4545       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5970    0.5938    0.5919      1600\n",
      "weighted avg     0.5970    0.5938    0.5919      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5650    0.4860       200\n",
      "           1     0.5859    0.6650    0.6230       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5612    0.5500    0.5556       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4754    0.4350    0.4543       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5981    0.5950    0.5927      1600\n",
      "weighted avg     0.5981    0.5950    0.5927      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5893    0.6600    0.6226       200\n",
      "           2     0.5145    0.3550    0.4201       200\n",
      "           3     0.5843    0.5200    0.5503       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8500    0.8500    0.8500       200\n",
      "           6     0.4653    0.4700    0.4677       200\n",
      "           7     0.4670    0.4600    0.4635       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5970    0.5938    0.5919      1600\n",
      "weighted avg     0.5970    0.5938    0.5919      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5919    0.6600    0.6241       200\n",
      "           2     0.5255    0.3600    0.4273       200\n",
      "           3     0.5753    0.5350    0.5544       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8500    0.8500    0.8500       200\n",
      "           6     0.4650    0.4650    0.4650       200\n",
      "           7     0.4663    0.4500    0.4580       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5975    0.5944    0.5925      1600\n",
      "weighted avg     0.5975    0.5944    0.5925      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5650    0.4860       200\n",
      "           1     0.5859    0.6650    0.6230       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5612    0.5500    0.5556       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4754    0.4350    0.4543       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5981    0.5950    0.5927      1600\n",
      "weighted avg     0.5981    0.5950    0.5927      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4330    0.5650    0.4902       200\n",
      "           1     0.5893    0.6600    0.6226       200\n",
      "           2     0.5108    0.3550    0.4189       200\n",
      "           3     0.5778    0.5200    0.5474       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8500    0.8500    0.8500       200\n",
      "           6     0.4703    0.4750    0.4726       200\n",
      "           7     0.4564    0.4450    0.4506       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5952    0.5925    0.5906      1600\n",
      "weighted avg     0.5952    0.5925    0.5906      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4297    0.5650    0.4881       200\n",
      "           1     0.5893    0.6600    0.6226       200\n",
      "           2     0.5255    0.3600    0.4273       200\n",
      "           3     0.5677    0.5450    0.5561       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4750    0.4750    0.4750       200\n",
      "           7     0.4624    0.4300    0.4456       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5973    0.5944    0.5924      1600\n",
      "weighted avg     0.5973    0.5944    0.5924      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5650    0.4860       200\n",
      "           1     0.5885    0.6650    0.6244       200\n",
      "           2     0.5294    0.3600    0.4286       200\n",
      "           3     0.5619    0.5450    0.5533       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4747    0.4700    0.4724       200\n",
      "           7     0.4699    0.4300    0.4491       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5974    0.5944    0.5923      1600\n",
      "weighted avg     0.5974    0.5944    0.5923      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4330    0.5650    0.4902       200\n",
      "           1     0.5856    0.6500    0.6161       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5805    0.5050    0.5401       200\n",
      "           4     0.8756    0.8800    0.8778       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4563    0.4700    0.4631       200\n",
      "           7     0.4623    0.4600    0.4612       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5943    0.5913    0.5894      1600\n",
      "weighted avg     0.5943    0.5913    0.5894      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5893    0.6600    0.6226       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5824    0.5300    0.5550       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8500    0.8500    0.8500       200\n",
      "           6     0.4677    0.4700    0.4688       200\n",
      "           7     0.4592    0.4500    0.4545       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5970    0.5938    0.5919      1600\n",
      "weighted avg     0.5970    0.5938    0.5919      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5650    0.4860       200\n",
      "           1     0.5859    0.6650    0.6230       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5612    0.5500    0.5556       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4754    0.4350    0.4543       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5981    0.5950    0.5927      1600\n",
      "weighted avg     0.5981    0.5950    0.5927      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5893    0.6600    0.6226       200\n",
      "           2     0.5145    0.3550    0.4201       200\n",
      "           3     0.5843    0.5200    0.5503       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8500    0.8500    0.8500       200\n",
      "           6     0.4653    0.4700    0.4677       200\n",
      "           7     0.4670    0.4600    0.4635       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5970    0.5938    0.5919      1600\n",
      "weighted avg     0.5970    0.5938    0.5919      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5919    0.6600    0.6241       200\n",
      "           2     0.5255    0.3600    0.4273       200\n",
      "           3     0.5753    0.5350    0.5544       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8500    0.8500    0.8500       200\n",
      "           6     0.4650    0.4650    0.4650       200\n",
      "           7     0.4663    0.4500    0.4580       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5975    0.5944    0.5925      1600\n",
      "weighted avg     0.5975    0.5944    0.5925      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5650    0.4860       200\n",
      "           1     0.5859    0.6650    0.6230       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5612    0.5500    0.5556       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4754    0.4350    0.4543       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5981    0.5950    0.5927      1600\n",
      "weighted avg     0.5981    0.5950    0.5927      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4330    0.5650    0.4902       200\n",
      "           1     0.5893    0.6600    0.6226       200\n",
      "           2     0.5108    0.3550    0.4189       200\n",
      "           3     0.5778    0.5200    0.5474       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8500    0.8500    0.8500       200\n",
      "           6     0.4703    0.4750    0.4726       200\n",
      "           7     0.4564    0.4450    0.4506       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5952    0.5925    0.5906      1600\n",
      "weighted avg     0.5952    0.5925    0.5906      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4297    0.5650    0.4881       200\n",
      "           1     0.5893    0.6600    0.6226       200\n",
      "           2     0.5255    0.3600    0.4273       200\n",
      "           3     0.5677    0.5450    0.5561       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4750    0.4750    0.4750       200\n",
      "           7     0.4624    0.4300    0.4456       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5973    0.5944    0.5924      1600\n",
      "weighted avg     0.5973    0.5944    0.5924      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5650    0.4860       200\n",
      "           1     0.5885    0.6650    0.6244       200\n",
      "           2     0.5294    0.3600    0.4286       200\n",
      "           3     0.5619    0.5450    0.5533       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4747    0.4700    0.4724       200\n",
      "           7     0.4699    0.4300    0.4491       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5974    0.5944    0.5923      1600\n",
      "weighted avg     0.5974    0.5944    0.5923      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4330    0.5650    0.4902       200\n",
      "           1     0.5752    0.6500    0.6103       200\n",
      "           2     0.4965    0.3500    0.4106       200\n",
      "           3     0.5810    0.5200    0.5488       200\n",
      "           4     0.8750    0.8750    0.8750       200\n",
      "           5     0.8500    0.8500    0.8500       200\n",
      "           6     0.4680    0.4750    0.4715       200\n",
      "           7     0.4737    0.4500    0.4615       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5940    0.5919    0.5897      1600\n",
      "weighted avg     0.5940    0.5919    0.5897      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5919    0.6600    0.6241       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5902    0.5400    0.5640       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8500    0.8500    0.8500       200\n",
      "           6     0.4677    0.4700    0.4688       200\n",
      "           7     0.4643    0.4550    0.4596       200\n",
      "\n",
      "    accuracy                         0.5956      1600\n",
      "   macro avg     0.5990    0.5956    0.5938      1600\n",
      "weighted avg     0.5990    0.5956    0.5938      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5650    0.4860       200\n",
      "           1     0.5859    0.6650    0.6230       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5612    0.5500    0.5556       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4754    0.4350    0.4543       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5981    0.5950    0.5927      1600\n",
      "weighted avg     0.5981    0.5950    0.5927      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5848    0.6550    0.6179       200\n",
      "           2     0.5145    0.3550    0.4201       200\n",
      "           3     0.5843    0.5200    0.5503       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8500    0.8500    0.8500       200\n",
      "           6     0.4653    0.4700    0.4677       200\n",
      "           7     0.4670    0.4600    0.4635       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.5965    0.5931    0.5914      1600\n",
      "weighted avg     0.5965    0.5931    0.5914      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5893    0.6600    0.6226       200\n",
      "           2     0.5255    0.3600    0.4273       200\n",
      "           3     0.5730    0.5300    0.5506       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8500    0.8500    0.8500       200\n",
      "           6     0.4650    0.4650    0.4650       200\n",
      "           7     0.4663    0.4500    0.4580       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5968    0.5938    0.5919      1600\n",
      "weighted avg     0.5968    0.5938    0.5919      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5650    0.4860       200\n",
      "           1     0.5859    0.6650    0.6230       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5612    0.5500    0.5556       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4754    0.4350    0.4543       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5981    0.5950    0.5927      1600\n",
      "weighted avg     0.5981    0.5950    0.5927      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4330    0.5650    0.4902       200\n",
      "           1     0.5893    0.6600    0.6226       200\n",
      "           2     0.5108    0.3550    0.4189       200\n",
      "           3     0.5746    0.5200    0.5459       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8500    0.8500    0.8500       200\n",
      "           6     0.4703    0.4750    0.4726       200\n",
      "           7     0.4588    0.4450    0.4518       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5951    0.5925    0.5905      1600\n",
      "weighted avg     0.5951    0.5925    0.5905      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4297    0.5650    0.4881       200\n",
      "           1     0.5893    0.6600    0.6226       200\n",
      "           2     0.5217    0.3600    0.4260       200\n",
      "           3     0.5677    0.5450    0.5561       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4750    0.4750    0.4750       200\n",
      "           7     0.4649    0.4300    0.4468       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5971    0.5944    0.5924      1600\n",
      "weighted avg     0.5971    0.5944    0.5924      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5650    0.4860       200\n",
      "           1     0.5885    0.6650    0.6244       200\n",
      "           2     0.5294    0.3600    0.4286       200\n",
      "           3     0.5619    0.5450    0.5533       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4747    0.4700    0.4724       200\n",
      "           7     0.4699    0.4300    0.4491       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5974    0.5944    0.5923      1600\n",
      "weighted avg     0.5974    0.5944    0.5923      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4173    0.5800    0.4854       200\n",
      "           1     0.5690    0.6800    0.6196       200\n",
      "           2     0.5657    0.2800    0.3746       200\n",
      "           3     0.5476    0.5750    0.5610       200\n",
      "           4     0.8750    0.8750    0.8750       200\n",
      "           5     0.8450    0.8450    0.8450       200\n",
      "           6     0.4519    0.4700    0.4608       200\n",
      "           7     0.4819    0.4000    0.4372       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5942    0.5881    0.5823      1600\n",
      "weighted avg     0.5942    0.5881    0.5823      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4194    0.5850    0.4885       200\n",
      "           1     0.5708    0.6850    0.6227       200\n",
      "           2     0.5789    0.2750    0.3729       200\n",
      "           3     0.5583    0.5750    0.5665       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8408    0.8450    0.8429       200\n",
      "           6     0.4481    0.4750    0.4612       200\n",
      "           7     0.4821    0.4050    0.4402       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5966    0.5894    0.5834      1600\n",
      "weighted avg     0.5966    0.5894    0.5834      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4209    0.5850    0.4895       200\n",
      "           1     0.5638    0.6850    0.6185       200\n",
      "           2     0.5955    0.2650    0.3668       200\n",
      "           3     0.5360    0.5950    0.5640       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8408    0.8450    0.8429       200\n",
      "           6     0.4476    0.4700    0.4585       200\n",
      "           7     0.4937    0.3900    0.4358       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5966    0.5881    0.5810      1600\n",
      "weighted avg     0.5966    0.5881    0.5810      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4173    0.5800    0.4854       200\n",
      "           1     0.5667    0.6800    0.6182       200\n",
      "           2     0.5789    0.2750    0.3729       200\n",
      "           3     0.5604    0.5800    0.5700       200\n",
      "           4     0.8750    0.8750    0.8750       200\n",
      "           5     0.8450    0.8450    0.8450       200\n",
      "           6     0.4481    0.4750    0.4612       200\n",
      "           7     0.4821    0.4050    0.4402       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5967    0.5894    0.5835      1600\n",
      "weighted avg     0.5967    0.5894    0.5835      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4209    0.5850    0.4895       200\n",
      "           1     0.5661    0.6850    0.6199       200\n",
      "           2     0.5729    0.2750    0.3716       200\n",
      "           3     0.5583    0.5750    0.5665       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8408    0.8450    0.8429       200\n",
      "           6     0.4524    0.4750    0.4634       200\n",
      "           7     0.4821    0.4050    0.4402       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5960    0.5894    0.5833      1600\n",
      "weighted avg     0.5960    0.5894    0.5833      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4209    0.5850    0.4895       200\n",
      "           1     0.5638    0.6850    0.6185       200\n",
      "           2     0.5955    0.2650    0.3668       200\n",
      "           3     0.5360    0.5950    0.5640       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8408    0.8450    0.8429       200\n",
      "           6     0.4476    0.4700    0.4585       200\n",
      "           7     0.4937    0.3900    0.4358       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5966    0.5881    0.5810      1600\n",
      "weighted avg     0.5966    0.5881    0.5810      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4194    0.5850    0.4885       200\n",
      "           1     0.5643    0.6800    0.6168       200\n",
      "           2     0.5789    0.2750    0.3729       200\n",
      "           3     0.5455    0.5700    0.5575       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8408    0.8450    0.8429       200\n",
      "           6     0.4465    0.4800    0.4627       200\n",
      "           7     0.4845    0.3900    0.4321       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5943    0.5869    0.5807      1600\n",
      "weighted avg     0.5943    0.5869    0.5807      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4188    0.5800    0.4864       200\n",
      "           1     0.5661    0.6850    0.6199       200\n",
      "           2     0.5789    0.2750    0.3729       200\n",
      "           3     0.5425    0.5750    0.5583       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8408    0.8450    0.8429       200\n",
      "           6     0.4507    0.4800    0.4649       200\n",
      "           7     0.4907    0.3950    0.4377       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5954    0.5881    0.5819      1600\n",
      "weighted avg     0.5954    0.5881    0.5819      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4209    0.5850    0.4895       200\n",
      "           1     0.5638    0.6850    0.6185       200\n",
      "           2     0.5955    0.2650    0.3668       200\n",
      "           3     0.5385    0.5950    0.5653       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8408    0.8450    0.8429       200\n",
      "           6     0.4502    0.4750    0.4623       200\n",
      "           7     0.4937    0.3900    0.4358       200\n",
      "\n",
      "    accuracy                         0.5887      1600\n",
      "   macro avg     0.5972    0.5887    0.5817      1600\n",
      "weighted avg     0.5972    0.5887    0.5817      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4330    0.5650    0.4902       200\n",
      "           1     0.5856    0.6500    0.6161       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5805    0.5050    0.5401       200\n",
      "           4     0.8756    0.8800    0.8778       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4563    0.4700    0.4631       200\n",
      "           7     0.4623    0.4600    0.4612       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5943    0.5913    0.5894      1600\n",
      "weighted avg     0.5943    0.5913    0.5894      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5893    0.6600    0.6226       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5824    0.5300    0.5550       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8500    0.8500    0.8500       200\n",
      "           6     0.4677    0.4700    0.4688       200\n",
      "           7     0.4592    0.4500    0.4545       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5970    0.5938    0.5919      1600\n",
      "weighted avg     0.5970    0.5938    0.5919      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5650    0.4860       200\n",
      "           1     0.5859    0.6650    0.6230       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5612    0.5500    0.5556       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4754    0.4350    0.4543       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5981    0.5950    0.5927      1600\n",
      "weighted avg     0.5981    0.5950    0.5927      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5893    0.6600    0.6226       200\n",
      "           2     0.5145    0.3550    0.4201       200\n",
      "           3     0.5843    0.5200    0.5503       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8500    0.8500    0.8500       200\n",
      "           6     0.4653    0.4700    0.4677       200\n",
      "           7     0.4670    0.4600    0.4635       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5970    0.5938    0.5919      1600\n",
      "weighted avg     0.5970    0.5938    0.5919      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5919    0.6600    0.6241       200\n",
      "           2     0.5255    0.3600    0.4273       200\n",
      "           3     0.5753    0.5350    0.5544       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8500    0.8500    0.8500       200\n",
      "           6     0.4650    0.4650    0.4650       200\n",
      "           7     0.4663    0.4500    0.4580       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5975    0.5944    0.5925      1600\n",
      "weighted avg     0.5975    0.5944    0.5925      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5650    0.4860       200\n",
      "           1     0.5859    0.6650    0.6230       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5612    0.5500    0.5556       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4754    0.4350    0.4543       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5981    0.5950    0.5927      1600\n",
      "weighted avg     0.5981    0.5950    0.5927      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4330    0.5650    0.4902       200\n",
      "           1     0.5893    0.6600    0.6226       200\n",
      "           2     0.5108    0.3550    0.4189       200\n",
      "           3     0.5778    0.5200    0.5474       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8500    0.8500    0.8500       200\n",
      "           6     0.4703    0.4750    0.4726       200\n",
      "           7     0.4564    0.4450    0.4506       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5952    0.5925    0.5906      1600\n",
      "weighted avg     0.5952    0.5925    0.5906      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4297    0.5650    0.4881       200\n",
      "           1     0.5893    0.6600    0.6226       200\n",
      "           2     0.5255    0.3600    0.4273       200\n",
      "           3     0.5677    0.5450    0.5561       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4750    0.4750    0.4750       200\n",
      "           7     0.4624    0.4300    0.4456       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5973    0.5944    0.5924      1600\n",
      "weighted avg     0.5973    0.5944    0.5924      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=12, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5650    0.4860       200\n",
      "           1     0.5885    0.6650    0.6244       200\n",
      "           2     0.5294    0.3600    0.4286       200\n",
      "           3     0.5619    0.5450    0.5533       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8543    0.8500    0.8521       200\n",
      "           6     0.4747    0.4700    0.4724       200\n",
      "           7     0.4699    0.4300    0.4491       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5974    0.5944    0.5923      1600\n",
      "weighted avg     0.5974    0.5944    0.5923      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 60.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4723    0.5550    0.5103       200\n",
      "           1     0.5895    0.6750    0.6294       200\n",
      "           2     0.5232    0.3950    0.4501       200\n",
      "           3     0.5740    0.4850    0.5257       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4581    0.4650    0.4615       200\n",
      "\n",
      "    accuracy                         0.6000      1600\n",
      "   macro avg     0.6026    0.6000    0.5988      1600\n",
      "weighted avg     0.6026    0.6000    0.5988      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 60.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4723    0.5550    0.5103       200\n",
      "           1     0.5787    0.6800    0.6253       200\n",
      "           2     0.5203    0.3850    0.4425       200\n",
      "           3     0.5585    0.5250    0.5412       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4807    0.4350    0.4567       200\n",
      "\n",
      "    accuracy                         0.6006      1600\n",
      "   macro avg     0.6018    0.6006    0.5987      1600\n",
      "weighted avg     0.6018    0.6006    0.5987      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5855    0.6850    0.6313       200\n",
      "           2     0.5161    0.4000    0.4507       200\n",
      "           3     0.5436    0.5300    0.5367       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4909    0.4050    0.4438       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6024    0.6019    0.5996      1600\n",
      "weighted avg     0.6024    0.6019    0.5996      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4744    0.5550    0.5115       200\n",
      "           1     0.5756    0.6850    0.6256       200\n",
      "           2     0.5374    0.3950    0.4553       200\n",
      "           3     0.5632    0.4900    0.5241       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4742    0.4600    0.4670       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6042    0.6019    0.6002      1600\n",
      "weighted avg     0.6042    0.6019    0.6002      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4723    0.5550    0.5103       200\n",
      "           1     0.5787    0.6800    0.6253       200\n",
      "           2     0.5338    0.3950    0.4540       200\n",
      "           3     0.5604    0.5100    0.5340       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4558    0.4900    0.4723       200\n",
      "           7     0.4815    0.4550    0.4679       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6038    0.6019    0.6003      1600\n",
      "weighted avg     0.6038    0.6019    0.6003      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5855    0.6850    0.6313       200\n",
      "           2     0.5161    0.4000    0.4507       200\n",
      "           3     0.5436    0.5300    0.5367       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4909    0.4050    0.4438       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6024    0.6019    0.5996      1600\n",
      "weighted avg     0.6024    0.6019    0.5996      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 60.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4744    0.5550    0.5115       200\n",
      "           1     0.5880    0.6850    0.6328       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5574    0.5100    0.5326       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4787    0.4500    0.4639       200\n",
      "\n",
      "    accuracy                         0.6025      1600\n",
      "   macro avg     0.6041    0.6025    0.6009      1600\n",
      "weighted avg     0.6041    0.6025    0.6009      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 60.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4766    0.5600    0.5149       200\n",
      "           1     0.5837    0.6800    0.6282       200\n",
      "           2     0.5267    0.3950    0.4514       200\n",
      "           3     0.5550    0.5300    0.5422       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4888    0.4350    0.4603       200\n",
      "\n",
      "    accuracy                         0.6038      1600\n",
      "   macro avg     0.6049    0.6038    0.6019      1600\n",
      "weighted avg     0.6049    0.6038    0.6019      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 60.12%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4726    0.5600    0.5126       200\n",
      "           1     0.5830    0.6850    0.6299       200\n",
      "           2     0.5128    0.4000    0.4494       200\n",
      "           3     0.5459    0.5350    0.5404       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4583    0.4950    0.4760       200\n",
      "           7     0.4939    0.4050    0.4451       200\n",
      "\n",
      "    accuracy                         0.6012      1600\n",
      "   macro avg     0.6018    0.6013    0.5990      1600\n",
      "weighted avg     0.6018    0.6012    0.5990      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 60.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4723    0.5550    0.5103       200\n",
      "           1     0.5895    0.6750    0.6294       200\n",
      "           2     0.5232    0.3950    0.4501       200\n",
      "           3     0.5740    0.4850    0.5257       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4581    0.4650    0.4615       200\n",
      "\n",
      "    accuracy                         0.6000      1600\n",
      "   macro avg     0.6026    0.6000    0.5988      1600\n",
      "weighted avg     0.6026    0.6000    0.5988      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 60.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4723    0.5550    0.5103       200\n",
      "           1     0.5787    0.6800    0.6253       200\n",
      "           2     0.5203    0.3850    0.4425       200\n",
      "           3     0.5585    0.5250    0.5412       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4807    0.4350    0.4567       200\n",
      "\n",
      "    accuracy                         0.6006      1600\n",
      "   macro avg     0.6018    0.6006    0.5987      1600\n",
      "weighted avg     0.6018    0.6006    0.5987      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5855    0.6850    0.6313       200\n",
      "           2     0.5161    0.4000    0.4507       200\n",
      "           3     0.5436    0.5300    0.5367       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4909    0.4050    0.4438       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6024    0.6019    0.5996      1600\n",
      "weighted avg     0.6024    0.6019    0.5996      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4744    0.5550    0.5115       200\n",
      "           1     0.5756    0.6850    0.6256       200\n",
      "           2     0.5374    0.3950    0.4553       200\n",
      "           3     0.5632    0.4900    0.5241       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4742    0.4600    0.4670       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6042    0.6019    0.6002      1600\n",
      "weighted avg     0.6042    0.6019    0.6002      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4723    0.5550    0.5103       200\n",
      "           1     0.5787    0.6800    0.6253       200\n",
      "           2     0.5338    0.3950    0.4540       200\n",
      "           3     0.5604    0.5100    0.5340       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4558    0.4900    0.4723       200\n",
      "           7     0.4815    0.4550    0.4679       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6038    0.6019    0.6003      1600\n",
      "weighted avg     0.6038    0.6019    0.6003      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5855    0.6850    0.6313       200\n",
      "           2     0.5161    0.4000    0.4507       200\n",
      "           3     0.5436    0.5300    0.5367       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4909    0.4050    0.4438       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6024    0.6019    0.5996      1600\n",
      "weighted avg     0.6024    0.6019    0.5996      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 60.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4744    0.5550    0.5115       200\n",
      "           1     0.5880    0.6850    0.6328       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5574    0.5100    0.5326       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4787    0.4500    0.4639       200\n",
      "\n",
      "    accuracy                         0.6025      1600\n",
      "   macro avg     0.6041    0.6025    0.6009      1600\n",
      "weighted avg     0.6041    0.6025    0.6009      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 60.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4766    0.5600    0.5149       200\n",
      "           1     0.5837    0.6800    0.6282       200\n",
      "           2     0.5267    0.3950    0.4514       200\n",
      "           3     0.5550    0.5300    0.5422       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4888    0.4350    0.4603       200\n",
      "\n",
      "    accuracy                         0.6038      1600\n",
      "   macro avg     0.6049    0.6038    0.6019      1600\n",
      "weighted avg     0.6049    0.6038    0.6019      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 60.12%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4726    0.5600    0.5126       200\n",
      "           1     0.5830    0.6850    0.6299       200\n",
      "           2     0.5128    0.4000    0.4494       200\n",
      "           3     0.5459    0.5350    0.5404       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4583    0.4950    0.4760       200\n",
      "           7     0.4939    0.4050    0.4451       200\n",
      "\n",
      "    accuracy                         0.6012      1600\n",
      "   macro avg     0.6018    0.6013    0.5990      1600\n",
      "weighted avg     0.6018    0.6012    0.5990      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4746    0.5600    0.5138       200\n",
      "           1     0.5870    0.6750    0.6279       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5731    0.4900    0.5283       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4583    0.4950    0.4760       200\n",
      "           7     0.4653    0.4700    0.4677       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6045    0.6019    0.6006      1600\n",
      "weighted avg     0.6045    0.6019    0.6006      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 60.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4790    0.5700    0.5205       200\n",
      "           1     0.5763    0.6800    0.6239       200\n",
      "           2     0.5270    0.3900    0.4483       200\n",
      "           3     0.5585    0.5250    0.5412       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4859    0.4300    0.4562       200\n",
      "\n",
      "    accuracy                         0.6025      1600\n",
      "   macro avg     0.6038    0.6025    0.6004      1600\n",
      "weighted avg     0.6038    0.6025    0.6004      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 60.12%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4726    0.5600    0.5126       200\n",
      "           1     0.5855    0.6850    0.6313       200\n",
      "           2     0.5161    0.4000    0.4507       200\n",
      "           3     0.5436    0.5300    0.5367       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4880    0.4050    0.4426       200\n",
      "\n",
      "    accuracy                         0.6012      1600\n",
      "   macro avg     0.6018    0.6013    0.5990      1600\n",
      "weighted avg     0.6018    0.6012    0.5990      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 60.12%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4744    0.5550    0.5115       200\n",
      "           1     0.5756    0.6850    0.6256       200\n",
      "           2     0.5374    0.3950    0.4553       200\n",
      "           3     0.5607    0.4850    0.5201       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4583    0.4950    0.4760       200\n",
      "           7     0.4745    0.4650    0.4697       200\n",
      "\n",
      "    accuracy                         0.6012      1600\n",
      "   macro avg     0.6036    0.6013    0.5996      1600\n",
      "weighted avg     0.6036    0.6012    0.5996      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4723    0.5550    0.5103       200\n",
      "           1     0.5812    0.6800    0.6267       200\n",
      "           2     0.5338    0.3950    0.4540       200\n",
      "           3     0.5580    0.5050    0.5302       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4583    0.4950    0.4760       200\n",
      "           7     0.4789    0.4550    0.4667       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6038    0.6019    0.6003      1600\n",
      "weighted avg     0.6038    0.6019    0.6003      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 60.12%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4726    0.5600    0.5126       200\n",
      "           1     0.5855    0.6850    0.6313       200\n",
      "           2     0.5161    0.4000    0.4507       200\n",
      "           3     0.5436    0.5300    0.5367       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4880    0.4050    0.4426       200\n",
      "\n",
      "    accuracy                         0.6012      1600\n",
      "   macro avg     0.6018    0.6013    0.5990      1600\n",
      "weighted avg     0.6018    0.6012    0.5990      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4744    0.5550    0.5115       200\n",
      "           1     0.5862    0.6800    0.6296       200\n",
      "           2     0.5267    0.3950    0.4514       200\n",
      "           3     0.5574    0.5100    0.5326       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4787    0.4500    0.4639       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6034    0.6019    0.6003      1600\n",
      "weighted avg     0.6034    0.6019    0.6003      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 60.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4766    0.5600    0.5149       200\n",
      "           1     0.5837    0.6800    0.6282       200\n",
      "           2     0.5267    0.3950    0.4514       200\n",
      "           3     0.5550    0.5300    0.5422       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4888    0.4350    0.4603       200\n",
      "\n",
      "    accuracy                         0.6038      1600\n",
      "   macro avg     0.6049    0.6038    0.6019      1600\n",
      "weighted avg     0.6049    0.6038    0.6019      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 60.12%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4726    0.5600    0.5126       200\n",
      "           1     0.5830    0.6850    0.6299       200\n",
      "           2     0.5128    0.4000    0.4494       200\n",
      "           3     0.5459    0.5350    0.5404       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4583    0.4950    0.4760       200\n",
      "           7     0.4939    0.4050    0.4451       200\n",
      "\n",
      "    accuracy                         0.6012      1600\n",
      "   macro avg     0.6018    0.6013    0.5990      1600\n",
      "weighted avg     0.6018    0.6012    0.5990      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4346    0.6150    0.5093       200\n",
      "           1     0.5560    0.6950    0.6178       200\n",
      "           2     0.5854    0.2400    0.3404       200\n",
      "           3     0.5286    0.5550    0.5415       200\n",
      "           4     0.8762    0.8850    0.8806       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4480    0.4950    0.4703       200\n",
      "           7     0.5253    0.4150    0.4637       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.6032    0.5931    0.5852      1600\n",
      "weighted avg     0.6032    0.5931    0.5852      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4346    0.6150    0.5093       200\n",
      "           1     0.5582    0.6950    0.6192       200\n",
      "           2     0.6000    0.2400    0.3429       200\n",
      "           3     0.5166    0.5450    0.5304       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4434    0.4900    0.4656       200\n",
      "           7     0.5127    0.4050    0.4525       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.6005    0.5900    0.5817      1600\n",
      "weighted avg     0.6005    0.5900    0.5817      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4346    0.6150    0.5093       200\n",
      "           1     0.5534    0.7000    0.6181       200\n",
      "           2     0.6173    0.2500    0.3559       200\n",
      "           3     0.5261    0.5550    0.5401       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4525    0.5000    0.4751       200\n",
      "           7     0.5130    0.3950    0.4463       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.6050    0.5931    0.5851      1600\n",
      "weighted avg     0.6050    0.5931    0.5851      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4362    0.6150    0.5104       200\n",
      "           1     0.5556    0.7000    0.6195       200\n",
      "           2     0.5926    0.2400    0.3416       200\n",
      "           3     0.5369    0.5450    0.5409       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4500    0.4950    0.4714       200\n",
      "           7     0.5122    0.4200    0.4615       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.6028    0.5931    0.5849      1600\n",
      "weighted avg     0.6028    0.5931    0.5849      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4362    0.6150    0.5104       200\n",
      "           1     0.5600    0.7000    0.6222       200\n",
      "           2     0.6000    0.2400    0.3429       200\n",
      "           3     0.5166    0.5450    0.5304       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4434    0.4900    0.4656       200\n",
      "           7     0.5127    0.4050    0.4525       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.6010    0.5906    0.5823      1600\n",
      "weighted avg     0.6010    0.5906    0.5823      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4346    0.6150    0.5093       200\n",
      "           1     0.5534    0.7000    0.6181       200\n",
      "           2     0.6173    0.2500    0.3559       200\n",
      "           3     0.5261    0.5550    0.5401       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4525    0.5000    0.4751       200\n",
      "           7     0.5130    0.3950    0.4463       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.6050    0.5931    0.5851      1600\n",
      "weighted avg     0.6050    0.5931    0.5851      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4362    0.6150    0.5104       200\n",
      "           1     0.5534    0.7000    0.6181       200\n",
      "           2     0.5926    0.2400    0.3416       200\n",
      "           3     0.5308    0.5600    0.5450       200\n",
      "           4     0.8762    0.8850    0.8806       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4500    0.4950    0.4714       200\n",
      "           7     0.5159    0.4050    0.4538       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.6033    0.5931    0.5848      1600\n",
      "weighted avg     0.6033    0.5931    0.5848      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4362    0.6150    0.5104       200\n",
      "           1     0.5556    0.7000    0.6195       200\n",
      "           2     0.6000    0.2400    0.3429       200\n",
      "           3     0.5166    0.5450    0.5304       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4455    0.4900    0.4667       200\n",
      "           7     0.5127    0.4050    0.4525       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.6012    0.5906    0.5823      1600\n",
      "weighted avg     0.6012    0.5906    0.5823      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4331    0.6150    0.5083       200\n",
      "           1     0.5556    0.7000    0.6195       200\n",
      "           2     0.6098    0.2500    0.3546       200\n",
      "           3     0.5311    0.5550    0.5428       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4525    0.5000    0.4751       200\n",
      "           7     0.5161    0.4000    0.4507       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.6051    0.5938    0.5859      1600\n",
      "weighted avg     0.6051    0.5938    0.5859      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 60.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4723    0.5550    0.5103       200\n",
      "           1     0.5895    0.6750    0.6294       200\n",
      "           2     0.5232    0.3950    0.4501       200\n",
      "           3     0.5740    0.4850    0.5257       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4581    0.4650    0.4615       200\n",
      "\n",
      "    accuracy                         0.6000      1600\n",
      "   macro avg     0.6026    0.6000    0.5988      1600\n",
      "weighted avg     0.6026    0.6000    0.5988      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 60.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4723    0.5550    0.5103       200\n",
      "           1     0.5787    0.6800    0.6253       200\n",
      "           2     0.5203    0.3850    0.4425       200\n",
      "           3     0.5585    0.5250    0.5412       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4807    0.4350    0.4567       200\n",
      "\n",
      "    accuracy                         0.6006      1600\n",
      "   macro avg     0.6018    0.6006    0.5987      1600\n",
      "weighted avg     0.6018    0.6006    0.5987      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5855    0.6850    0.6313       200\n",
      "           2     0.5161    0.4000    0.4507       200\n",
      "           3     0.5436    0.5300    0.5367       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4909    0.4050    0.4438       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6024    0.6019    0.5996      1600\n",
      "weighted avg     0.6024    0.6019    0.5996      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4744    0.5550    0.5115       200\n",
      "           1     0.5756    0.6850    0.6256       200\n",
      "           2     0.5374    0.3950    0.4553       200\n",
      "           3     0.5632    0.4900    0.5241       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4742    0.4600    0.4670       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6042    0.6019    0.6002      1600\n",
      "weighted avg     0.6042    0.6019    0.6002      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4723    0.5550    0.5103       200\n",
      "           1     0.5787    0.6800    0.6253       200\n",
      "           2     0.5338    0.3950    0.4540       200\n",
      "           3     0.5604    0.5100    0.5340       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4558    0.4900    0.4723       200\n",
      "           7     0.4815    0.4550    0.4679       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6038    0.6019    0.6003      1600\n",
      "weighted avg     0.6038    0.6019    0.6003      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5855    0.6850    0.6313       200\n",
      "           2     0.5161    0.4000    0.4507       200\n",
      "           3     0.5436    0.5300    0.5367       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4909    0.4050    0.4438       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6024    0.6019    0.5996      1600\n",
      "weighted avg     0.6024    0.6019    0.5996      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 60.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4744    0.5550    0.5115       200\n",
      "           1     0.5880    0.6850    0.6328       200\n",
      "           2     0.5302    0.3950    0.4527       200\n",
      "           3     0.5574    0.5100    0.5326       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4787    0.4500    0.4639       200\n",
      "\n",
      "    accuracy                         0.6025      1600\n",
      "   macro avg     0.6041    0.6025    0.6009      1600\n",
      "weighted avg     0.6041    0.6025    0.6009      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 60.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4766    0.5600    0.5149       200\n",
      "           1     0.5837    0.6800    0.6282       200\n",
      "           2     0.5267    0.3950    0.4514       200\n",
      "           3     0.5550    0.5300    0.5422       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4888    0.4350    0.4603       200\n",
      "\n",
      "    accuracy                         0.6038      1600\n",
      "   macro avg     0.6049    0.6038    0.6019      1600\n",
      "weighted avg     0.6049    0.6038    0.6019      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=150, max_features=None, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 60.12%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4726    0.5600    0.5126       200\n",
      "           1     0.5830    0.6850    0.6299       200\n",
      "           2     0.5128    0.4000    0.4494       200\n",
      "           3     0.5459    0.5350    0.5404       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4583    0.4950    0.4760       200\n",
      "           7     0.4939    0.4050    0.4451       200\n",
      "\n",
      "    accuracy                         0.6012      1600\n",
      "   macro avg     0.6018    0.6013    0.5990      1600\n",
      "weighted avg     0.6018    0.6012    0.5990      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5600    0.4859       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5170    0.3800    0.4380       200\n",
      "           3     0.5532    0.5200    0.5361       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8639    0.8250    0.8440       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4603    0.4350    0.4473       200\n",
      "\n",
      "    accuracy                         0.5887      1600\n",
      "   macro avg     0.5911    0.5887    0.5870      1600\n",
      "weighted avg     0.5911    0.5887    0.5870      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5550    0.4847       200\n",
      "           1     0.5708    0.6450    0.6056       200\n",
      "           2     0.5232    0.3950    0.4501       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8473    0.8600    0.8536       200\n",
      "           5     0.8462    0.8250    0.8354       200\n",
      "           6     0.4792    0.4600    0.4694       200\n",
      "           7     0.4497    0.4250    0.4370       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5869    0.5844    0.5831      1600\n",
      "weighted avg     0.5869    0.5844    0.5831      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4247    0.5500    0.4793       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5135    0.3800    0.4368       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8632    0.8200    0.8410       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4468    0.4200    0.4330       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5888    0.5869    0.5849      1600\n",
      "weighted avg     0.5888    0.5869    0.5849      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4336    0.5550    0.4868       200\n",
      "           1     0.5683    0.6450    0.6042       200\n",
      "           2     0.5133    0.3850    0.4400       200\n",
      "           3     0.5459    0.5050    0.5247       200\n",
      "           4     0.8495    0.8750    0.8621       200\n",
      "           5     0.8418    0.8250    0.8333       200\n",
      "           6     0.4819    0.4650    0.4733       200\n",
      "           7     0.4492    0.4200    0.4341       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5854    0.5844    0.5823      1600\n",
      "weighted avg     0.5854    0.5844    0.5823      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5450    0.4812       200\n",
      "           1     0.5658    0.6450    0.6028       200\n",
      "           2     0.5098    0.3900    0.4419       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8549    0.8250    0.8397       200\n",
      "           6     0.4844    0.4650    0.4745       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5875    0.5862    0.5844      1600\n",
      "weighted avg     0.5875    0.5863    0.5844      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4247    0.5500    0.4793       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5135    0.3800    0.4368       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8632    0.8200    0.8410       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4468    0.4200    0.4330       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5888    0.5869    0.5849      1600\n",
      "weighted avg     0.5888    0.5869    0.5849      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4368    0.5700    0.4946       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5034    0.3750    0.4298       200\n",
      "           3     0.5525    0.5000    0.5249       200\n",
      "           4     0.8454    0.8750    0.8600       200\n",
      "           5     0.8497    0.8200    0.8346       200\n",
      "           6     0.4817    0.4600    0.4706       200\n",
      "           7     0.4444    0.4200    0.4319       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5857    0.5844    0.5821      1600\n",
      "weighted avg     0.5857    0.5844    0.5821      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5500    0.4803       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5033    0.3800    0.4330       200\n",
      "           3     0.5470    0.4950    0.5197       200\n",
      "           4     0.8469    0.8850    0.8655       200\n",
      "           5     0.8586    0.8200    0.8389       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4404    0.4250    0.4326       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5849    0.5831    0.5813      1600\n",
      "weighted avg     0.5849    0.5831    0.5813      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5600    0.4859       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5102    0.3750    0.4323       200\n",
      "           3     0.5489    0.5050    0.5260       200\n",
      "           4     0.8469    0.8850    0.8655       200\n",
      "           5     0.8677    0.8200    0.8432       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4479    0.4300    0.4388       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5892    0.5869    0.5850      1600\n",
      "weighted avg     0.5892    0.5869    0.5850      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5600    0.4848       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5278    0.3800    0.4419       200\n",
      "           3     0.5550    0.5300    0.5422       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8639    0.8250    0.8440       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4624    0.4300    0.4456       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5926    0.5900    0.5881      1600\n",
      "weighted avg     0.5926    0.5900    0.5881      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4286    0.5550    0.4837       200\n",
      "           1     0.5683    0.6450    0.6042       200\n",
      "           2     0.5267    0.3950    0.4514       200\n",
      "           3     0.5514    0.5100    0.5299       200\n",
      "           4     0.8473    0.8600    0.8536       200\n",
      "           5     0.8462    0.8250    0.8354       200\n",
      "           6     0.4819    0.4650    0.4733       200\n",
      "           7     0.4521    0.4250    0.4381       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5878    0.5850    0.5837      1600\n",
      "weighted avg     0.5878    0.5850    0.5837      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4269    0.5550    0.4826       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5102    0.3750    0.4323       200\n",
      "           3     0.5459    0.5050    0.5247       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8723    0.8200    0.8454       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5899    0.5875    0.5857      1600\n",
      "weighted avg     0.5899    0.5875    0.5857      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4314    0.5500    0.4835       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5133    0.3850    0.4400       200\n",
      "           3     0.5514    0.5100    0.5299       200\n",
      "           4     0.8495    0.8750    0.8621       200\n",
      "           5     0.8418    0.8250    0.8333       200\n",
      "           6     0.4792    0.4600    0.4694       200\n",
      "           7     0.4492    0.4200    0.4341       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5854    0.5844    0.5823      1600\n",
      "weighted avg     0.5854    0.5844    0.5823      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5450    0.4802       200\n",
      "           1     0.5658    0.6450    0.6028       200\n",
      "           2     0.5000    0.3850    0.4350       200\n",
      "           3     0.5543    0.5100    0.5312       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8549    0.8250    0.8397       200\n",
      "           6     0.4844    0.4650    0.4745       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5868    0.5856    0.5838      1600\n",
      "weighted avg     0.5868    0.5856    0.5838      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4269    0.5550    0.4826       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5102    0.3750    0.4323       200\n",
      "           3     0.5459    0.5050    0.5247       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8723    0.8200    0.8454       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5899    0.5875    0.5857      1600\n",
      "weighted avg     0.5899    0.5875    0.5857      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4346    0.5650    0.4913       200\n",
      "           1     0.5746    0.6550    0.6121       200\n",
      "           2     0.5000    0.3800    0.4318       200\n",
      "           3     0.5525    0.5000    0.5249       200\n",
      "           4     0.8454    0.8750    0.8600       200\n",
      "           5     0.8497    0.8200    0.8346       200\n",
      "           6     0.4817    0.4600    0.4706       200\n",
      "           7     0.4468    0.4200    0.4330       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5857    0.5844    0.5823      1600\n",
      "weighted avg     0.5857    0.5844    0.5823      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5500    0.4803       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5033    0.3800    0.4330       200\n",
      "           3     0.5470    0.4950    0.5197       200\n",
      "           4     0.8469    0.8850    0.8655       200\n",
      "           5     0.8586    0.8200    0.8389       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4404    0.4250    0.4326       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5849    0.5831    0.5813      1600\n",
      "weighted avg     0.5849    0.5831    0.5813      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5600    0.4859       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5172    0.3750    0.4348       200\n",
      "           3     0.5401    0.5050    0.5220       200\n",
      "           4     0.8469    0.8850    0.8655       200\n",
      "           5     0.8677    0.8200    0.8432       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5893    0.5869    0.5850      1600\n",
      "weighted avg     0.5893    0.5869    0.5850      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4358    0.5600    0.4902       200\n",
      "           1     0.5683    0.6450    0.6042       200\n",
      "           2     0.5033    0.3800    0.4330       200\n",
      "           3     0.5568    0.5150    0.5351       200\n",
      "           4     0.8657    0.8700    0.8678       200\n",
      "           5     0.8579    0.8450    0.8514       200\n",
      "           6     0.4789    0.4550    0.4667       200\n",
      "           7     0.4427    0.4250    0.4337       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5887    0.5869    0.5853      1600\n",
      "weighted avg     0.5887    0.5869    0.5853      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4365    0.5500    0.4867       200\n",
      "           1     0.5683    0.6450    0.6042       200\n",
      "           2     0.5000    0.3850    0.4350       200\n",
      "           3     0.5608    0.5300    0.5450       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8549    0.8250    0.8397       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4492    0.4200    0.4341       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5881    0.5875    0.5855      1600\n",
      "weighted avg     0.5881    0.5875    0.5855      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4336    0.5550    0.4868       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5168    0.3850    0.4413       200\n",
      "           3     0.5515    0.5350    0.5431       200\n",
      "           4     0.8517    0.8900    0.8704       200\n",
      "           5     0.8684    0.8250    0.8462       200\n",
      "           6     0.4840    0.4550    0.4691       200\n",
      "           7     0.4595    0.4250    0.4416       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5922    0.5906    0.5886      1600\n",
      "weighted avg     0.5922    0.5906    0.5886      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4319    0.5550    0.4858       200\n",
      "           1     0.5785    0.6450    0.6099       200\n",
      "           2     0.5032    0.3900    0.4394       200\n",
      "           3     0.5503    0.5200    0.5347       200\n",
      "           4     0.8502    0.8800    0.8649       200\n",
      "           5     0.8639    0.8250    0.8440       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4497    0.4250    0.4370       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5887    0.5869    0.5854      1600\n",
      "weighted avg     0.5887    0.5869    0.5854      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4319    0.5550    0.4858       200\n",
      "           1     0.5785    0.6450    0.6099       200\n",
      "           2     0.5065    0.3900    0.4407       200\n",
      "           3     0.5421    0.5150    0.5282       200\n",
      "           4     0.8454    0.8750    0.8600       200\n",
      "           5     0.8497    0.8200    0.8346       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4439    0.4150    0.4289       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5849    0.5837    0.5820      1600\n",
      "weighted avg     0.5849    0.5837    0.5820      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4336    0.5550    0.4868       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5168    0.3850    0.4413       200\n",
      "           3     0.5515    0.5350    0.5431       200\n",
      "           4     0.8517    0.8900    0.8704       200\n",
      "           5     0.8684    0.8250    0.8462       200\n",
      "           6     0.4840    0.4550    0.4691       200\n",
      "           7     0.4595    0.4250    0.4416       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5922    0.5906    0.5886      1600\n",
      "weighted avg     0.5922    0.5906    0.5886      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5671    0.6550    0.6079       200\n",
      "           2     0.5101    0.3800    0.4355       200\n",
      "           3     0.5622    0.5200    0.5403       200\n",
      "           4     0.8565    0.8950    0.8753       200\n",
      "           5     0.8783    0.8300    0.8535       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4439    0.4150    0.4289       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5916    0.5894    0.5875      1600\n",
      "weighted avg     0.5916    0.5894    0.5875      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4286    0.5550    0.4837       200\n",
      "           1     0.5746    0.6550    0.6121       200\n",
      "           2     0.5101    0.3800    0.4355       200\n",
      "           3     0.5543    0.5100    0.5312       200\n",
      "           4     0.8531    0.9000    0.8759       200\n",
      "           5     0.8824    0.8250    0.8527       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4427    0.4250    0.4337       200\n",
      "\n",
      "    accuracy                         0.5887      1600\n",
      "   macro avg     0.5912    0.5888    0.5871      1600\n",
      "weighted avg     0.5912    0.5887    0.5871      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4353    0.5550    0.4879       200\n",
      "           1     0.5683    0.6450    0.6042       200\n",
      "           2     0.5000    0.3900    0.4382       200\n",
      "           3     0.5526    0.5250    0.5385       200\n",
      "           4     0.8498    0.9050    0.8765       200\n",
      "           5     0.8865    0.8200    0.8519       200\n",
      "           6     0.4840    0.4550    0.4691       200\n",
      "           7     0.4570    0.4250    0.4404       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5917    0.5900    0.5883      1600\n",
      "weighted avg     0.5917    0.5900    0.5883      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4265    0.5800    0.4915       200\n",
      "           1     0.5801    0.6700    0.6218       200\n",
      "           2     0.5484    0.3400    0.4198       200\n",
      "           3     0.5417    0.5200    0.5306       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4697    0.4650    0.4673       200\n",
      "           7     0.4565    0.4200    0.4375       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5934    0.5894    0.5863      1600\n",
      "weighted avg     0.5934    0.5894    0.5863      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5750    0.4904       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5500    0.3300    0.4125       200\n",
      "           3     0.5450    0.5450    0.5450       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4691    0.4550    0.4619       200\n",
      "           7     0.4590    0.4200    0.4386       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5937    0.5900    0.5864      1600\n",
      "weighted avg     0.5937    0.5900    0.5864      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5750    0.4904       200\n",
      "           1     0.5720    0.6750    0.6193       200\n",
      "           2     0.5546    0.3300    0.4138       200\n",
      "           3     0.5396    0.5450    0.5423       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4712    0.4500    0.4604       200\n",
      "           7     0.4565    0.4200    0.4375       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5933    0.5894    0.5857      1600\n",
      "weighted avg     0.5933    0.5894    0.5857      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4259    0.5750    0.4894       200\n",
      "           1     0.5801    0.6700    0.6218       200\n",
      "           2     0.5440    0.3400    0.4185       200\n",
      "           3     0.5427    0.5400    0.5414       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4718    0.4600    0.4658       200\n",
      "           7     0.4696    0.4250    0.4462       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5948    0.5913    0.5881      1600\n",
      "weighted avg     0.5948    0.5913    0.5881      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4259    0.5750    0.4894       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5500    0.3300    0.4125       200\n",
      "           3     0.5427    0.5400    0.5414       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4691    0.4550    0.4619       200\n",
      "           7     0.4590    0.4200    0.4386       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5932    0.5894    0.5858      1600\n",
      "weighted avg     0.5932    0.5894    0.5858      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5750    0.4904       200\n",
      "           1     0.5720    0.6750    0.6193       200\n",
      "           2     0.5546    0.3300    0.4138       200\n",
      "           3     0.5396    0.5450    0.5423       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4712    0.4500    0.4604       200\n",
      "           7     0.4565    0.4200    0.4375       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5933    0.5894    0.5857      1600\n",
      "weighted avg     0.5933    0.5894    0.5857      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4238    0.5700    0.4861       200\n",
      "           1     0.5751    0.6700    0.6189       200\n",
      "           2     0.5447    0.3350    0.4149       200\n",
      "           3     0.5477    0.5450    0.5464       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4718    0.4600    0.4658       200\n",
      "           7     0.4670    0.4250    0.4450       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5943    0.5906    0.5874      1600\n",
      "weighted avg     0.5943    0.5906    0.5874      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4254    0.5700    0.4872       200\n",
      "           1     0.5720    0.6750    0.6193       200\n",
      "           2     0.5500    0.3300    0.4125       200\n",
      "           3     0.5450    0.5450    0.5450       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4691    0.4550    0.4619       200\n",
      "           7     0.4590    0.4200    0.4386       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5931    0.5894    0.5858      1600\n",
      "weighted avg     0.5931    0.5894    0.5858      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5750    0.4904       200\n",
      "           1     0.5720    0.6750    0.6193       200\n",
      "           2     0.5546    0.3300    0.4138       200\n",
      "           3     0.5423    0.5450    0.5436       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4712    0.4500    0.4604       200\n",
      "           7     0.4595    0.4250    0.4416       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5940    0.5900    0.5864      1600\n",
      "weighted avg     0.5940    0.5900    0.5864      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5600    0.4859       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5170    0.3800    0.4380       200\n",
      "           3     0.5532    0.5200    0.5361       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8639    0.8250    0.8440       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4603    0.4350    0.4473       200\n",
      "\n",
      "    accuracy                         0.5887      1600\n",
      "   macro avg     0.5911    0.5887    0.5870      1600\n",
      "weighted avg     0.5911    0.5887    0.5870      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5550    0.4847       200\n",
      "           1     0.5708    0.6450    0.6056       200\n",
      "           2     0.5232    0.3950    0.4501       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8473    0.8600    0.8536       200\n",
      "           5     0.8462    0.8250    0.8354       200\n",
      "           6     0.4792    0.4600    0.4694       200\n",
      "           7     0.4497    0.4250    0.4370       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5869    0.5844    0.5831      1600\n",
      "weighted avg     0.5869    0.5844    0.5831      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4247    0.5500    0.4793       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5135    0.3800    0.4368       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8632    0.8200    0.8410       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4468    0.4200    0.4330       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5888    0.5869    0.5849      1600\n",
      "weighted avg     0.5888    0.5869    0.5849      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4336    0.5550    0.4868       200\n",
      "           1     0.5683    0.6450    0.6042       200\n",
      "           2     0.5133    0.3850    0.4400       200\n",
      "           3     0.5459    0.5050    0.5247       200\n",
      "           4     0.8495    0.8750    0.8621       200\n",
      "           5     0.8418    0.8250    0.8333       200\n",
      "           6     0.4819    0.4650    0.4733       200\n",
      "           7     0.4492    0.4200    0.4341       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5854    0.5844    0.5823      1600\n",
      "weighted avg     0.5854    0.5844    0.5823      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5450    0.4812       200\n",
      "           1     0.5658    0.6450    0.6028       200\n",
      "           2     0.5098    0.3900    0.4419       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8549    0.8250    0.8397       200\n",
      "           6     0.4844    0.4650    0.4745       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5875    0.5862    0.5844      1600\n",
      "weighted avg     0.5875    0.5863    0.5844      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4247    0.5500    0.4793       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5135    0.3800    0.4368       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8632    0.8200    0.8410       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4468    0.4200    0.4330       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5888    0.5869    0.5849      1600\n",
      "weighted avg     0.5888    0.5869    0.5849      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4368    0.5700    0.4946       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5034    0.3750    0.4298       200\n",
      "           3     0.5525    0.5000    0.5249       200\n",
      "           4     0.8454    0.8750    0.8600       200\n",
      "           5     0.8497    0.8200    0.8346       200\n",
      "           6     0.4817    0.4600    0.4706       200\n",
      "           7     0.4444    0.4200    0.4319       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5857    0.5844    0.5821      1600\n",
      "weighted avg     0.5857    0.5844    0.5821      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5500    0.4803       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5033    0.3800    0.4330       200\n",
      "           3     0.5470    0.4950    0.5197       200\n",
      "           4     0.8469    0.8850    0.8655       200\n",
      "           5     0.8586    0.8200    0.8389       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4404    0.4250    0.4326       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5849    0.5831    0.5813      1600\n",
      "weighted avg     0.5849    0.5831    0.5813      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=sqrt, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5600    0.4859       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5102    0.3750    0.4323       200\n",
      "           3     0.5489    0.5050    0.5260       200\n",
      "           4     0.8469    0.8850    0.8655       200\n",
      "           5     0.8677    0.8200    0.8432       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4479    0.4300    0.4388       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5892    0.5869    0.5850      1600\n",
      "weighted avg     0.5892    0.5869    0.5850      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5600    0.4859       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5170    0.3800    0.4380       200\n",
      "           3     0.5532    0.5200    0.5361       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8639    0.8250    0.8440       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4603    0.4350    0.4473       200\n",
      "\n",
      "    accuracy                         0.5887      1600\n",
      "   macro avg     0.5911    0.5887    0.5870      1600\n",
      "weighted avg     0.5911    0.5887    0.5870      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5550    0.4847       200\n",
      "           1     0.5708    0.6450    0.6056       200\n",
      "           2     0.5232    0.3950    0.4501       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8473    0.8600    0.8536       200\n",
      "           5     0.8462    0.8250    0.8354       200\n",
      "           6     0.4792    0.4600    0.4694       200\n",
      "           7     0.4497    0.4250    0.4370       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5869    0.5844    0.5831      1600\n",
      "weighted avg     0.5869    0.5844    0.5831      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4247    0.5500    0.4793       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5135    0.3800    0.4368       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8632    0.8200    0.8410       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4468    0.4200    0.4330       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5888    0.5869    0.5849      1600\n",
      "weighted avg     0.5888    0.5869    0.5849      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4336    0.5550    0.4868       200\n",
      "           1     0.5683    0.6450    0.6042       200\n",
      "           2     0.5133    0.3850    0.4400       200\n",
      "           3     0.5459    0.5050    0.5247       200\n",
      "           4     0.8495    0.8750    0.8621       200\n",
      "           5     0.8418    0.8250    0.8333       200\n",
      "           6     0.4819    0.4650    0.4733       200\n",
      "           7     0.4492    0.4200    0.4341       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5854    0.5844    0.5823      1600\n",
      "weighted avg     0.5854    0.5844    0.5823      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5450    0.4812       200\n",
      "           1     0.5658    0.6450    0.6028       200\n",
      "           2     0.5098    0.3900    0.4419       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8549    0.8250    0.8397       200\n",
      "           6     0.4844    0.4650    0.4745       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5875    0.5862    0.5844      1600\n",
      "weighted avg     0.5875    0.5863    0.5844      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4247    0.5500    0.4793       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5135    0.3800    0.4368       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8632    0.8200    0.8410       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4468    0.4200    0.4330       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5888    0.5869    0.5849      1600\n",
      "weighted avg     0.5888    0.5869    0.5849      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4368    0.5700    0.4946       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5034    0.3750    0.4298       200\n",
      "           3     0.5525    0.5000    0.5249       200\n",
      "           4     0.8454    0.8750    0.8600       200\n",
      "           5     0.8497    0.8200    0.8346       200\n",
      "           6     0.4817    0.4600    0.4706       200\n",
      "           7     0.4444    0.4200    0.4319       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5857    0.5844    0.5821      1600\n",
      "weighted avg     0.5857    0.5844    0.5821      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5500    0.4803       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5033    0.3800    0.4330       200\n",
      "           3     0.5470    0.4950    0.5197       200\n",
      "           4     0.8469    0.8850    0.8655       200\n",
      "           5     0.8586    0.8200    0.8389       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4404    0.4250    0.4326       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5849    0.5831    0.5813      1600\n",
      "weighted avg     0.5849    0.5831    0.5813      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5600    0.4859       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5102    0.3750    0.4323       200\n",
      "           3     0.5489    0.5050    0.5260       200\n",
      "           4     0.8469    0.8850    0.8655       200\n",
      "           5     0.8677    0.8200    0.8432       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4479    0.4300    0.4388       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5892    0.5869    0.5850      1600\n",
      "weighted avg     0.5892    0.5869    0.5850      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5600    0.4848       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5278    0.3800    0.4419       200\n",
      "           3     0.5550    0.5300    0.5422       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8639    0.8250    0.8440       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4624    0.4300    0.4456       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5926    0.5900    0.5881      1600\n",
      "weighted avg     0.5926    0.5900    0.5881      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4286    0.5550    0.4837       200\n",
      "           1     0.5683    0.6450    0.6042       200\n",
      "           2     0.5267    0.3950    0.4514       200\n",
      "           3     0.5514    0.5100    0.5299       200\n",
      "           4     0.8473    0.8600    0.8536       200\n",
      "           5     0.8462    0.8250    0.8354       200\n",
      "           6     0.4819    0.4650    0.4733       200\n",
      "           7     0.4521    0.4250    0.4381       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5878    0.5850    0.5837      1600\n",
      "weighted avg     0.5878    0.5850    0.5837      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4269    0.5550    0.4826       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5102    0.3750    0.4323       200\n",
      "           3     0.5459    0.5050    0.5247       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8723    0.8200    0.8454       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5899    0.5875    0.5857      1600\n",
      "weighted avg     0.5899    0.5875    0.5857      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4314    0.5500    0.4835       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5133    0.3850    0.4400       200\n",
      "           3     0.5514    0.5100    0.5299       200\n",
      "           4     0.8495    0.8750    0.8621       200\n",
      "           5     0.8418    0.8250    0.8333       200\n",
      "           6     0.4792    0.4600    0.4694       200\n",
      "           7     0.4492    0.4200    0.4341       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5854    0.5844    0.5823      1600\n",
      "weighted avg     0.5854    0.5844    0.5823      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5450    0.4802       200\n",
      "           1     0.5658    0.6450    0.6028       200\n",
      "           2     0.5000    0.3850    0.4350       200\n",
      "           3     0.5543    0.5100    0.5312       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8549    0.8250    0.8397       200\n",
      "           6     0.4844    0.4650    0.4745       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5856      1600\n",
      "   macro avg     0.5868    0.5856    0.5838      1600\n",
      "weighted avg     0.5868    0.5856    0.5838      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4269    0.5550    0.4826       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5102    0.3750    0.4323       200\n",
      "           3     0.5459    0.5050    0.5247       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8723    0.8200    0.8454       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5899    0.5875    0.5857      1600\n",
      "weighted avg     0.5899    0.5875    0.5857      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4346    0.5650    0.4913       200\n",
      "           1     0.5746    0.6550    0.6121       200\n",
      "           2     0.5000    0.3800    0.4318       200\n",
      "           3     0.5525    0.5000    0.5249       200\n",
      "           4     0.8454    0.8750    0.8600       200\n",
      "           5     0.8497    0.8200    0.8346       200\n",
      "           6     0.4817    0.4600    0.4706       200\n",
      "           7     0.4468    0.4200    0.4330       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5857    0.5844    0.5823      1600\n",
      "weighted avg     0.5857    0.5844    0.5823      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5500    0.4803       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5033    0.3800    0.4330       200\n",
      "           3     0.5470    0.4950    0.5197       200\n",
      "           4     0.8469    0.8850    0.8655       200\n",
      "           5     0.8586    0.8200    0.8389       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4404    0.4250    0.4326       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5849    0.5831    0.5813      1600\n",
      "weighted avg     0.5849    0.5831    0.5813      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5600    0.4859       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5172    0.3750    0.4348       200\n",
      "           3     0.5401    0.5050    0.5220       200\n",
      "           4     0.8469    0.8850    0.8655       200\n",
      "           5     0.8677    0.8200    0.8432       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4503    0.4300    0.4399       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5893    0.5869    0.5850      1600\n",
      "weighted avg     0.5893    0.5869    0.5850      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4358    0.5600    0.4902       200\n",
      "           1     0.5683    0.6450    0.6042       200\n",
      "           2     0.5033    0.3800    0.4330       200\n",
      "           3     0.5568    0.5150    0.5351       200\n",
      "           4     0.8657    0.8700    0.8678       200\n",
      "           5     0.8579    0.8450    0.8514       200\n",
      "           6     0.4789    0.4550    0.4667       200\n",
      "           7     0.4427    0.4250    0.4337       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5887    0.5869    0.5853      1600\n",
      "weighted avg     0.5887    0.5869    0.5853      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4365    0.5500    0.4867       200\n",
      "           1     0.5683    0.6450    0.6042       200\n",
      "           2     0.5000    0.3850    0.4350       200\n",
      "           3     0.5608    0.5300    0.5450       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8549    0.8250    0.8397       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4492    0.4200    0.4341       200\n",
      "\n",
      "    accuracy                         0.5875      1600\n",
      "   macro avg     0.5881    0.5875    0.5855      1600\n",
      "weighted avg     0.5881    0.5875    0.5855      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4336    0.5550    0.4868       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5168    0.3850    0.4413       200\n",
      "           3     0.5515    0.5350    0.5431       200\n",
      "           4     0.8517    0.8900    0.8704       200\n",
      "           5     0.8684    0.8250    0.8462       200\n",
      "           6     0.4840    0.4550    0.4691       200\n",
      "           7     0.4595    0.4250    0.4416       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5922    0.5906    0.5886      1600\n",
      "weighted avg     0.5922    0.5906    0.5886      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4319    0.5550    0.4858       200\n",
      "           1     0.5785    0.6450    0.6099       200\n",
      "           2     0.5032    0.3900    0.4394       200\n",
      "           3     0.5503    0.5200    0.5347       200\n",
      "           4     0.8502    0.8800    0.8649       200\n",
      "           5     0.8639    0.8250    0.8440       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4497    0.4250    0.4370       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5887    0.5869    0.5854      1600\n",
      "weighted avg     0.5887    0.5869    0.5854      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4319    0.5550    0.4858       200\n",
      "           1     0.5785    0.6450    0.6099       200\n",
      "           2     0.5065    0.3900    0.4407       200\n",
      "           3     0.5421    0.5150    0.5282       200\n",
      "           4     0.8454    0.8750    0.8600       200\n",
      "           5     0.8497    0.8200    0.8346       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4439    0.4150    0.4289       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5849    0.5837    0.5820      1600\n",
      "weighted avg     0.5849    0.5837    0.5820      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4336    0.5550    0.4868       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5168    0.3850    0.4413       200\n",
      "           3     0.5515    0.5350    0.5431       200\n",
      "           4     0.8517    0.8900    0.8704       200\n",
      "           5     0.8684    0.8250    0.8462       200\n",
      "           6     0.4840    0.4550    0.4691       200\n",
      "           7     0.4595    0.4250    0.4416       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5922    0.5906    0.5886      1600\n",
      "weighted avg     0.5922    0.5906    0.5886      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5600    0.4870       200\n",
      "           1     0.5671    0.6550    0.6079       200\n",
      "           2     0.5101    0.3800    0.4355       200\n",
      "           3     0.5622    0.5200    0.5403       200\n",
      "           4     0.8565    0.8950    0.8753       200\n",
      "           5     0.8783    0.8300    0.8535       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4439    0.4150    0.4289       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5916    0.5894    0.5875      1600\n",
      "weighted avg     0.5916    0.5894    0.5875      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4286    0.5550    0.4837       200\n",
      "           1     0.5746    0.6550    0.6121       200\n",
      "           2     0.5101    0.3800    0.4355       200\n",
      "           3     0.5543    0.5100    0.5312       200\n",
      "           4     0.8531    0.9000    0.8759       200\n",
      "           5     0.8824    0.8250    0.8527       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4427    0.4250    0.4337       200\n",
      "\n",
      "    accuracy                         0.5887      1600\n",
      "   macro avg     0.5912    0.5888    0.5871      1600\n",
      "weighted avg     0.5912    0.5887    0.5871      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4353    0.5550    0.4879       200\n",
      "           1     0.5683    0.6450    0.6042       200\n",
      "           2     0.5000    0.3900    0.4382       200\n",
      "           3     0.5526    0.5250    0.5385       200\n",
      "           4     0.8498    0.9050    0.8765       200\n",
      "           5     0.8865    0.8200    0.8519       200\n",
      "           6     0.4840    0.4550    0.4691       200\n",
      "           7     0.4570    0.4250    0.4404       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5917    0.5900    0.5883      1600\n",
      "weighted avg     0.5917    0.5900    0.5883      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4265    0.5800    0.4915       200\n",
      "           1     0.5801    0.6700    0.6218       200\n",
      "           2     0.5484    0.3400    0.4198       200\n",
      "           3     0.5417    0.5200    0.5306       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4697    0.4650    0.4673       200\n",
      "           7     0.4565    0.4200    0.4375       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5934    0.5894    0.5863      1600\n",
      "weighted avg     0.5934    0.5894    0.5863      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5750    0.4904       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5500    0.3300    0.4125       200\n",
      "           3     0.5450    0.5450    0.5450       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4691    0.4550    0.4619       200\n",
      "           7     0.4590    0.4200    0.4386       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5937    0.5900    0.5864      1600\n",
      "weighted avg     0.5937    0.5900    0.5864      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5750    0.4904       200\n",
      "           1     0.5720    0.6750    0.6193       200\n",
      "           2     0.5546    0.3300    0.4138       200\n",
      "           3     0.5396    0.5450    0.5423       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4712    0.4500    0.4604       200\n",
      "           7     0.4565    0.4200    0.4375       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5933    0.5894    0.5857      1600\n",
      "weighted avg     0.5933    0.5894    0.5857      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4259    0.5750    0.4894       200\n",
      "           1     0.5801    0.6700    0.6218       200\n",
      "           2     0.5440    0.3400    0.4185       200\n",
      "           3     0.5427    0.5400    0.5414       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4718    0.4600    0.4658       200\n",
      "           7     0.4696    0.4250    0.4462       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5948    0.5913    0.5881      1600\n",
      "weighted avg     0.5948    0.5913    0.5881      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4259    0.5750    0.4894       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5500    0.3300    0.4125       200\n",
      "           3     0.5427    0.5400    0.5414       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4691    0.4550    0.4619       200\n",
      "           7     0.4590    0.4200    0.4386       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5932    0.5894    0.5858      1600\n",
      "weighted avg     0.5932    0.5894    0.5858      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5750    0.4904       200\n",
      "           1     0.5720    0.6750    0.6193       200\n",
      "           2     0.5546    0.3300    0.4138       200\n",
      "           3     0.5396    0.5450    0.5423       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4712    0.4500    0.4604       200\n",
      "           7     0.4565    0.4200    0.4375       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5933    0.5894    0.5857      1600\n",
      "weighted avg     0.5933    0.5894    0.5857      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4238    0.5700    0.4861       200\n",
      "           1     0.5751    0.6700    0.6189       200\n",
      "           2     0.5447    0.3350    0.4149       200\n",
      "           3     0.5477    0.5450    0.5464       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4718    0.4600    0.4658       200\n",
      "           7     0.4670    0.4250    0.4450       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5943    0.5906    0.5874      1600\n",
      "weighted avg     0.5943    0.5906    0.5874      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4254    0.5700    0.4872       200\n",
      "           1     0.5720    0.6750    0.6193       200\n",
      "           2     0.5500    0.3300    0.4125       200\n",
      "           3     0.5450    0.5450    0.5450       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4691    0.4550    0.4619       200\n",
      "           7     0.4590    0.4200    0.4386       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5931    0.5894    0.5858      1600\n",
      "weighted avg     0.5931    0.5894    0.5858      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4275    0.5750    0.4904       200\n",
      "           1     0.5720    0.6750    0.6193       200\n",
      "           2     0.5546    0.3300    0.4138       200\n",
      "           3     0.5423    0.5450    0.5436       200\n",
      "           4     0.8592    0.8850    0.8719       200\n",
      "           5     0.8653    0.8350    0.8499       200\n",
      "           6     0.4712    0.4500    0.4604       200\n",
      "           7     0.4595    0.4250    0.4416       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5940    0.5900    0.5864      1600\n",
      "weighted avg     0.5940    0.5900    0.5864      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5600    0.4859       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5170    0.3800    0.4380       200\n",
      "           3     0.5532    0.5200    0.5361       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8639    0.8250    0.8440       200\n",
      "           6     0.4815    0.4550    0.4679       200\n",
      "           7     0.4603    0.4350    0.4473       200\n",
      "\n",
      "    accuracy                         0.5887      1600\n",
      "   macro avg     0.5911    0.5887    0.5870      1600\n",
      "weighted avg     0.5911    0.5887    0.5870      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5550    0.4847       200\n",
      "           1     0.5708    0.6450    0.6056       200\n",
      "           2     0.5232    0.3950    0.4501       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8473    0.8600    0.8536       200\n",
      "           5     0.8462    0.8250    0.8354       200\n",
      "           6     0.4792    0.4600    0.4694       200\n",
      "           7     0.4497    0.4250    0.4370       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5869    0.5844    0.5831      1600\n",
      "weighted avg     0.5869    0.5844    0.5831      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4247    0.5500    0.4793       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5135    0.3800    0.4368       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8632    0.8200    0.8410       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4468    0.4200    0.4330       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5888    0.5869    0.5849      1600\n",
      "weighted avg     0.5888    0.5869    0.5849      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4336    0.5550    0.4868       200\n",
      "           1     0.5683    0.6450    0.6042       200\n",
      "           2     0.5133    0.3850    0.4400       200\n",
      "           3     0.5459    0.5050    0.5247       200\n",
      "           4     0.8495    0.8750    0.8621       200\n",
      "           5     0.8418    0.8250    0.8333       200\n",
      "           6     0.4819    0.4650    0.4733       200\n",
      "           7     0.4492    0.4200    0.4341       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5854    0.5844    0.5823      1600\n",
      "weighted avg     0.5854    0.5844    0.5823      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.63%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4308    0.5450    0.4812       200\n",
      "           1     0.5658    0.6450    0.6028       200\n",
      "           2     0.5098    0.3900    0.4419       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8510    0.8850    0.8676       200\n",
      "           5     0.8549    0.8250    0.8397       200\n",
      "           6     0.4844    0.4650    0.4745       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5863      1600\n",
      "   macro avg     0.5875    0.5862    0.5844      1600\n",
      "weighted avg     0.5875    0.5863    0.5844      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4247    0.5500    0.4793       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5135    0.3800    0.4368       200\n",
      "           3     0.5484    0.5100    0.5285       200\n",
      "           4     0.8476    0.8900    0.8683       200\n",
      "           5     0.8632    0.8200    0.8410       200\n",
      "           6     0.4869    0.4650    0.4757       200\n",
      "           7     0.4468    0.4200    0.4330       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5888    0.5869    0.5849      1600\n",
      "weighted avg     0.5888    0.5869    0.5849      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4368    0.5700    0.4946       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5034    0.3750    0.4298       200\n",
      "           3     0.5525    0.5000    0.5249       200\n",
      "           4     0.8454    0.8750    0.8600       200\n",
      "           5     0.8497    0.8200    0.8346       200\n",
      "           6     0.4817    0.4600    0.4706       200\n",
      "           7     0.4444    0.4200    0.4319       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5857    0.5844    0.5821      1600\n",
      "weighted avg     0.5857    0.5844    0.5821      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5500    0.4803       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5033    0.3800    0.4330       200\n",
      "           3     0.5470    0.4950    0.5197       200\n",
      "           4     0.8469    0.8850    0.8655       200\n",
      "           5     0.8586    0.8200    0.8389       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4404    0.4250    0.4326       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5849    0.5831    0.5813      1600\n",
      "weighted avg     0.5849    0.5831    0.5813      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=log2, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4291    0.5600    0.4859       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5102    0.3750    0.4323       200\n",
      "           3     0.5489    0.5050    0.5260       200\n",
      "           4     0.8469    0.8850    0.8655       200\n",
      "           5     0.8677    0.8200    0.8432       200\n",
      "           6     0.4842    0.4600    0.4718       200\n",
      "           7     0.4479    0.4300    0.4388       200\n",
      "\n",
      "    accuracy                         0.5869      1600\n",
      "   macro avg     0.5892    0.5869    0.5850      1600\n",
      "weighted avg     0.5892    0.5869    0.5850      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4248    0.5650    0.4850       200\n",
      "           1     0.5689    0.6400    0.6024       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5532    0.5200    0.5361       200\n",
      "           4     0.8956    0.8150    0.8534       200\n",
      "           5     0.8233    0.8850    0.8530       200\n",
      "           6     0.4406    0.4450    0.4428       200\n",
      "           7     0.4595    0.4250    0.4416       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5855    0.5813    0.5794      1600\n",
      "weighted avg     0.5855    0.5813    0.5794      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4248    0.5650    0.4850       200\n",
      "           1     0.5785    0.6450    0.6099       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5426    0.5100    0.5258       200\n",
      "           4     0.8913    0.8200    0.8542       200\n",
      "           5     0.8302    0.8800    0.8544       200\n",
      "           6     0.4369    0.4500    0.4433       200\n",
      "           7     0.4492    0.4200    0.4341       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5854    0.5806    0.5790      1600\n",
      "weighted avg     0.5854    0.5806    0.5790      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4232    0.5650    0.4839       200\n",
      "           1     0.5818    0.6400    0.6095       200\n",
      "           2     0.5227    0.3450    0.4157       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8907    0.8150    0.8512       200\n",
      "           5     0.8224    0.8800    0.8502       200\n",
      "           6     0.4320    0.4450    0.4384       200\n",
      "           7     0.4486    0.4150    0.4312       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5832    0.5787    0.5768      1600\n",
      "weighted avg     0.5832    0.5787    0.5768      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4216    0.5650    0.4829       200\n",
      "           1     0.5766    0.6400    0.6066       200\n",
      "           2     0.5224    0.3500    0.4192       200\n",
      "           3     0.5591    0.5200    0.5389       200\n",
      "           4     0.8859    0.8150    0.8490       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4348    0.4500    0.4423       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5851    0.5800    0.5784      1600\n",
      "weighted avg     0.5851    0.5800    0.5784      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4248    0.5650    0.4850       200\n",
      "           1     0.5766    0.6400    0.6066       200\n",
      "           2     0.5263    0.3500    0.4204       200\n",
      "           3     0.5487    0.5350    0.5418       200\n",
      "           4     0.8859    0.8150    0.8490       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4320    0.4450    0.4384       200\n",
      "           7     0.4670    0.4250    0.4450       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5859    0.5813    0.5795      1600\n",
      "weighted avg     0.5859    0.5813    0.5795      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4232    0.5650    0.4839       200\n",
      "           1     0.5818    0.6400    0.6095       200\n",
      "           2     0.5227    0.3450    0.4157       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8907    0.8150    0.8512       200\n",
      "           5     0.8224    0.8800    0.8502       200\n",
      "           6     0.4320    0.4450    0.4384       200\n",
      "           7     0.4486    0.4150    0.4312       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5832    0.5787    0.5768      1600\n",
      "weighted avg     0.5832    0.5787    0.5768      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5650    0.4860       200\n",
      "           1     0.5792    0.6400    0.6081       200\n",
      "           2     0.5407    0.3650    0.4358       200\n",
      "           3     0.5632    0.5350    0.5487       200\n",
      "           4     0.8865    0.8200    0.8519       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4333    0.4550    0.4439       200\n",
      "           7     0.4536    0.4150    0.4334       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5890    0.5837    0.5824      1600\n",
      "weighted avg     0.5890    0.5837    0.5824      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4232    0.5650    0.4839       200\n",
      "           1     0.5792    0.6400    0.6081       200\n",
      "           2     0.5263    0.3500    0.4204       200\n",
      "           3     0.5469    0.5250    0.5357       200\n",
      "           4     0.8865    0.8200    0.8519       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4279    0.4450    0.4363       200\n",
      "           7     0.4536    0.4150    0.4334       200\n",
      "\n",
      "    accuracy                         0.5794      1600\n",
      "   macro avg     0.5841    0.5794    0.5777      1600\n",
      "weighted avg     0.5841    0.5794    0.5777      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4232    0.5650    0.4839       200\n",
      "           1     0.5818    0.6400    0.6095       200\n",
      "           2     0.5263    0.3500    0.4204       200\n",
      "           3     0.5436    0.5300    0.5367       200\n",
      "           4     0.9056    0.8150    0.8579       200\n",
      "           5     0.8287    0.8950    0.8606       200\n",
      "           6     0.4320    0.4450    0.4384       200\n",
      "           7     0.4536    0.4150    0.4334       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5868    0.5819    0.5801      1600\n",
      "weighted avg     0.5868    0.5819    0.5801      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4248    0.5650    0.4850       200\n",
      "           1     0.5689    0.6400    0.6024       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5561    0.5200    0.5375       200\n",
      "           4     0.8956    0.8150    0.8534       200\n",
      "           5     0.8233    0.8850    0.8530       200\n",
      "           6     0.4406    0.4450    0.4428       200\n",
      "           7     0.4624    0.4300    0.4456       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5862    0.5819    0.5801      1600\n",
      "weighted avg     0.5862    0.5819    0.5801      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4248    0.5650    0.4850       200\n",
      "           1     0.5785    0.6450    0.6099       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5426    0.5100    0.5258       200\n",
      "           4     0.8913    0.8200    0.8542       200\n",
      "           5     0.8302    0.8800    0.8544       200\n",
      "           6     0.4369    0.4500    0.4433       200\n",
      "           7     0.4492    0.4200    0.4341       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5854    0.5806    0.5790      1600\n",
      "weighted avg     0.5854    0.5806    0.5790      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4232    0.5650    0.4839       200\n",
      "           1     0.5818    0.6400    0.6095       200\n",
      "           2     0.5227    0.3450    0.4157       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8907    0.8150    0.8512       200\n",
      "           5     0.8224    0.8800    0.8502       200\n",
      "           6     0.4320    0.4450    0.4384       200\n",
      "           7     0.4486    0.4150    0.4312       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5832    0.5787    0.5768      1600\n",
      "weighted avg     0.5832    0.5787    0.5768      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4216    0.5650    0.4829       200\n",
      "           1     0.5766    0.6400    0.6066       200\n",
      "           2     0.5224    0.3500    0.4192       200\n",
      "           3     0.5591    0.5200    0.5389       200\n",
      "           4     0.8859    0.8150    0.8490       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4348    0.4500    0.4423       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5851    0.5800    0.5784      1600\n",
      "weighted avg     0.5851    0.5800    0.5784      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4248    0.5650    0.4850       200\n",
      "           1     0.5766    0.6400    0.6066       200\n",
      "           2     0.5263    0.3500    0.4204       200\n",
      "           3     0.5487    0.5350    0.5418       200\n",
      "           4     0.8859    0.8150    0.8490       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4320    0.4450    0.4384       200\n",
      "           7     0.4670    0.4250    0.4450       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5859    0.5813    0.5795      1600\n",
      "weighted avg     0.5859    0.5813    0.5795      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4232    0.5650    0.4839       200\n",
      "           1     0.5818    0.6400    0.6095       200\n",
      "           2     0.5227    0.3450    0.4157       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8907    0.8150    0.8512       200\n",
      "           5     0.8224    0.8800    0.8502       200\n",
      "           6     0.4320    0.4450    0.4384       200\n",
      "           7     0.4486    0.4150    0.4312       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5832    0.5787    0.5768      1600\n",
      "weighted avg     0.5832    0.5787    0.5768      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5650    0.4860       200\n",
      "           1     0.5792    0.6400    0.6081       200\n",
      "           2     0.5407    0.3650    0.4358       200\n",
      "           3     0.5632    0.5350    0.5487       200\n",
      "           4     0.8865    0.8200    0.8519       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4333    0.4550    0.4439       200\n",
      "           7     0.4536    0.4150    0.4334       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5890    0.5837    0.5824      1600\n",
      "weighted avg     0.5890    0.5837    0.5824      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4232    0.5650    0.4839       200\n",
      "           1     0.5792    0.6400    0.6081       200\n",
      "           2     0.5263    0.3500    0.4204       200\n",
      "           3     0.5469    0.5250    0.5357       200\n",
      "           4     0.8865    0.8200    0.8519       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4279    0.4450    0.4363       200\n",
      "           7     0.4536    0.4150    0.4334       200\n",
      "\n",
      "    accuracy                         0.5794      1600\n",
      "   macro avg     0.5841    0.5794    0.5777      1600\n",
      "weighted avg     0.5841    0.5794    0.5777      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4232    0.5650    0.4839       200\n",
      "           1     0.5818    0.6400    0.6095       200\n",
      "           2     0.5263    0.3500    0.4204       200\n",
      "           3     0.5436    0.5300    0.5367       200\n",
      "           4     0.9056    0.8150    0.8579       200\n",
      "           5     0.8287    0.8950    0.8606       200\n",
      "           6     0.4320    0.4450    0.4384       200\n",
      "           7     0.4536    0.4150    0.4334       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5868    0.5819    0.5801      1600\n",
      "weighted avg     0.5868    0.5819    0.5801      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4270    0.5700    0.4882       200\n",
      "           1     0.5818    0.6400    0.6095       200\n",
      "           2     0.5252    0.3650    0.4307       200\n",
      "           3     0.5515    0.5350    0.5431       200\n",
      "           4     0.8907    0.8150    0.8512       200\n",
      "           5     0.8263    0.8800    0.8523       200\n",
      "           6     0.4417    0.4550    0.4483       200\n",
      "           7     0.4607    0.4100    0.4339       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5881    0.5837    0.5821      1600\n",
      "weighted avg     0.5881    0.5837    0.5821      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4248    0.5650    0.4850       200\n",
      "           1     0.5747    0.6350    0.6033       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5521    0.5300    0.5408       200\n",
      "           4     0.8907    0.8150    0.8512       200\n",
      "           5     0.8263    0.8800    0.8523       200\n",
      "           6     0.4327    0.4500    0.4412       200\n",
      "           7     0.4556    0.4100    0.4316       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5844    0.5800    0.5783      1600\n",
      "weighted avg     0.5844    0.5800    0.5783      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4248    0.5650    0.4850       200\n",
      "           1     0.5818    0.6400    0.6095       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5350    0.5350    0.5350       200\n",
      "           4     0.9148    0.8050    0.8564       200\n",
      "           5     0.8227    0.9050    0.8619       200\n",
      "           6     0.4384    0.4450    0.4417       200\n",
      "           7     0.4637    0.4150    0.4380       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5879    0.5831    0.5813      1600\n",
      "weighted avg     0.5879    0.5831    0.5813      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4254    0.5700    0.4872       200\n",
      "           1     0.5792    0.6400    0.6081       200\n",
      "           2     0.5259    0.3550    0.4239       200\n",
      "           3     0.5445    0.5200    0.5320       200\n",
      "           4     0.8962    0.8200    0.8564       200\n",
      "           5     0.8310    0.8850    0.8571       200\n",
      "           6     0.4327    0.4500    0.4412       200\n",
      "           7     0.4530    0.4100    0.4304       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5860    0.5813    0.5795      1600\n",
      "weighted avg     0.5860    0.5813    0.5795      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4248    0.5650    0.4850       200\n",
      "           1     0.5792    0.6400    0.6081       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5510    0.5400    0.5455       200\n",
      "           4     0.9011    0.8200    0.8586       200\n",
      "           5     0.8318    0.8900    0.8599       200\n",
      "           6     0.4348    0.4500    0.4423       200\n",
      "           7     0.4667    0.4200    0.4421       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5899    0.5850    0.5833      1600\n",
      "weighted avg     0.5899    0.5850    0.5833      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4248    0.5650    0.4850       200\n",
      "           1     0.5818    0.6400    0.6095       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5350    0.5350    0.5350       200\n",
      "           4     0.9148    0.8050    0.8564       200\n",
      "           5     0.8227    0.9050    0.8619       200\n",
      "           6     0.4384    0.4450    0.4417       200\n",
      "           7     0.4637    0.4150    0.4380       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5879    0.5831    0.5813      1600\n",
      "weighted avg     0.5879    0.5831    0.5813      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4270    0.5700    0.4882       200\n",
      "           1     0.5845    0.6400    0.6110       200\n",
      "           2     0.5108    0.3550    0.4189       200\n",
      "           3     0.5455    0.5400    0.5427       200\n",
      "           4     0.9096    0.8050    0.8541       200\n",
      "           5     0.8219    0.9000    0.8592       200\n",
      "           6     0.4341    0.4450    0.4395       200\n",
      "           7     0.4602    0.4050    0.4309       200\n",
      "\n",
      "    accuracy                         0.5825      1600\n",
      "   macro avg     0.5867    0.5825    0.5806      1600\n",
      "weighted avg     0.5867    0.5825    0.5806      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4232    0.5650    0.4839       200\n",
      "           1     0.5792    0.6400    0.6081       200\n",
      "           2     0.5145    0.3550    0.4201       200\n",
      "           3     0.5533    0.5450    0.5491       200\n",
      "           4     0.9143    0.8000    0.8533       200\n",
      "           5     0.8190    0.9050    0.8599       200\n",
      "           6     0.4341    0.4450    0.4395       200\n",
      "           7     0.4602    0.4050    0.4309       200\n",
      "\n",
      "    accuracy                         0.5825      1600\n",
      "   macro avg     0.5872    0.5825    0.5806      1600\n",
      "weighted avg     0.5872    0.5825    0.5806      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4254    0.5700    0.4872       200\n",
      "           1     0.5792    0.6400    0.6081       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5327    0.5300    0.5313       200\n",
      "           4     0.9148    0.8050    0.8564       200\n",
      "           5     0.8227    0.9050    0.8619       200\n",
      "           6     0.4384    0.4450    0.4417       200\n",
      "           7     0.4689    0.4150    0.4403       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5880    0.5831    0.5812      1600\n",
      "weighted avg     0.5880    0.5831    0.5812      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 57.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4234    0.5800    0.4895       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5652    0.3250    0.4127       200\n",
      "           3     0.5130    0.4950    0.5038       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8246    0.8700    0.8467       200\n",
      "           6     0.4300    0.4450    0.4373       200\n",
      "           7     0.4409    0.4100    0.4249       200\n",
      "\n",
      "    accuracy                         0.5756      1600\n",
      "   macro avg     0.5822    0.5756    0.5727      1600\n",
      "weighted avg     0.5822    0.5756    0.5727      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 57.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4234    0.5800    0.4895       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5596    0.3050    0.3948       200\n",
      "           3     0.5202    0.5150    0.5176       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8208    0.8700    0.8447       200\n",
      "           6     0.4363    0.4450    0.4406       200\n",
      "           7     0.4574    0.4300    0.4433       200\n",
      "\n",
      "    accuracy                         0.5769      1600\n",
      "   macro avg     0.5834    0.5769    0.5733      1600\n",
      "weighted avg     0.5834    0.5769    0.5733      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4312    0.5800    0.4947       200\n",
      "           1     0.5574    0.6550    0.6023       200\n",
      "           2     0.5556    0.3000    0.3896       200\n",
      "           3     0.5250    0.5250    0.5250       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8246    0.8700    0.8467       200\n",
      "           6     0.4363    0.4450    0.4406       200\n",
      "           7     0.4599    0.4300    0.4444       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5840    0.5781    0.5741      1600\n",
      "weighted avg     0.5840    0.5781    0.5741      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 57.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4239    0.5850    0.4916       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5586    0.3100    0.3987       200\n",
      "           3     0.5185    0.4900    0.5039       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8246    0.8700    0.8467       200\n",
      "           6     0.4320    0.4450    0.4384       200\n",
      "           7     0.4427    0.4250    0.4337       200\n",
      "\n",
      "    accuracy                         0.5744      1600\n",
      "   macro avg     0.5812    0.5744    0.5711      1600\n",
      "weighted avg     0.5812    0.5744    0.5711      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 57.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4234    0.5800    0.4895       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5596    0.3050    0.3948       200\n",
      "           3     0.5224    0.5250    0.5237       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8208    0.8700    0.8447       200\n",
      "           6     0.4341    0.4450    0.4395       200\n",
      "           7     0.4565    0.4200    0.4375       200\n",
      "\n",
      "    accuracy                         0.5769      1600\n",
      "   macro avg     0.5833    0.5769    0.5732      1600\n",
      "weighted avg     0.5833    0.5769    0.5732      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4312    0.5800    0.4947       200\n",
      "           1     0.5574    0.6550    0.6023       200\n",
      "           2     0.5556    0.3000    0.3896       200\n",
      "           3     0.5250    0.5250    0.5250       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8246    0.8700    0.8467       200\n",
      "           6     0.4363    0.4450    0.4406       200\n",
      "           7     0.4599    0.4300    0.4444       200\n",
      "\n",
      "    accuracy                         0.5781      1600\n",
      "   macro avg     0.5840    0.5781    0.5741      1600\n",
      "weighted avg     0.5840    0.5781    0.5741      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 57.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4255    0.5850    0.4926       200\n",
      "           1     0.5683    0.6450    0.6042       200\n",
      "           2     0.5575    0.3150    0.4026       200\n",
      "           3     0.5156    0.4950    0.5051       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8246    0.8700    0.8467       200\n",
      "           6     0.4320    0.4450    0.4384       200\n",
      "           7     0.4421    0.4200    0.4308       200\n",
      "\n",
      "    accuracy                         0.5744      1600\n",
      "   macro avg     0.5809    0.5744    0.5713      1600\n",
      "weighted avg     0.5809    0.5744    0.5713      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4234    0.5800    0.4895       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5545    0.3050    0.3935       200\n",
      "           3     0.5253    0.5200    0.5226       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8208    0.8700    0.8447       200\n",
      "           6     0.4363    0.4450    0.4406       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5769      1600\n",
      "   macro avg     0.5830    0.5769    0.5732      1600\n",
      "weighted avg     0.5830    0.5769    0.5732      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 57.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4296    0.5800    0.4936       200\n",
      "           1     0.5598    0.6550    0.6037       200\n",
      "           2     0.5556    0.3000    0.3896       200\n",
      "           3     0.5226    0.5200    0.5213       200\n",
      "           4     0.8817    0.8200    0.8497       200\n",
      "           5     0.8246    0.8700    0.8467       200\n",
      "           6     0.4363    0.4450    0.4406       200\n",
      "           7     0.4574    0.4300    0.4433       200\n",
      "\n",
      "    accuracy                         0.5775      1600\n",
      "   macro avg     0.5835    0.5775    0.5736      1600\n",
      "weighted avg     0.5835    0.5775    0.5736      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4248    0.5650    0.4850       200\n",
      "           1     0.5689    0.6400    0.6024       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5532    0.5200    0.5361       200\n",
      "           4     0.8956    0.8150    0.8534       200\n",
      "           5     0.8233    0.8850    0.8530       200\n",
      "           6     0.4406    0.4450    0.4428       200\n",
      "           7     0.4595    0.4250    0.4416       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5855    0.5813    0.5794      1600\n",
      "weighted avg     0.5855    0.5813    0.5794      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4248    0.5650    0.4850       200\n",
      "           1     0.5785    0.6450    0.6099       200\n",
      "           2     0.5299    0.3550    0.4251       200\n",
      "           3     0.5426    0.5100    0.5258       200\n",
      "           4     0.8913    0.8200    0.8542       200\n",
      "           5     0.8302    0.8800    0.8544       200\n",
      "           6     0.4369    0.4500    0.4433       200\n",
      "           7     0.4492    0.4200    0.4341       200\n",
      "\n",
      "    accuracy                         0.5806      1600\n",
      "   macro avg     0.5854    0.5806    0.5790      1600\n",
      "weighted avg     0.5854    0.5806    0.5790      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4232    0.5650    0.4839       200\n",
      "           1     0.5818    0.6400    0.6095       200\n",
      "           2     0.5227    0.3450    0.4157       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8907    0.8150    0.8512       200\n",
      "           5     0.8224    0.8800    0.8502       200\n",
      "           6     0.4320    0.4450    0.4384       200\n",
      "           7     0.4486    0.4150    0.4312       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5832    0.5787    0.5768      1600\n",
      "weighted avg     0.5832    0.5787    0.5768      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4216    0.5650    0.4829       200\n",
      "           1     0.5766    0.6400    0.6066       200\n",
      "           2     0.5224    0.3500    0.4192       200\n",
      "           3     0.5591    0.5200    0.5389       200\n",
      "           4     0.8859    0.8150    0.8490       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4348    0.4500    0.4423       200\n",
      "           7     0.4545    0.4250    0.4393       200\n",
      "\n",
      "    accuracy                         0.5800      1600\n",
      "   macro avg     0.5851    0.5800    0.5784      1600\n",
      "weighted avg     0.5851    0.5800    0.5784      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4248    0.5650    0.4850       200\n",
      "           1     0.5766    0.6400    0.6066       200\n",
      "           2     0.5263    0.3500    0.4204       200\n",
      "           3     0.5487    0.5350    0.5418       200\n",
      "           4     0.8859    0.8150    0.8490       200\n",
      "           5     0.8255    0.8750    0.8495       200\n",
      "           6     0.4320    0.4450    0.4384       200\n",
      "           7     0.4670    0.4250    0.4450       200\n",
      "\n",
      "    accuracy                         0.5813      1600\n",
      "   macro avg     0.5859    0.5813    0.5795      1600\n",
      "weighted avg     0.5859    0.5813    0.5795      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 57.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4232    0.5650    0.4839       200\n",
      "           1     0.5818    0.6400    0.6095       200\n",
      "           2     0.5227    0.3450    0.4157       200\n",
      "           3     0.5440    0.5250    0.5344       200\n",
      "           4     0.8907    0.8150    0.8512       200\n",
      "           5     0.8224    0.8800    0.8502       200\n",
      "           6     0.4320    0.4450    0.4384       200\n",
      "           7     0.4486    0.4150    0.4312       200\n",
      "\n",
      "    accuracy                         0.5787      1600\n",
      "   macro avg     0.5832    0.5787    0.5768      1600\n",
      "weighted avg     0.5832    0.5787    0.5768      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4264    0.5650    0.4860       200\n",
      "           1     0.5792    0.6400    0.6081       200\n",
      "           2     0.5407    0.3650    0.4358       200\n",
      "           3     0.5632    0.5350    0.5487       200\n",
      "           4     0.8865    0.8200    0.8519       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4333    0.4550    0.4439       200\n",
      "           7     0.4536    0.4150    0.4334       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5890    0.5837    0.5824      1600\n",
      "weighted avg     0.5890    0.5837    0.5824      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 57.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4232    0.5650    0.4839       200\n",
      "           1     0.5792    0.6400    0.6081       200\n",
      "           2     0.5263    0.3500    0.4204       200\n",
      "           3     0.5469    0.5250    0.5357       200\n",
      "           4     0.8865    0.8200    0.8519       200\n",
      "           5     0.8294    0.8750    0.8516       200\n",
      "           6     0.4279    0.4450    0.4363       200\n",
      "           7     0.4536    0.4150    0.4334       200\n",
      "\n",
      "    accuracy                         0.5794      1600\n",
      "   macro avg     0.5841    0.5794    0.5777      1600\n",
      "weighted avg     0.5841    0.5794    0.5777      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=5, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4232    0.5650    0.4839       200\n",
      "           1     0.5818    0.6400    0.6095       200\n",
      "           2     0.5263    0.3500    0.4204       200\n",
      "           3     0.5436    0.5300    0.5367       200\n",
      "           4     0.9056    0.8150    0.8579       200\n",
      "           5     0.8287    0.8950    0.8606       200\n",
      "           6     0.4320    0.4450    0.4384       200\n",
      "           7     0.4536    0.4150    0.4334       200\n",
      "\n",
      "    accuracy                         0.5819      1600\n",
      "   macro avg     0.5868    0.5819    0.5801      1600\n",
      "weighted avg     0.5868    0.5819    0.5801      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4257    0.5300    0.4722       200\n",
      "           1     0.5771    0.6550    0.6136       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5729    0.5500    0.5612       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4486    0.4800    0.4638       200\n",
      "           7     0.4536    0.4150    0.4334       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5929    0.5900    0.5884      1600\n",
      "weighted avg     0.5929    0.5900    0.5884      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4246    0.5350    0.4735       200\n",
      "           1     0.5815    0.6600    0.6183       200\n",
      "           2     0.5217    0.3600    0.4260       200\n",
      "           3     0.5684    0.5400    0.5538       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4450    0.4850    0.4641       200\n",
      "           7     0.4494    0.4000    0.4233       200\n",
      "\n",
      "    accuracy                         0.5887      1600\n",
      "   macro avg     0.5917    0.5888    0.5869      1600\n",
      "weighted avg     0.5917    0.5887    0.5869      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4183    0.5250    0.4656       200\n",
      "           1     0.5841    0.6600    0.6197       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5692    0.5550    0.5620       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4465    0.4800    0.4627       200\n",
      "           7     0.4602    0.4050    0.4309       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5919    0.5894    0.5875      1600\n",
      "weighted avg     0.5919    0.5894    0.5875      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4234    0.5250    0.4688       200\n",
      "           1     0.5796    0.6550    0.6150       200\n",
      "           2     0.5070    0.3600    0.4211       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4393    0.4700    0.4541       200\n",
      "           7     0.4286    0.4050    0.4165       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5858    0.5831    0.5817      1600\n",
      "weighted avg     0.5858    0.5831    0.5817      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4240    0.5300    0.4711       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5798    0.5450    0.5619       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4450    0.4850    0.4641       200\n",
      "           7     0.4556    0.4100    0.4316       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5930    0.5900    0.5883      1600\n",
      "weighted avg     0.5930    0.5900    0.5883      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4183    0.5250    0.4656       200\n",
      "           1     0.5841    0.6600    0.6197       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5692    0.5550    0.5620       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4465    0.4800    0.4627       200\n",
      "           7     0.4602    0.4050    0.4309       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5919    0.5894    0.5875      1600\n",
      "weighted avg     0.5919    0.5894    0.5875      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4257    0.5300    0.4722       200\n",
      "           1     0.5815    0.6600    0.6183       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5784    0.5350    0.5558       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4424    0.4800    0.4604       200\n",
      "           7     0.4486    0.4150    0.4312       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5909    0.5881    0.5865      1600\n",
      "weighted avg     0.5909    0.5881    0.5865      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4257    0.5300    0.4722       200\n",
      "           1     0.5867    0.6600    0.6212       200\n",
      "           2     0.5070    0.3600    0.4211       200\n",
      "           3     0.5789    0.5500    0.5641       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4450    0.4850    0.4641       200\n",
      "           7     0.4637    0.4150    0.4380       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5938    0.5913    0.5896      1600\n",
      "weighted avg     0.5938    0.5913    0.5896      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4206    0.5300    0.4690       200\n",
      "           1     0.5867    0.6600    0.6212       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5722    0.5550    0.5635       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4439    0.4750    0.4589       200\n",
      "           7     0.4607    0.4100    0.4339       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5926    0.5900    0.5882      1600\n",
      "weighted avg     0.5926    0.5900    0.5882      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4257    0.5300    0.4722       200\n",
      "           1     0.5771    0.6550    0.6136       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5729    0.5500    0.5612       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4486    0.4800    0.4638       200\n",
      "           7     0.4536    0.4150    0.4334       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5929    0.5900    0.5884      1600\n",
      "weighted avg     0.5929    0.5900    0.5884      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4246    0.5350    0.4735       200\n",
      "           1     0.5815    0.6600    0.6183       200\n",
      "           2     0.5217    0.3600    0.4260       200\n",
      "           3     0.5684    0.5400    0.5538       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4450    0.4850    0.4641       200\n",
      "           7     0.4494    0.4000    0.4233       200\n",
      "\n",
      "    accuracy                         0.5887      1600\n",
      "   macro avg     0.5917    0.5888    0.5869      1600\n",
      "weighted avg     0.5917    0.5887    0.5869      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4183    0.5250    0.4656       200\n",
      "           1     0.5841    0.6600    0.6197       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5692    0.5550    0.5620       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4465    0.4800    0.4627       200\n",
      "           7     0.4602    0.4050    0.4309       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5919    0.5894    0.5875      1600\n",
      "weighted avg     0.5919    0.5894    0.5875      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4234    0.5250    0.4688       200\n",
      "           1     0.5796    0.6550    0.6150       200\n",
      "           2     0.5070    0.3600    0.4211       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4393    0.4700    0.4541       200\n",
      "           7     0.4286    0.4050    0.4165       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5858    0.5831    0.5817      1600\n",
      "weighted avg     0.5858    0.5831    0.5817      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4240    0.5300    0.4711       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5798    0.5450    0.5619       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4450    0.4850    0.4641       200\n",
      "           7     0.4556    0.4100    0.4316       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5930    0.5900    0.5883      1600\n",
      "weighted avg     0.5930    0.5900    0.5883      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4183    0.5250    0.4656       200\n",
      "           1     0.5841    0.6600    0.6197       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5692    0.5550    0.5620       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4465    0.4800    0.4627       200\n",
      "           7     0.4602    0.4050    0.4309       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5919    0.5894    0.5875      1600\n",
      "weighted avg     0.5919    0.5894    0.5875      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4257    0.5300    0.4722       200\n",
      "           1     0.5815    0.6600    0.6183       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5784    0.5350    0.5558       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4424    0.4800    0.4604       200\n",
      "           7     0.4486    0.4150    0.4312       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5909    0.5881    0.5865      1600\n",
      "weighted avg     0.5909    0.5881    0.5865      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4257    0.5300    0.4722       200\n",
      "           1     0.5867    0.6600    0.6212       200\n",
      "           2     0.5070    0.3600    0.4211       200\n",
      "           3     0.5789    0.5500    0.5641       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4450    0.4850    0.4641       200\n",
      "           7     0.4637    0.4150    0.4380       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5938    0.5913    0.5896      1600\n",
      "weighted avg     0.5938    0.5913    0.5896      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4206    0.5300    0.4690       200\n",
      "           1     0.5867    0.6600    0.6212       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5722    0.5550    0.5635       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4439    0.4750    0.4589       200\n",
      "           7     0.4607    0.4100    0.4339       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5926    0.5900    0.5882      1600\n",
      "weighted avg     0.5926    0.5900    0.5882      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4217    0.5250    0.4677       200\n",
      "           1     0.5893    0.6600    0.6226       200\n",
      "           2     0.5035    0.3550    0.4164       200\n",
      "           3     0.5663    0.5550    0.5606       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4465    0.4800    0.4627       200\n",
      "           7     0.4551    0.4050    0.4286       200\n",
      "\n",
      "    accuracy                         0.5887      1600\n",
      "   macro avg     0.5907    0.5888    0.5869      1600\n",
      "weighted avg     0.5907    0.5887    0.5869      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4234    0.5250    0.4688       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5143    0.3600    0.4235       200\n",
      "           3     0.5677    0.5450    0.5561       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4450    0.4850    0.4641       200\n",
      "           7     0.4545    0.4000    0.4255       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5903    0.5881    0.5861      1600\n",
      "weighted avg     0.5903    0.5881    0.5861      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4183    0.5250    0.4656       200\n",
      "           1     0.5841    0.6600    0.6197       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4439    0.4750    0.4589       200\n",
      "           7     0.4576    0.4050    0.4297       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5906    0.5881    0.5862      1600\n",
      "weighted avg     0.5906    0.5881    0.5862      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4228    0.5200    0.4664       200\n",
      "           1     0.5771    0.6550    0.6136       200\n",
      "           2     0.5106    0.3600    0.4223       200\n",
      "           3     0.5608    0.5300    0.5450       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4419    0.4750    0.4578       200\n",
      "           7     0.4402    0.4050    0.4219       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5865    0.5844    0.5826      1600\n",
      "weighted avg     0.5865    0.5844    0.5826      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4211    0.5200    0.4653       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5106    0.3600    0.4223       200\n",
      "           3     0.5820    0.5500    0.5656       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4450    0.4850    0.4641       200\n",
      "           7     0.4637    0.4150    0.4380       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5925    0.5900    0.5883      1600\n",
      "weighted avg     0.5925    0.5900    0.5883      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4183    0.5250    0.4656       200\n",
      "           1     0.5841    0.6600    0.6197       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5641    0.5500    0.5570       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4439    0.4750    0.4589       200\n",
      "           7     0.4576    0.4050    0.4297       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5906    0.5881    0.5862      1600\n",
      "weighted avg     0.5906    0.5881    0.5862      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4257    0.5300    0.4722       200\n",
      "           1     0.5815    0.6600    0.6183       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5699    0.5300    0.5492       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4424    0.4800    0.4604       200\n",
      "           7     0.4565    0.4200    0.4375       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5908    0.5881    0.5864      1600\n",
      "weighted avg     0.5908    0.5881    0.5864      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4257    0.5300    0.4722       200\n",
      "           1     0.5841    0.6600    0.6197       200\n",
      "           2     0.5070    0.3600    0.4211       200\n",
      "           3     0.5767    0.5450    0.5604       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4424    0.4800    0.4604       200\n",
      "           7     0.4611    0.4150    0.4368       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5925    0.5900    0.5884      1600\n",
      "weighted avg     0.5925    0.5900    0.5884      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4206    0.5300    0.4690       200\n",
      "           1     0.5893    0.6600    0.6226       200\n",
      "           2     0.5143    0.3600    0.4235       200\n",
      "           3     0.5751    0.5550    0.5649       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4439    0.4750    0.4589       200\n",
      "           7     0.4637    0.4150    0.4380       200\n",
      "\n",
      "    accuracy                         0.5906      1600\n",
      "   macro avg     0.5932    0.5906    0.5889      1600\n",
      "weighted avg     0.5932    0.5906    0.5889      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4160    0.5450    0.4719       200\n",
      "           1     0.5819    0.6750    0.6250       200\n",
      "           2     0.5524    0.2900    0.3803       200\n",
      "           3     0.5459    0.5350    0.5404       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4336    0.4900    0.4601       200\n",
      "           7     0.4500    0.4050    0.4263       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5893    0.5837    0.5795      1600\n",
      "weighted avg     0.5893    0.5837    0.5795      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4138    0.5400    0.4685       200\n",
      "           1     0.5812    0.6800    0.6267       200\n",
      "           2     0.5577    0.2900    0.3816       200\n",
      "           3     0.5366    0.5500    0.5432       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4324    0.4800    0.4550       200\n",
      "           7     0.4629    0.4050    0.4320       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5899    0.5844    0.5799      1600\n",
      "weighted avg     0.5899    0.5844    0.5799      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4160    0.5450    0.4719       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5800    0.2900    0.3867       200\n",
      "           3     0.5314    0.5500    0.5405       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4253    0.4700    0.4466       200\n",
      "           7     0.4602    0.4050    0.4309       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5902    0.5831    0.5786      1600\n",
      "weighted avg     0.5902    0.5831    0.5786      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4138    0.5400    0.4685       200\n",
      "           1     0.5837    0.6800    0.6282       200\n",
      "           2     0.5472    0.2900    0.3791       200\n",
      "           3     0.5404    0.5350    0.5377       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4336    0.4900    0.4601       200\n",
      "           7     0.4576    0.4050    0.4297       200\n",
      "\n",
      "    accuracy                         0.5837      1600\n",
      "   macro avg     0.5888    0.5837    0.5794      1600\n",
      "weighted avg     0.5888    0.5837    0.5794      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4138    0.5400    0.4685       200\n",
      "           1     0.5812    0.6800    0.6267       200\n",
      "           2     0.5577    0.2900    0.3816       200\n",
      "           3     0.5366    0.5500    0.5432       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4324    0.4800    0.4550       200\n",
      "           7     0.4629    0.4050    0.4320       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5899    0.5844    0.5799      1600\n",
      "weighted avg     0.5899    0.5844    0.5799      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4160    0.5450    0.4719       200\n",
      "           1     0.5745    0.6750    0.6207       200\n",
      "           2     0.5800    0.2900    0.3867       200\n",
      "           3     0.5314    0.5500    0.5405       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4253    0.4700    0.4466       200\n",
      "           7     0.4602    0.4050    0.4309       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5902    0.5831    0.5786      1600\n",
      "weighted avg     0.5902    0.5831    0.5786      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4138    0.5400    0.4685       200\n",
      "           1     0.5819    0.6750    0.6250       200\n",
      "           2     0.5524    0.2900    0.3803       200\n",
      "           3     0.5455    0.5400    0.5427       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4381    0.4950    0.4648       200\n",
      "           7     0.4581    0.4100    0.4327       200\n",
      "\n",
      "    accuracy                         0.5850      1600\n",
      "   macro avg     0.5905    0.5850    0.5808      1600\n",
      "weighted avg     0.5905    0.5850    0.5808      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4138    0.5400    0.4685       200\n",
      "           1     0.5769    0.6750    0.6221       200\n",
      "           2     0.5577    0.2900    0.3816       200\n",
      "           3     0.5396    0.5450    0.5423       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4350    0.4850    0.4586       200\n",
      "           7     0.4633    0.4100    0.4350       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5901    0.5844    0.5800      1600\n",
      "weighted avg     0.5901    0.5844    0.5800      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 58.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4160    0.5450    0.4719       200\n",
      "           1     0.5763    0.6800    0.6239       200\n",
      "           2     0.5859    0.2900    0.3880       200\n",
      "           3     0.5340    0.5500    0.5419       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4253    0.4700    0.4466       200\n",
      "           7     0.4633    0.4100    0.4350       200\n",
      "\n",
      "    accuracy                         0.5844      1600\n",
      "   macro avg     0.5919    0.5844    0.5799      1600\n",
      "weighted avg     0.5919    0.5844    0.5799      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4257    0.5300    0.4722       200\n",
      "           1     0.5771    0.6550    0.6136       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5729    0.5500    0.5612       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4486    0.4800    0.4638       200\n",
      "           7     0.4536    0.4150    0.4334       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5929    0.5900    0.5884      1600\n",
      "weighted avg     0.5929    0.5900    0.5884      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 58.88%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4246    0.5350    0.4735       200\n",
      "           1     0.5815    0.6600    0.6183       200\n",
      "           2     0.5217    0.3600    0.4260       200\n",
      "           3     0.5684    0.5400    0.5538       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4450    0.4850    0.4641       200\n",
      "           7     0.4494    0.4000    0.4233       200\n",
      "\n",
      "    accuracy                         0.5887      1600\n",
      "   macro avg     0.5917    0.5888    0.5869      1600\n",
      "weighted avg     0.5917    0.5887    0.5869      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4183    0.5250    0.4656       200\n",
      "           1     0.5841    0.6600    0.6197       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5692    0.5550    0.5620       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4465    0.4800    0.4627       200\n",
      "           7     0.4602    0.4050    0.4309       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5919    0.5894    0.5875      1600\n",
      "weighted avg     0.5919    0.5894    0.5875      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 58.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4234    0.5250    0.4688       200\n",
      "           1     0.5796    0.6550    0.6150       200\n",
      "           2     0.5070    0.3600    0.4211       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4393    0.4700    0.4541       200\n",
      "           7     0.4286    0.4050    0.4165       200\n",
      "\n",
      "    accuracy                         0.5831      1600\n",
      "   macro avg     0.5858    0.5831    0.5817      1600\n",
      "weighted avg     0.5858    0.5831    0.5817      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4240    0.5300    0.4711       200\n",
      "           1     0.5789    0.6600    0.6168       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5798    0.5450    0.5619       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4450    0.4850    0.4641       200\n",
      "           7     0.4556    0.4100    0.4316       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5930    0.5900    0.5883      1600\n",
      "weighted avg     0.5930    0.5900    0.5883      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 58.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4183    0.5250    0.4656       200\n",
      "           1     0.5841    0.6600    0.6197       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5692    0.5550    0.5620       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4465    0.4800    0.4627       200\n",
      "           7     0.4602    0.4050    0.4309       200\n",
      "\n",
      "    accuracy                         0.5894      1600\n",
      "   macro avg     0.5919    0.5894    0.5875      1600\n",
      "weighted avg     0.5919    0.5894    0.5875      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 58.81%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4257    0.5300    0.4722       200\n",
      "           1     0.5815    0.6600    0.6183       200\n",
      "           2     0.5071    0.3550    0.4176       200\n",
      "           3     0.5784    0.5350    0.5558       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4424    0.4800    0.4604       200\n",
      "           7     0.4486    0.4150    0.4312       200\n",
      "\n",
      "    accuracy                         0.5881      1600\n",
      "   macro avg     0.5909    0.5881    0.5865      1600\n",
      "weighted avg     0.5909    0.5881    0.5865      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4257    0.5300    0.4722       200\n",
      "           1     0.5867    0.6600    0.6212       200\n",
      "           2     0.5070    0.3600    0.4211       200\n",
      "           3     0.5789    0.5500    0.5641       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4450    0.4850    0.4641       200\n",
      "           7     0.4637    0.4150    0.4380       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5938    0.5913    0.5896      1600\n",
      "weighted avg     0.5938    0.5913    0.5896      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=8, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4206    0.5300    0.4690       200\n",
      "           1     0.5867    0.6600    0.6212       200\n",
      "           2     0.5180    0.3600    0.4248       200\n",
      "           3     0.5722    0.5550    0.5635       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8667    0.8450    0.8557       200\n",
      "           6     0.4439    0.4750    0.4589       200\n",
      "           7     0.4607    0.4100    0.4339       200\n",
      "\n",
      "    accuracy                         0.5900      1600\n",
      "   macro avg     0.5926    0.5900    0.5882      1600\n",
      "weighted avg     0.5926    0.5900    0.5882      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4318    0.5700    0.4914       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5930    0.5100    0.5484       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8718    0.8500    0.8608       200\n",
      "           6     0.4712    0.4900    0.4804       200\n",
      "           7     0.4615    0.4500    0.4557       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5973    0.5925    0.5911      1600\n",
      "weighted avg     0.5973    0.5925    0.5911      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.62%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4318    0.5700    0.4914       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5912    0.5350    0.5617       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4729    0.4800    0.4764       200\n",
      "           7     0.4740    0.4550    0.4643       200\n",
      "\n",
      "    accuracy                         0.5962      1600\n",
      "   macro avg     0.6007    0.5963    0.5947      1600\n",
      "weighted avg     0.6007    0.5962    0.5947      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5700    0.4903       200\n",
      "           1     0.5826    0.6700    0.6233       200\n",
      "           2     0.5294    0.3600    0.4286       200\n",
      "           3     0.5722    0.5550    0.5635       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4783    0.4400    0.4583       200\n",
      "\n",
      "    accuracy                         0.5975      1600\n",
      "   macro avg     0.6014    0.5975    0.5957      1600\n",
      "weighted avg     0.6014    0.5975    0.5957      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5690    0.6600    0.6111       200\n",
      "           2     0.5185    0.3500    0.4179       200\n",
      "           3     0.5977    0.5200    0.5561       200\n",
      "           4     0.8750    0.8750    0.8750       200\n",
      "           5     0.8718    0.8500    0.8608       200\n",
      "           6     0.4729    0.4800    0.4764       200\n",
      "           7     0.4673    0.4650    0.4662       200\n",
      "\n",
      "    accuracy                         0.5956      1600\n",
      "   macro avg     0.6004    0.5956    0.5941      1600\n",
      "weighted avg     0.6004    0.5956    0.5941      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4318    0.5700    0.4914       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5838    0.5400    0.5610       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4680    0.4750    0.4715       200\n",
      "           7     0.4789    0.4550    0.4667       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5993    0.5950    0.5936      1600\n",
      "weighted avg     0.5993    0.5950    0.5936      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5700    0.4903       200\n",
      "           1     0.5826    0.6700    0.6233       200\n",
      "           2     0.5294    0.3600    0.4286       200\n",
      "           3     0.5722    0.5550    0.5635       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4783    0.4400    0.4583       200\n",
      "\n",
      "    accuracy                         0.5975      1600\n",
      "   macro avg     0.6014    0.5975    0.5957      1600\n",
      "weighted avg     0.6014    0.5975    0.5957      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5843    0.5200    0.5503       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8718    0.8500    0.8608       200\n",
      "           6     0.4729    0.4800    0.4764       200\n",
      "           7     0.4541    0.4450    0.4495       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5955    0.5913    0.5898      1600\n",
      "weighted avg     0.5955    0.5913    0.5898      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4318    0.5700    0.4914       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5737    0.5450    0.5590       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4729    0.4800    0.4764       200\n",
      "           7     0.4703    0.4350    0.4519       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5976    0.5938    0.5921      1600\n",
      "weighted avg     0.5976    0.5938    0.5921      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5700    0.4903       200\n",
      "           1     0.5852    0.6700    0.6247       200\n",
      "           2     0.5294    0.3600    0.4286       200\n",
      "           3     0.5670    0.5500    0.5584       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4757    0.4400    0.4571       200\n",
      "\n",
      "    accuracy                         0.5969      1600\n",
      "   macro avg     0.6007    0.5969    0.5951      1600\n",
      "weighted avg     0.6007    0.5969    0.5951      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4318    0.5700    0.4914       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5930    0.5100    0.5484       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8718    0.8500    0.8608       200\n",
      "           6     0.4712    0.4900    0.4804       200\n",
      "           7     0.4615    0.4500    0.4557       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5973    0.5925    0.5911      1600\n",
      "weighted avg     0.5973    0.5925    0.5911      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.62%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4318    0.5700    0.4914       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5912    0.5350    0.5617       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4729    0.4800    0.4764       200\n",
      "           7     0.4740    0.4550    0.4643       200\n",
      "\n",
      "    accuracy                         0.5962      1600\n",
      "   macro avg     0.6007    0.5963    0.5947      1600\n",
      "weighted avg     0.6007    0.5962    0.5947      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5700    0.4903       200\n",
      "           1     0.5826    0.6700    0.6233       200\n",
      "           2     0.5294    0.3600    0.4286       200\n",
      "           3     0.5722    0.5550    0.5635       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4783    0.4400    0.4583       200\n",
      "\n",
      "    accuracy                         0.5975      1600\n",
      "   macro avg     0.6014    0.5975    0.5957      1600\n",
      "weighted avg     0.6014    0.5975    0.5957      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5690    0.6600    0.6111       200\n",
      "           2     0.5185    0.3500    0.4179       200\n",
      "           3     0.5977    0.5200    0.5561       200\n",
      "           4     0.8750    0.8750    0.8750       200\n",
      "           5     0.8718    0.8500    0.8608       200\n",
      "           6     0.4729    0.4800    0.4764       200\n",
      "           7     0.4673    0.4650    0.4662       200\n",
      "\n",
      "    accuracy                         0.5956      1600\n",
      "   macro avg     0.6004    0.5956    0.5941      1600\n",
      "weighted avg     0.6004    0.5956    0.5941      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4318    0.5700    0.4914       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5838    0.5400    0.5610       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4680    0.4750    0.4715       200\n",
      "           7     0.4789    0.4550    0.4667       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5993    0.5950    0.5936      1600\n",
      "weighted avg     0.5993    0.5950    0.5936      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5700    0.4903       200\n",
      "           1     0.5826    0.6700    0.6233       200\n",
      "           2     0.5294    0.3600    0.4286       200\n",
      "           3     0.5722    0.5550    0.5635       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4783    0.4400    0.4583       200\n",
      "\n",
      "    accuracy                         0.5975      1600\n",
      "   macro avg     0.6014    0.5975    0.5957      1600\n",
      "weighted avg     0.6014    0.5975    0.5957      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5843    0.5200    0.5503       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8718    0.8500    0.8608       200\n",
      "           6     0.4729    0.4800    0.4764       200\n",
      "           7     0.4541    0.4450    0.4495       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5955    0.5913    0.5898      1600\n",
      "weighted avg     0.5955    0.5913    0.5898      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4318    0.5700    0.4914       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5737    0.5450    0.5590       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4729    0.4800    0.4764       200\n",
      "           7     0.4703    0.4350    0.4519       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5976    0.5938    0.5921      1600\n",
      "weighted avg     0.5976    0.5938    0.5921      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5700    0.4903       200\n",
      "           1     0.5852    0.6700    0.6247       200\n",
      "           2     0.5294    0.3600    0.4286       200\n",
      "           3     0.5670    0.5500    0.5584       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4757    0.4400    0.4571       200\n",
      "\n",
      "    accuracy                         0.5969      1600\n",
      "   macro avg     0.6007    0.5969    0.5951      1600\n",
      "weighted avg     0.6007    0.5969    0.5951      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4335    0.5700    0.4924       200\n",
      "           1     0.5628    0.6500    0.6032       200\n",
      "           2     0.5109    0.3500    0.4154       200\n",
      "           3     0.5920    0.5150    0.5508       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8718    0.8500    0.8608       200\n",
      "           6     0.4757    0.4900    0.4828       200\n",
      "           7     0.4667    0.4550    0.4608       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5985    0.5938    0.5923      1600\n",
      "weighted avg     0.5985    0.5938    0.5923      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4318    0.5700    0.4914       200\n",
      "           1     0.5647    0.6550    0.6065       200\n",
      "           2     0.5224    0.3500    0.4192       200\n",
      "           3     0.5944    0.5350    0.5632       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4729    0.4800    0.4764       200\n",
      "           7     0.4715    0.4550    0.4631       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5998    0.5950    0.5935      1600\n",
      "weighted avg     0.5998    0.5950    0.5935      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5700    0.4903       200\n",
      "           1     0.5826    0.6700    0.6233       200\n",
      "           2     0.5294    0.3600    0.4286       200\n",
      "           3     0.5722    0.5550    0.5635       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4783    0.4400    0.4583       200\n",
      "\n",
      "    accuracy                         0.5975      1600\n",
      "   macro avg     0.6014    0.5975    0.5957      1600\n",
      "weighted avg     0.6014    0.5975    0.5957      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5671    0.6550    0.6079       200\n",
      "           2     0.5147    0.3500    0.4167       200\n",
      "           3     0.5977    0.5200    0.5561       200\n",
      "           4     0.8750    0.8750    0.8750       200\n",
      "           5     0.8718    0.8500    0.8608       200\n",
      "           6     0.4729    0.4800    0.4764       200\n",
      "           7     0.4673    0.4650    0.4662       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5997    0.5950    0.5935      1600\n",
      "weighted avg     0.5997    0.5950    0.5935      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4318    0.5700    0.4914       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5815    0.5350    0.5573       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4703    0.4750    0.4726       200\n",
      "           7     0.4764    0.4550    0.4655       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5994    0.5950    0.5935      1600\n",
      "weighted avg     0.5994    0.5950    0.5935      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5700    0.4903       200\n",
      "           1     0.5826    0.6700    0.6233       200\n",
      "           2     0.5294    0.3600    0.4286       200\n",
      "           3     0.5722    0.5550    0.5635       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4783    0.4400    0.4583       200\n",
      "\n",
      "    accuracy                         0.5975      1600\n",
      "   macro avg     0.6014    0.5975    0.5957      1600\n",
      "weighted avg     0.6014    0.5975    0.5957      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5696    0.6550    0.6093       200\n",
      "           2     0.5109    0.3500    0.4154       200\n",
      "           3     0.5810    0.5200    0.5488       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8718    0.8500    0.8608       200\n",
      "           6     0.4729    0.4800    0.4764       200\n",
      "           7     0.4564    0.4450    0.4506       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.5960    0.5919    0.5903      1600\n",
      "weighted avg     0.5960    0.5919    0.5903      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4318    0.5700    0.4914       200\n",
      "           1     0.5746    0.6550    0.6121       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5737    0.5450    0.5590       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4729    0.4800    0.4764       200\n",
      "           7     0.4703    0.4350    0.4519       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.5983    0.5944    0.5927      1600\n",
      "weighted avg     0.5983    0.5944    0.5927      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5700    0.4903       200\n",
      "           1     0.5852    0.6700    0.6247       200\n",
      "           2     0.5294    0.3600    0.4286       200\n",
      "           3     0.5670    0.5500    0.5584       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4757    0.4400    0.4571       200\n",
      "\n",
      "    accuracy                         0.5969      1600\n",
      "   macro avg     0.6007    0.5969    0.5951      1600\n",
      "weighted avg     0.6007    0.5969    0.5951      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4203    0.5800    0.4874       200\n",
      "           1     0.5638    0.6850    0.6185       200\n",
      "           2     0.5789    0.2750    0.3729       200\n",
      "           3     0.5550    0.5800    0.5672       200\n",
      "           4     0.8750    0.8750    0.8750       200\n",
      "           5     0.8629    0.8500    0.8564       200\n",
      "           6     0.4524    0.4750    0.4634       200\n",
      "           7     0.5000    0.4250    0.4595       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.6010    0.5931    0.5875      1600\n",
      "weighted avg     0.6010    0.5931    0.5875      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4239    0.5850    0.4916       200\n",
      "           1     0.5569    0.6850    0.6143       200\n",
      "           2     0.5914    0.2750    0.3754       200\n",
      "           3     0.5550    0.5800    0.5672       200\n",
      "           4     0.8750    0.8750    0.8750       200\n",
      "           5     0.8629    0.8500    0.8564       200\n",
      "           6     0.4545    0.4750    0.4645       200\n",
      "           7     0.5000    0.4250    0.4595       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.6025    0.5938    0.5880      1600\n",
      "weighted avg     0.6025    0.5938    0.5880      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4224    0.5850    0.4906       200\n",
      "           1     0.5592    0.6850    0.6157       200\n",
      "           2     0.5955    0.2650    0.3668       200\n",
      "           3     0.5339    0.5900    0.5606       200\n",
      "           4     0.8750    0.8750    0.8750       200\n",
      "           5     0.8629    0.8500    0.8564       200\n",
      "           6     0.4519    0.4700    0.4608       200\n",
      "           7     0.5031    0.4100    0.4518       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.6005    0.5913    0.5847      1600\n",
      "weighted avg     0.6005    0.5913    0.5847      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4224    0.5850    0.4906       200\n",
      "           1     0.5592    0.6850    0.6157       200\n",
      "           2     0.5851    0.2750    0.3741       200\n",
      "           3     0.5637    0.5750    0.5693       200\n",
      "           4     0.8750    0.8750    0.8750       200\n",
      "           5     0.8629    0.8500    0.8564       200\n",
      "           6     0.4524    0.4750    0.4634       200\n",
      "           7     0.4913    0.4250    0.4558       200\n",
      "\n",
      "    accuracy                         0.5931      1600\n",
      "   macro avg     0.6015    0.5931    0.5875      1600\n",
      "weighted avg     0.6015    0.5931    0.5875      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4239    0.5850    0.4916       200\n",
      "           1     0.5615    0.6850    0.6171       200\n",
      "           2     0.5851    0.2750    0.3741       200\n",
      "           3     0.5493    0.5850    0.5666       200\n",
      "           4     0.8750    0.8750    0.8750       200\n",
      "           5     0.8629    0.8500    0.8564       200\n",
      "           6     0.4545    0.4750    0.4645       200\n",
      "           7     0.5030    0.4200    0.4578       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.6019    0.5938    0.5879      1600\n",
      "weighted avg     0.6019    0.5938    0.5879      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4224    0.5850    0.4906       200\n",
      "           1     0.5592    0.6850    0.6157       200\n",
      "           2     0.5955    0.2650    0.3668       200\n",
      "           3     0.5339    0.5900    0.5606       200\n",
      "           4     0.8750    0.8750    0.8750       200\n",
      "           5     0.8629    0.8500    0.8564       200\n",
      "           6     0.4519    0.4700    0.4608       200\n",
      "           7     0.5031    0.4100    0.4518       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.6005    0.5913    0.5847      1600\n",
      "weighted avg     0.6005    0.5913    0.5847      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4224    0.5850    0.4906       200\n",
      "           1     0.5615    0.6850    0.6171       200\n",
      "           2     0.5851    0.2750    0.3741       200\n",
      "           3     0.5502    0.5750    0.5623       200\n",
      "           4     0.8750    0.8750    0.8750       200\n",
      "           5     0.8629    0.8500    0.8564       200\n",
      "           6     0.4554    0.4850    0.4697       200\n",
      "           7     0.4940    0.4100    0.4481       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.6008    0.5925    0.5867      1600\n",
      "weighted avg     0.6008    0.5925    0.5867      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4239    0.5850    0.4916       200\n",
      "           1     0.5661    0.6850    0.6199       200\n",
      "           2     0.5851    0.2750    0.3741       200\n",
      "           3     0.5446    0.5800    0.5617       200\n",
      "           4     0.8750    0.8750    0.8750       200\n",
      "           5     0.8629    0.8500    0.8564       200\n",
      "           6     0.4554    0.4850    0.4697       200\n",
      "           7     0.5030    0.4150    0.4548       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.6020    0.5938    0.5879      1600\n",
      "weighted avg     0.6020    0.5938    0.5879      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4224    0.5850    0.4906       200\n",
      "           1     0.5547    0.6850    0.6130       200\n",
      "           2     0.6000    0.2700    0.3724       200\n",
      "           3     0.5367    0.5850    0.5598       200\n",
      "           4     0.8750    0.8750    0.8750       200\n",
      "           5     0.8629    0.8500    0.8564       200\n",
      "           6     0.4541    0.4700    0.4619       200\n",
      "           7     0.5000    0.4100    0.4505       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.6007    0.5913    0.5850      1600\n",
      "weighted avg     0.6007    0.5913    0.5850      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4318    0.5700    0.4914       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5930    0.5100    0.5484       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8718    0.8500    0.8608       200\n",
      "           6     0.4712    0.4900    0.4804       200\n",
      "           7     0.4615    0.4500    0.4557       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.5973    0.5925    0.5911      1600\n",
      "weighted avg     0.5973    0.5925    0.5911      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.62%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4318    0.5700    0.4914       200\n",
      "           1     0.5721    0.6550    0.6107       200\n",
      "           2     0.5221    0.3550    0.4226       200\n",
      "           3     0.5912    0.5350    0.5617       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4729    0.4800    0.4764       200\n",
      "           7     0.4740    0.4550    0.4643       200\n",
      "\n",
      "    accuracy                         0.5962      1600\n",
      "   macro avg     0.6007    0.5963    0.5947      1600\n",
      "weighted avg     0.6007    0.5962    0.5947      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5700    0.4903       200\n",
      "           1     0.5826    0.6700    0.6233       200\n",
      "           2     0.5294    0.3600    0.4286       200\n",
      "           3     0.5722    0.5550    0.5635       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4783    0.4400    0.4583       200\n",
      "\n",
      "    accuracy                         0.5975      1600\n",
      "   macro avg     0.6014    0.5975    0.5957      1600\n",
      "weighted avg     0.6014    0.5975    0.5957      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5690    0.6600    0.6111       200\n",
      "           2     0.5185    0.3500    0.4179       200\n",
      "           3     0.5977    0.5200    0.5561       200\n",
      "           4     0.8750    0.8750    0.8750       200\n",
      "           5     0.8718    0.8500    0.8608       200\n",
      "           6     0.4729    0.4800    0.4764       200\n",
      "           7     0.4673    0.4650    0.4662       200\n",
      "\n",
      "    accuracy                         0.5956      1600\n",
      "   macro avg     0.6004    0.5956    0.5941      1600\n",
      "weighted avg     0.6004    0.5956    0.5941      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4318    0.5700    0.4914       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5838    0.5400    0.5610       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4680    0.4750    0.4715       200\n",
      "           7     0.4789    0.4550    0.4667       200\n",
      "\n",
      "    accuracy                         0.5950      1600\n",
      "   macro avg     0.5993    0.5950    0.5936      1600\n",
      "weighted avg     0.5993    0.5950    0.5936      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.75%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5700    0.4903       200\n",
      "           1     0.5826    0.6700    0.6233       200\n",
      "           2     0.5294    0.3600    0.4286       200\n",
      "           3     0.5722    0.5550    0.5635       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4783    0.4400    0.4583       200\n",
      "\n",
      "    accuracy                         0.5975      1600\n",
      "   macro avg     0.6014    0.5975    0.5957      1600\n",
      "weighted avg     0.6014    0.5975    0.5957      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.13%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4313    0.5650    0.4892       200\n",
      "           1     0.5677    0.6500    0.6061       200\n",
      "           2     0.5072    0.3500    0.4142       200\n",
      "           3     0.5843    0.5200    0.5503       200\n",
      "           4     0.8744    0.8700    0.8722       200\n",
      "           5     0.8718    0.8500    0.8608       200\n",
      "           6     0.4729    0.4800    0.4764       200\n",
      "           7     0.4541    0.4450    0.4495       200\n",
      "\n",
      "    accuracy                         0.5913      1600\n",
      "   macro avg     0.5955    0.5913    0.5898      1600\n",
      "weighted avg     0.5955    0.5913    0.5898      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4318    0.5700    0.4914       200\n",
      "           1     0.5727    0.6500    0.6089       200\n",
      "           2     0.5182    0.3550    0.4214       200\n",
      "           3     0.5737    0.5450    0.5590       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4729    0.4800    0.4764       200\n",
      "           7     0.4703    0.4350    0.4519       200\n",
      "\n",
      "    accuracy                         0.5938      1600\n",
      "   macro avg     0.5976    0.5938    0.5921      1600\n",
      "weighted avg     0.5976    0.5938    0.5921      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=12, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4302    0.5700    0.4903       200\n",
      "           1     0.5852    0.6700    0.6247       200\n",
      "           2     0.5294    0.3600    0.4286       200\n",
      "           3     0.5670    0.5500    0.5584       200\n",
      "           4     0.8737    0.8650    0.8693       200\n",
      "           5     0.8673    0.8500    0.8586       200\n",
      "           6     0.4772    0.4700    0.4736       200\n",
      "           7     0.4757    0.4400    0.4571       200\n",
      "\n",
      "    accuracy                         0.5969      1600\n",
      "   macro avg     0.6007    0.5969    0.5951      1600\n",
      "weighted avg     0.6007    0.5969    0.5951      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=20, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 60.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4812    0.5750    0.5239       200\n",
      "           1     0.5870    0.6750    0.6279       200\n",
      "           2     0.5270    0.3900    0.4483       200\n",
      "           3     0.5714    0.4800    0.5217       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4630    0.5000    0.4808       200\n",
      "           7     0.4680    0.4750    0.4715       200\n",
      "\n",
      "    accuracy                         0.6031      1600\n",
      "   macro avg     0.6056    0.6031    0.6016      1600\n",
      "weighted avg     0.6056    0.6031    0.6016      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=20, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 60.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4711    0.5700    0.5158       200\n",
      "           1     0.5787    0.6800    0.6253       200\n",
      "           2     0.5205    0.3800    0.4393       200\n",
      "           3     0.5628    0.5150    0.5379       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4630    0.5000    0.4808       200\n",
      "           7     0.4890    0.4450    0.4660       200\n",
      "\n",
      "    accuracy                         0.6025      1600\n",
      "   macro avg     0.6041    0.6025    0.6004      1600\n",
      "weighted avg     0.6041    0.6025    0.6004      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=20, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 60.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4733    0.5750    0.5192       200\n",
      "           1     0.5812    0.6800    0.6267       200\n",
      "           2     0.5294    0.4050    0.4589       200\n",
      "           3     0.5528    0.5500    0.5514       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4764    0.5050    0.4903       200\n",
      "           7     0.5031    0.4100    0.4518       200\n",
      "\n",
      "    accuracy                         0.6069      1600\n",
      "   macro avg     0.6080    0.6069    0.6046      1600\n",
      "weighted avg     0.6080    0.6069    0.6046      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=20, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 60.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5763    0.6800    0.6239       200\n",
      "           2     0.5310    0.3850    0.4464       200\n",
      "           3     0.5714    0.5000    0.5333       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4633    0.5050    0.4833       200\n",
      "           7     0.4844    0.4650    0.4745       200\n",
      "\n",
      "    accuracy                         0.6038      1600\n",
      "   macro avg     0.6061    0.6038    0.6020      1600\n",
      "weighted avg     0.6061    0.6038    0.6020      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=20, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 60.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4730    0.5700    0.5170       200\n",
      "           1     0.5769    0.6750    0.6221       200\n",
      "           2     0.5205    0.3800    0.4393       200\n",
      "           3     0.5580    0.5050    0.5302       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4865    0.4500    0.4675       200\n",
      "\n",
      "    accuracy                         0.6006      1600\n",
      "   macro avg     0.6023    0.6006    0.5987      1600\n",
      "weighted avg     0.6023    0.6006    0.5987      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=20, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 60.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4733    0.5750    0.5192       200\n",
      "           1     0.5812    0.6800    0.6267       200\n",
      "           2     0.5294    0.4050    0.4589       200\n",
      "           3     0.5528    0.5500    0.5514       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4764    0.5050    0.4903       200\n",
      "           7     0.5031    0.4100    0.4518       200\n",
      "\n",
      "    accuracy                         0.6069      1600\n",
      "   macro avg     0.6080    0.6069    0.6046      1600\n",
      "weighted avg     0.6080    0.6069    0.6046      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=20, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 60.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4766    0.5600    0.5149       200\n",
      "           1     0.5763    0.6800    0.6239       200\n",
      "           2     0.5342    0.3900    0.4509       200\n",
      "           3     0.5622    0.5200    0.5403       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4973    0.4600    0.4779       200\n",
      "\n",
      "    accuracy                         0.6050      1600\n",
      "   macro avg     0.6069    0.6050    0.6032      1600\n",
      "weighted avg     0.6069    0.6050    0.6032      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=20, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 60.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4728    0.5650    0.5148       200\n",
      "           1     0.5812    0.6800    0.6267       200\n",
      "           2     0.5306    0.3900    0.4496       200\n",
      "           3     0.5550    0.5300    0.5422       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.5000    0.4400    0.4681       200\n",
      "\n",
      "    accuracy                         0.6038      1600\n",
      "   macro avg     0.6054    0.6038    0.6018      1600\n",
      "weighted avg     0.6054    0.6038    0.6018      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=20, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 60.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4711    0.5700    0.5158       200\n",
      "           1     0.5837    0.6800    0.6282       200\n",
      "           2     0.5260    0.4050    0.4576       200\n",
      "           3     0.5490    0.5600    0.5545       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4764    0.5050    0.4903       200\n",
      "           7     0.5094    0.4050    0.4513       200\n",
      "\n",
      "    accuracy                         0.6069      1600\n",
      "   macro avg     0.6079    0.6069    0.6045      1600\n",
      "weighted avg     0.6079    0.6069    0.6045      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=15, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 60.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4812    0.5750    0.5239       200\n",
      "           1     0.5870    0.6750    0.6279       200\n",
      "           2     0.5270    0.3900    0.4483       200\n",
      "           3     0.5714    0.4800    0.5217       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4630    0.5000    0.4808       200\n",
      "           7     0.4680    0.4750    0.4715       200\n",
      "\n",
      "    accuracy                         0.6031      1600\n",
      "   macro avg     0.6056    0.6031    0.6016      1600\n",
      "weighted avg     0.6056    0.6031    0.6016      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=15, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 60.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4711    0.5700    0.5158       200\n",
      "           1     0.5787    0.6800    0.6253       200\n",
      "           2     0.5205    0.3800    0.4393       200\n",
      "           3     0.5628    0.5150    0.5379       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4630    0.5000    0.4808       200\n",
      "           7     0.4890    0.4450    0.4660       200\n",
      "\n",
      "    accuracy                         0.6025      1600\n",
      "   macro avg     0.6041    0.6025    0.6004      1600\n",
      "weighted avg     0.6041    0.6025    0.6004      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=15, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 60.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4733    0.5750    0.5192       200\n",
      "           1     0.5812    0.6800    0.6267       200\n",
      "           2     0.5294    0.4050    0.4589       200\n",
      "           3     0.5528    0.5500    0.5514       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4764    0.5050    0.4903       200\n",
      "           7     0.5031    0.4100    0.4518       200\n",
      "\n",
      "    accuracy                         0.6069      1600\n",
      "   macro avg     0.6080    0.6069    0.6046      1600\n",
      "weighted avg     0.6080    0.6069    0.6046      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=15, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 60.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5763    0.6800    0.6239       200\n",
      "           2     0.5310    0.3850    0.4464       200\n",
      "           3     0.5714    0.5000    0.5333       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4633    0.5050    0.4833       200\n",
      "           7     0.4844    0.4650    0.4745       200\n",
      "\n",
      "    accuracy                         0.6038      1600\n",
      "   macro avg     0.6061    0.6038    0.6020      1600\n",
      "weighted avg     0.6061    0.6038    0.6020      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=15, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 60.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4730    0.5700    0.5170       200\n",
      "           1     0.5769    0.6750    0.6221       200\n",
      "           2     0.5205    0.3800    0.4393       200\n",
      "           3     0.5580    0.5050    0.5302       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4865    0.4500    0.4675       200\n",
      "\n",
      "    accuracy                         0.6006      1600\n",
      "   macro avg     0.6023    0.6006    0.5987      1600\n",
      "weighted avg     0.6023    0.6006    0.5987      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=15, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 60.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4733    0.5750    0.5192       200\n",
      "           1     0.5812    0.6800    0.6267       200\n",
      "           2     0.5294    0.4050    0.4589       200\n",
      "           3     0.5528    0.5500    0.5514       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4764    0.5050    0.4903       200\n",
      "           7     0.5031    0.4100    0.4518       200\n",
      "\n",
      "    accuracy                         0.6069      1600\n",
      "   macro avg     0.6080    0.6069    0.6046      1600\n",
      "weighted avg     0.6080    0.6069    0.6046      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=15, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 60.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4766    0.5600    0.5149       200\n",
      "           1     0.5763    0.6800    0.6239       200\n",
      "           2     0.5342    0.3900    0.4509       200\n",
      "           3     0.5622    0.5200    0.5403       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4973    0.4600    0.4779       200\n",
      "\n",
      "    accuracy                         0.6050      1600\n",
      "   macro avg     0.6069    0.6050    0.6032      1600\n",
      "weighted avg     0.6069    0.6050    0.6032      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=15, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 60.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4728    0.5650    0.5148       200\n",
      "           1     0.5812    0.6800    0.6267       200\n",
      "           2     0.5306    0.3900    0.4496       200\n",
      "           3     0.5550    0.5300    0.5422       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.5000    0.4400    0.4681       200\n",
      "\n",
      "    accuracy                         0.6038      1600\n",
      "   macro avg     0.6054    0.6038    0.6018      1600\n",
      "weighted avg     0.6054    0.6038    0.6018      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=15, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 60.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4711    0.5700    0.5158       200\n",
      "           1     0.5837    0.6800    0.6282       200\n",
      "           2     0.5260    0.4050    0.4576       200\n",
      "           3     0.5490    0.5600    0.5545       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4764    0.5050    0.4903       200\n",
      "           7     0.5094    0.4050    0.4513       200\n",
      "\n",
      "    accuracy                         0.6069      1600\n",
      "   macro avg     0.6079    0.6069    0.6045      1600\n",
      "weighted avg     0.6079    0.6069    0.6045      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=10, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 60.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4772    0.5750    0.5215       200\n",
      "           1     0.5819    0.6750    0.6250       200\n",
      "           2     0.5238    0.3850    0.4438       200\n",
      "           3     0.5673    0.4850    0.5229       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4605    0.4950    0.4771       200\n",
      "           7     0.4747    0.4700    0.4724       200\n",
      "\n",
      "    accuracy                         0.6019      1600\n",
      "   macro avg     0.6041    0.6019    0.6001      1600\n",
      "weighted avg     0.6041    0.6019    0.6001      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=10, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 60.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4711    0.5700    0.5158       200\n",
      "           1     0.5787    0.6800    0.6253       200\n",
      "           2     0.5205    0.3800    0.4393       200\n",
      "           3     0.5628    0.5150    0.5379       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4630    0.5000    0.4808       200\n",
      "           7     0.4890    0.4450    0.4660       200\n",
      "\n",
      "    accuracy                         0.6025      1600\n",
      "   macro avg     0.6041    0.6025    0.6004      1600\n",
      "weighted avg     0.6041    0.6025    0.6004      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=10, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 60.62%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4711    0.5700    0.5158       200\n",
      "           1     0.5812    0.6800    0.6267       200\n",
      "           2     0.5294    0.4050    0.4589       200\n",
      "           3     0.5500    0.5500    0.5500       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4764    0.5050    0.4903       200\n",
      "           7     0.5031    0.4100    0.4518       200\n",
      "\n",
      "    accuracy                         0.6062      1600\n",
      "   macro avg     0.6073    0.6062    0.6040      1600\n",
      "weighted avg     0.6073    0.6062    0.6040      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=10, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 60.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5763    0.6800    0.6239       200\n",
      "           2     0.5310    0.3850    0.4464       200\n",
      "           3     0.5747    0.5000    0.5348       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4633    0.5050    0.4833       200\n",
      "           7     0.4870    0.4700    0.4784       200\n",
      "\n",
      "    accuracy                         0.6044      1600\n",
      "   macro avg     0.6068    0.6044    0.6026      1600\n",
      "weighted avg     0.6068    0.6044    0.6026      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=10, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 60.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4730    0.5700    0.5170       200\n",
      "           1     0.5794    0.6750    0.6236       200\n",
      "           2     0.5170    0.3800    0.4380       200\n",
      "           3     0.5580    0.5050    0.5302       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4865    0.4500    0.4675       200\n",
      "\n",
      "    accuracy                         0.6006      1600\n",
      "   macro avg     0.6022    0.6006    0.5987      1600\n",
      "weighted avg     0.6022    0.6006    0.5987      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=10, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 60.62%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4711    0.5700    0.5158       200\n",
      "           1     0.5812    0.6800    0.6267       200\n",
      "           2     0.5294    0.4050    0.4589       200\n",
      "           3     0.5500    0.5500    0.5500       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4764    0.5050    0.4903       200\n",
      "           7     0.5031    0.4100    0.4518       200\n",
      "\n",
      "    accuracy                         0.6062      1600\n",
      "   macro avg     0.6073    0.6062    0.6040      1600\n",
      "weighted avg     0.6073    0.6062    0.6040      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=10, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 60.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4766    0.5600    0.5149       200\n",
      "           1     0.5738    0.6800    0.6224       200\n",
      "           2     0.5342    0.3900    0.4509       200\n",
      "           3     0.5652    0.5200    0.5417       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4973    0.4600    0.4779       200\n",
      "\n",
      "    accuracy                         0.6050      1600\n",
      "   macro avg     0.6069    0.6050    0.6032      1600\n",
      "weighted avg     0.6069    0.6050    0.6032      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=10, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 60.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4728    0.5650    0.5148       200\n",
      "           1     0.5794    0.6750    0.6236       200\n",
      "           2     0.5270    0.3900    0.4483       200\n",
      "           3     0.5550    0.5300    0.5422       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.5000    0.4400    0.4681       200\n",
      "\n",
      "    accuracy                         0.6031      1600\n",
      "   macro avg     0.6047    0.6031    0.6013      1600\n",
      "weighted avg     0.6047    0.6031    0.6013      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=10, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 60.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4711    0.5700    0.5158       200\n",
      "           1     0.5837    0.6800    0.6282       200\n",
      "           2     0.5260    0.4050    0.4576       200\n",
      "           3     0.5490    0.5600    0.5545       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4764    0.5050    0.4903       200\n",
      "           7     0.5094    0.4050    0.4513       200\n",
      "\n",
      "    accuracy                         0.6069      1600\n",
      "   macro avg     0.6079    0.6069    0.6045      1600\n",
      "weighted avg     0.6079    0.6069    0.6045      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=5, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4465    0.6050    0.5138       200\n",
      "           1     0.5534    0.7000    0.6181       200\n",
      "           2     0.5926    0.2400    0.3416       200\n",
      "           3     0.5213    0.5500    0.5353       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4410    0.5050    0.4709       200\n",
      "           7     0.5380    0.4250    0.4749       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.6045    0.5944    0.5864      1600\n",
      "weighted avg     0.6045    0.5944    0.5864      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=5, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4400    0.6050    0.5095       200\n",
      "           1     0.5469    0.7000    0.6140       200\n",
      "           2     0.5926    0.2400    0.3416       200\n",
      "           3     0.5209    0.5600    0.5398       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4480    0.4950    0.4703       200\n",
      "           7     0.5226    0.4050    0.4563       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.6017    0.5919    0.5835      1600\n",
      "weighted avg     0.6017    0.5919    0.5835      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=5, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4505    0.6150    0.5201       200\n",
      "           1     0.5426    0.7000    0.6114       200\n",
      "           2     0.5949    0.2350    0.3369       200\n",
      "           3     0.5308    0.5600    0.5450       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4484    0.5000    0.4728       200\n",
      "           7     0.5220    0.4150    0.4624       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.6041    0.5944    0.5856      1600\n",
      "weighted avg     0.6041    0.5944    0.5856      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=5, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4481    0.6050    0.5149       200\n",
      "           1     0.5405    0.7000    0.6100       200\n",
      "           2     0.5926    0.2400    0.3416       200\n",
      "           3     0.5190    0.5450    0.5317       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4484    0.5000    0.4728       200\n",
      "           7     0.5250    0.4200    0.4667       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.6021    0.5925    0.5843      1600\n",
      "weighted avg     0.6021    0.5925    0.5843      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=5, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 59.19%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4400    0.6050    0.5095       200\n",
      "           1     0.5447    0.7000    0.6127       200\n",
      "           2     0.5926    0.2400    0.3416       200\n",
      "           3     0.5209    0.5600    0.5398       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4500    0.4950    0.4714       200\n",
      "           7     0.5226    0.4050    0.4563       200\n",
      "\n",
      "    accuracy                         0.5919      1600\n",
      "   macro avg     0.6017    0.5919    0.5835      1600\n",
      "weighted avg     0.6017    0.5919    0.5835      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=5, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 59.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4505    0.6150    0.5201       200\n",
      "           1     0.5426    0.7000    0.6114       200\n",
      "           2     0.5949    0.2350    0.3369       200\n",
      "           3     0.5308    0.5600    0.5450       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4484    0.5000    0.4728       200\n",
      "           7     0.5220    0.4150    0.4624       200\n",
      "\n",
      "    accuracy                         0.5944      1600\n",
      "   macro avg     0.6041    0.5944    0.5856      1600\n",
      "weighted avg     0.6041    0.5944    0.5856      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=5, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4465    0.6050    0.5138       200\n",
      "           1     0.5447    0.7000    0.6127       200\n",
      "           2     0.5926    0.2400    0.3416       200\n",
      "           3     0.5189    0.5500    0.5340       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4464    0.5000    0.4717       200\n",
      "           7     0.5253    0.4150    0.4637       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.6022    0.5925    0.5842      1600\n",
      "weighted avg     0.6022    0.5925    0.5842      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=5, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 59.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4432    0.6050    0.5116       200\n",
      "           1     0.5447    0.7000    0.6127       200\n",
      "           2     0.5875    0.2350    0.3357       200\n",
      "           3     0.5234    0.5600    0.5411       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4480    0.4950    0.4703       200\n",
      "           7     0.5253    0.4150    0.4637       200\n",
      "\n",
      "    accuracy                         0.5925      1600\n",
      "   macro avg     0.6019    0.5925    0.5839      1600\n",
      "weighted avg     0.6019    0.5925    0.5839      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=5, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 59.56%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4522    0.6150    0.5212       200\n",
      "           1     0.5447    0.7000    0.6127       200\n",
      "           2     0.5949    0.2350    0.3369       200\n",
      "           3     0.5305    0.5650    0.5472       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8711    0.8450    0.8579       200\n",
      "           6     0.4505    0.5000    0.4739       200\n",
      "           7     0.5250    0.4200    0.4667       200\n",
      "\n",
      "    accuracy                         0.5956      1600\n",
      "   macro avg     0.6051    0.5956    0.5869      1600\n",
      "weighted avg     0.6051    0.5956    0.5869      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=None, min_samples_split=2, min_samples_leaf=1 ===\n",
      "Accuracy: 60.31%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4812    0.5750    0.5239       200\n",
      "           1     0.5870    0.6750    0.6279       200\n",
      "           2     0.5270    0.3900    0.4483       200\n",
      "           3     0.5714    0.4800    0.5217       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4630    0.5000    0.4808       200\n",
      "           7     0.4680    0.4750    0.4715       200\n",
      "\n",
      "    accuracy                         0.6031      1600\n",
      "   macro avg     0.6056    0.6031    0.6016      1600\n",
      "weighted avg     0.6056    0.6031    0.6016      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=None, min_samples_split=2, min_samples_leaf=2 ===\n",
      "Accuracy: 60.25%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4711    0.5700    0.5158       200\n",
      "           1     0.5787    0.6800    0.6253       200\n",
      "           2     0.5205    0.3800    0.4393       200\n",
      "           3     0.5628    0.5150    0.5379       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4630    0.5000    0.4808       200\n",
      "           7     0.4890    0.4450    0.4660       200\n",
      "\n",
      "    accuracy                         0.6025      1600\n",
      "   macro avg     0.6041    0.6025    0.6004      1600\n",
      "weighted avg     0.6041    0.6025    0.6004      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=None, min_samples_split=2, min_samples_leaf=4 ===\n",
      "Accuracy: 60.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4733    0.5750    0.5192       200\n",
      "           1     0.5812    0.6800    0.6267       200\n",
      "           2     0.5294    0.4050    0.4589       200\n",
      "           3     0.5528    0.5500    0.5514       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4764    0.5050    0.4903       200\n",
      "           7     0.5031    0.4100    0.4518       200\n",
      "\n",
      "    accuracy                         0.6069      1600\n",
      "   macro avg     0.6080    0.6069    0.6046      1600\n",
      "weighted avg     0.6080    0.6069    0.6046      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=None, min_samples_split=5, min_samples_leaf=1 ===\n",
      "Accuracy: 60.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4748    0.5650    0.5160       200\n",
      "           1     0.5763    0.6800    0.6239       200\n",
      "           2     0.5310    0.3850    0.4464       200\n",
      "           3     0.5714    0.5000    0.5333       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4633    0.5050    0.4833       200\n",
      "           7     0.4844    0.4650    0.4745       200\n",
      "\n",
      "    accuracy                         0.6038      1600\n",
      "   macro avg     0.6061    0.6038    0.6020      1600\n",
      "weighted avg     0.6061    0.6038    0.6020      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=None, min_samples_split=5, min_samples_leaf=2 ===\n",
      "Accuracy: 60.06%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4730    0.5700    0.5170       200\n",
      "           1     0.5769    0.6750    0.6221       200\n",
      "           2     0.5205    0.3800    0.4393       200\n",
      "           3     0.5580    0.5050    0.5302       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.4865    0.4500    0.4675       200\n",
      "\n",
      "    accuracy                         0.6006      1600\n",
      "   macro avg     0.6023    0.6006    0.5987      1600\n",
      "weighted avg     0.6023    0.6006    0.5987      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=None, min_samples_split=5, min_samples_leaf=4 ===\n",
      "Accuracy: 60.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4733    0.5750    0.5192       200\n",
      "           1     0.5812    0.6800    0.6267       200\n",
      "           2     0.5294    0.4050    0.4589       200\n",
      "           3     0.5528    0.5500    0.5514       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4764    0.5050    0.4903       200\n",
      "           7     0.5031    0.4100    0.4518       200\n",
      "\n",
      "    accuracy                         0.6069      1600\n",
      "   macro avg     0.6080    0.6069    0.6046      1600\n",
      "weighted avg     0.6080    0.6069    0.6046      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=None, min_samples_split=10, min_samples_leaf=1 ===\n",
      "Accuracy: 60.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4766    0.5600    0.5149       200\n",
      "           1     0.5763    0.6800    0.6239       200\n",
      "           2     0.5342    0.3900    0.4509       200\n",
      "           3     0.5622    0.5200    0.5403       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4608    0.5000    0.4796       200\n",
      "           7     0.4973    0.4600    0.4779       200\n",
      "\n",
      "    accuracy                         0.6050      1600\n",
      "   macro avg     0.6069    0.6050    0.6032      1600\n",
      "weighted avg     0.6069    0.6050    0.6032      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=None, min_samples_split=10, min_samples_leaf=2 ===\n",
      "Accuracy: 60.38%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4728    0.5650    0.5148       200\n",
      "           1     0.5812    0.6800    0.6267       200\n",
      "           2     0.5306    0.3900    0.4496       200\n",
      "           3     0.5550    0.5300    0.5422       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4562    0.4950    0.4748       200\n",
      "           7     0.5000    0.4400    0.4681       200\n",
      "\n",
      "    accuracy                         0.6038      1600\n",
      "   macro avg     0.6054    0.6038    0.6018      1600\n",
      "weighted avg     0.6054    0.6038    0.6018      1600\n",
      "\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "\n",
      "=== Testing: n_estimators=200, max_features=None, max_depth=None, min_samples_split=10, min_samples_leaf=4 ===\n",
      "Accuracy: 60.69%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4711    0.5700    0.5158       200\n",
      "           1     0.5837    0.6800    0.6282       200\n",
      "           2     0.5260    0.4050    0.4576       200\n",
      "           3     0.5490    0.5600    0.5545       200\n",
      "           4     0.8719    0.8850    0.8784       200\n",
      "           5     0.8756    0.8450    0.8601       200\n",
      "           6     0.4764    0.5050    0.4903       200\n",
      "           7     0.5094    0.4050    0.4513       200\n",
      "\n",
      "    accuracy                         0.6069      1600\n",
      "   macro avg     0.6079    0.6069    0.6045      1600\n",
      "weighted avg     0.6079    0.6069    0.6045      1600\n",
      "\n",
      "\n",
      "*** Best accuracy 60.94% with parameters:\n",
      "    n_estimators = 50\n",
      "    max_features = None\n",
      "    max_depth = 20\n",
      "    min_samples_split = 2\n",
      "    min_samples_leaf = 1\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble    import RandomForestClassifier\n",
    "from sklearn.metrics     import accuracy_score, classification_report\n",
    "from itertools           import product\n",
    "\n",
    "# 1) Define your parameter grid\n",
    "param_grid = {\n",
    "    'n_estimators':      [50, 100, 150, 200],\n",
    "    'max_features':      ['sqrt', 'log2', 5, 8, 12, None],\n",
    "    'max_depth':         [20, 15, 10, 5, None],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf':  [1, 2, 4]\n",
    "}\n",
    "\n",
    "best_acc    = 0.0\n",
    "best_params = None\n",
    "\n",
    "# 2) Loop over every combination\n",
    "for (n_est, mf, md, mss, msl) in product(\n",
    "        param_grid['n_estimators'],\n",
    "        param_grid['max_features'],\n",
    "        param_grid['max_depth'],\n",
    "        param_grid['min_samples_split'],\n",
    "        param_grid['min_samples_leaf']\n",
    "    ):\n",
    "    print(\"*\"*80)\n",
    "    print(\"-\"*80)\n",
    "    print(\"*\"*80)\n",
    "    print(f\"\\n=== Testing: n_estimators={n_est}, max_features={mf}, \"\n",
    "          f\"max_depth={md}, min_samples_split={mss}, min_samples_leaf={msl} ===\")\n",
    "\n",
    "    # 3) Build & fit\n",
    "    rf = RandomForestClassifier(\n",
    "        n_estimators=n_est,\n",
    "        max_features=mf,\n",
    "        max_depth=md,\n",
    "        min_samples_split=mss,\n",
    "        min_samples_leaf=msl,\n",
    "        n_jobs=-1,\n",
    "        random_state=42\n",
    "    )\n",
    "    rf.fit(concatenated_preds, y_train)\n",
    "\n",
    "    # 4) Predict & evaluate\n",
    "    y_pred = rf.predict(concatenated_preds_test)\n",
    "    acc    = accuracy_score(y_test, y_pred)\n",
    "    print(f\"Accuracy: {acc:.2%}\")\n",
    "    print(classification_report(y_test, y_pred, digits=4))\n",
    "\n",
    "    # 5) Track best\n",
    "    if acc > best_acc:\n",
    "        best_acc    = acc\n",
    "        best_params = {\n",
    "            'n_estimators': n_est,\n",
    "            'max_features': mf,\n",
    "            'max_depth':    md,\n",
    "            'min_samples_split': mss,\n",
    "            'min_samples_leaf':  msl\n",
    "        }\n",
    "\n",
    "# 6) Summary of the very best combo\n",
    "print(\"\\n*** Best accuracy {:.2%} with parameters:\".format(best_acc))\n",
    "for k, v in best_params.items():\n",
    "    print(f\"    {k} = {v}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "589e47a6-a1d3-47dc-93bf-8f3eecc79e19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello\n"
     ]
    }
   ],
   "source": [
    "*** Best accuracy 60.94% with parameters:\n",
    "    n_estimators = 50\n",
    "    max_features = None\n",
    "    max_depth = 20\n",
    "    min_samples_split = 2\n",
    "    min_samples_leaf = 1 ***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "bf04f267-1c55-412a-9a09-5c06bf455761",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 60.94%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.4818    0.5950    0.5324       200\n",
      "           1     0.5685    0.6850    0.6213       200\n",
      "           2     0.5455    0.3900    0.4548       200\n",
      "           3     0.5988    0.5000    0.5450       200\n",
      "           4     0.8676    0.8850    0.8762       200\n",
      "           5     0.8848    0.8450    0.8645       200\n",
      "           6     0.4739    0.5000    0.4866       200\n",
      "           7     0.4847    0.4750    0.4798       200\n",
      "\n",
      "    accuracy                         0.6094      1600\n",
      "   macro avg     0.6132    0.6094    0.6076      1600\n",
      "weighted avg     0.6132    0.6094    0.6076      1600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(\n",
    "        n_estimators=50,\n",
    "        max_features=None,\n",
    "        max_depth=20,\n",
    "        min_samples_split=2,\n",
    "        min_samples_leaf=1,\n",
    "        n_jobs=-1,\n",
    "        random_state=42\n",
    "    )\n",
    "\n",
    "rf.fit(concatenated_preds, y_train)\n",
    "\n",
    "# 4) Predict & evaluate\n",
    "y_pred = rf.predict(concatenated_preds_test)\n",
    "acc    = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {acc:.2%}\")\n",
    "print(classification_report(y_test, y_pred, digits=4))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "0efb8b17-efbf-4dd5-8aae-bfbbbe064f1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_indices = test_data.class_indices\n",
    "\n",
    "index_to_class = {v:k for k,v in class_indices.items()}\n",
    "class_names = [index_to_class[i] for i in range(len(index_to_class))]\n",
    "\n",
    "# 3) Compute your confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# 4) Plot with labels\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap='Blues', xticks_rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "703f0bfb-e59c-4221-9123-489f0e99f1bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in /usr/local/lib/python3.10/dist-packages (3.0.0)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xgboost) (2.1.3)\n",
      "Requirement already satisfied: nvidia-nccl-cu12 in /usr/local/lib/python3.10/dist-packages (from xgboost) (2.23.4)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.15.2)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "a5f66c0e-9683-43e4-86d5-12d0e660d2a2",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
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      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
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      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
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      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
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      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5675\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
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      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.570625\n",
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      "0.57125\n",
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      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.553125\n",
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      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.551875\n",
      "********************************************************************************\n",
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      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.555\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
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      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.551875\n",
      "********************************************************************************\n",
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      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.571875\n",
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      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
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      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57375\n",
      "********************************************************************************\n",
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      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
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      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5775\n",
      "********************************************************************************\n",
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      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58125\n",
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      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.578125\n",
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      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.570625\n",
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      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
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      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.569375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
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      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57125\n",
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      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.554375\n",
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      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.551875\n",
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      "--------------------------------------------------------------------------------\n",
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      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.554375\n",
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      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5475\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
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      "****For params as:\n",
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      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
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      "--------------------------------------------------------------------------------\n",
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      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
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      "--------------------------------------------------------------------------------\n",
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      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
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      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.568125\n",
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      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5675\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57\n",
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      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.581875\n",
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      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
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      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
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      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.568125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5675\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.553125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.551875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.555\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.551875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.554375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.551875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.554375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5475\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.553125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.551875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.555\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.551875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.554375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.551875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.554375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5475\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.569375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.595\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.595\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.595625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.595625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.595625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.595625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.595625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.595\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.595625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.595\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.595625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.595\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.595\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.568125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5675\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.56875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.56375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.56125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.56125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.561875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.561875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.568125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.566875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.569375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.56625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.569375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.569375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.56375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.563125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.56125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.561875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.561875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.56625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.569375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.569375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.56375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.563125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.56125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.561875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.561875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.566875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.595625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.595\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.596875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.595625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.595\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.595625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.595\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.595625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.595625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.595\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.595\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.596875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.573125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.56875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.569375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.569375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.57875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.578125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.57625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5775\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.575\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.576875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5725\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.570625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.575625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.57375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.571875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.01, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.574375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.595\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.595\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.595\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.595625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.595\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.595\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.583125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.594375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5925\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.593125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.5875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.596875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.591875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.59125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.581875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.588125\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.586875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.584375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.58875\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.595\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.580625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.579375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.59\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.589375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.590625\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "0.5825\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "0.58375\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 1}\n",
      "0.585\n",
      "********************************************************************************\n",
      "--------------------------------------------------------------------------------\n",
      "********************************************************************************\n",
      "****For params as:\n",
      "{'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "0.58375\n",
      "Top 10 Hyperparameter Configurations:\n",
      "1. Accuracy: 0.5969 | Params: {'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "2. Accuracy: 0.5969 | Params: {'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "3. Accuracy: 0.5969 | Params: {'n_estimators': 150, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "4. Accuracy: 0.5962 | Params: {'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}\n",
      "5. Accuracy: 0.5962 | Params: {'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "6. Accuracy: 0.5962 | Params: {'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "7. Accuracy: 0.5962 | Params: {'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
      "8. Accuracy: 0.5962 | Params: {'n_estimators': 100, 'learning_rate': 0.2, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 1.0, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "9. Accuracy: 0.5956 | Params: {'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "10. Accuracy: 0.5956 | Params: {'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0.1, 'reg_lambda': 1}\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "from xgboost import XGBClassifier, callback\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# 1) Define the parameter grid tuned for 24-D features\n",
    "param_grid = {\n",
    "    'n_estimators':     [50, 100, 150],\n",
    "    'learning_rate':    [0.01, 0.1, 0.2],\n",
    "    'max_depth':        [3, 5, 7],\n",
    "    'min_child_weight': [1, 3, 5],\n",
    "    'gamma':            [0, 0.2],\n",
    "    'subsample':        [0.7, 1.0],\n",
    "    'colsample_bytree': [0.7, 1.0],\n",
    "    'reg_alpha':        [0, 0.1],\n",
    "    'reg_lambda':       [1, 2]\n",
    "}\n",
    "\n",
    "# 2) Prepare to collect results\n",
    "keys = list(param_grid.keys())\n",
    "values = list(param_grid.values())\n",
    "results = []\n",
    "\n",
    "# 3) Iterate over all combinations\n",
    "for combo in itertools.product(*values):\n",
    "    params = dict(zip(keys, combo))\n",
    "    # Initialize classifier with fixed and variable params\n",
    "    clf = XGBClassifier(\n",
    "        objective='multi:softmax',\n",
    "        num_class=8,\n",
    "        #use_label_encoder=False,\n",
    "        eval_metric='mlogloss',\n",
    "        n_jobs=-1,\n",
    "        random_state=42,\n",
    "        **params\n",
    "    )\n",
    "    # Train with early stopping on a validation set\n",
    "    clf.fit(\n",
    "        concatenated_preds, y_train,\n",
    "        eval_set=[(concatenated_preds_test, y_test)],\n",
    "        verbose=False\n",
    "    )\n",
    "    # Evaluate\n",
    "    y_pred = clf.predict(concatenated_preds_test)\n",
    "    acc = accuracy_score(y_test, y_pred)\n",
    "    print(\"*\"*80)\n",
    "    print(\"-\"*80)\n",
    "    print(\"*\"*80)\n",
    "    print(\"****For params as:\")\n",
    "    print(params)\n",
    "    print(acc)\n",
    "    # Store params + accuracy\n",
    "    record = params.copy()\n",
    "    record['accuracy'] = acc\n",
    "    results.append(record)\n",
    "\n",
    "# 4) Sort results by descending accuracy\n",
    "results_sorted = sorted(results, key=lambda x: x['accuracy'], reverse=True)\n",
    "\n",
    "# 5) Display top 10 configurations\n",
    "print(\"Top 10 Hyperparameter Configurations:\")\n",
    "for idx, r in enumerate(results_sorted[:10], 1):\n",
    "    acc = r.pop('accuracy')\n",
    "    print(f\"{idx}. Accuracy: {acc:.4f} | Params: {r}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "214f070d-5854-439b-91e3-ac7b2447df0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "params= {'n_estimators': 100, 'learning_rate': 0.1, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.2, 'subsample': 0.7, 'colsample_bytree': 0.7, 'reg_alpha': 0, 'reg_lambda': 2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "df659ac2-3999-4860-8f8a-e7f3099a350f",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = XGBClassifier(\n",
    "        objective='multi:softmax',\n",
    "        num_class=8,\n",
    "        #use_label_encoder=False,\n",
    "        eval_metric='mlogloss',\n",
    "        n_jobs=-1,\n",
    "        random_state=42,\n",
    "        **params\n",
    "    )\n",
    "    # Train with early stopping on a validation set\n",
    "clf.fit(\n",
    "    concatenated_preds, y_train,\n",
    "    eval_set=[(concatenated_preds_test, y_test)],\n",
    "    verbose=False\n",
    ")\n",
    "# Evaluate\n",
    "y_pred = clf.predict(concatenated_preds_test)\n",
    "acc = accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "b84847b2-834d-4a51-8f7f-5144bc3d4718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_indices = test_data.class_indices\n",
    "\n",
    "index_to_class = {v:k for k,v in class_indices.items()}\n",
    "class_names = [index_to_class[i] for i in range(len(index_to_class))]\n",
    "\n",
    "# 3) Compute your confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# 4) Plot with labels\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap='Blues', xticks_rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "8fa4fca7-bf84-4567-b57f-90bf8071999b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.2.3)\n",
      "Requirement already satisfied: numpy>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.1.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2025.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas) (2025.2)\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
      "Collecting ace_tools\n",
      "  Downloading ace_tools-0.0-py3-none-any.whl.metadata (300 bytes)\n",
      "Downloading ace_tools-0.0-py3-none-any.whl (1.1 kB)\n",
      "Installing collected packages: ace_tools\n",
      "Successfully installed ace_tools-0.0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas\n",
    "!pip install ace_tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "48f914c9-c205-4d44-9fef-5394ba4c73f7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "{'n_estimators': 50, 'learning_rate': 0.01, 'max_depth': 1, 'min_samples_split': 2, 'min_samples_leaf': 2}\n",
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      "{'n_estimators': 100, 'learning_rate': 0.5, 'max_depth': 1, 'min_samples_split': 10, 'min_samples_leaf': 1}\n",
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      "{'n_estimators': 100, 'learning_rate': 0.5, 'max_depth': 1, 'min_samples_split': 10, 'min_samples_leaf': 2}\n",
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      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 2, 'min_samples_split': 10, 'min_samples_leaf': 4}\n",
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       "        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>n_estimators</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>min_samples_split</th>\n",
       "      <th>min_samples_leaf</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0.600625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0.599375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.598125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.598125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>100</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.197500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>100</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.197500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>100</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.197500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>100</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.197500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>100</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.197500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>576 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     n_estimators  learning_rate  max_depth  min_samples_split  \\\n",
       "0             150           0.10          4                 10   \n",
       "1             200           0.01          4                 10   \n",
       "2             200           0.10          4                 10   \n",
       "3             150           0.10          4                  2   \n",
       "4             150           0.10          4                  5   \n",
       "..            ...            ...        ...                ...   \n",
       "571           100           0.50          1                  5   \n",
       "572           100           0.50          1                 10   \n",
       "573           100           0.50          1                  2   \n",
       "574           100           0.50          1                  2   \n",
       "575           100           0.50          1                  2   \n",
       "\n",
       "     min_samples_leaf  accuracy  \n",
       "0                   4  0.600625  \n",
       "1                   2  0.600000  \n",
       "2                   4  0.599375  \n",
       "3                   4  0.598125  \n",
       "4                   4  0.598125  \n",
       "..                ...       ...  \n",
       "571                 1  0.197500  \n",
       "572                 2  0.197500  \n",
       "573                 1  0.197500  \n",
       "574                 2  0.197500  \n",
       "575                 4  0.197500  \n",
       "\n",
       "[576 rows x 6 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import itertools\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from inspect import signature\n",
    "\n",
    "# 1) Expanded parameter grid for AdaBoost on 24‑D features\n",
    "param_grid = {\n",
    "    'n_estimators':       [50, 100, 150, 200],\n",
    "    'learning_rate':      [0.01, 0.1, 0.5, 1.0],\n",
    "    'max_depth':          [1, 2, 3, 4],\n",
    "    'min_samples_split':  [2, 5, 10],\n",
    "    'min_samples_leaf':   [1, 2, 4]\n",
    "}\n",
    "\n",
    "# 2) Determine correct keyword: 'estimator' vs 'base_estimator'\n",
    "sig = signature(AdaBoostClassifier)\n",
    "est_kw = 'estimator' if 'estimator' in sig.parameters else 'base_estimator'\n",
    "\n",
    "# 3) Set up for grid search\n",
    "keys   = list(param_grid.keys())\n",
    "values = list(param_grid.values())\n",
    "results = []\n",
    "\n",
    "# 4) Manual grid loop\n",
    "for combo in itertools.product(*values):\n",
    "    params = dict(zip(keys, combo))\n",
    "    # Build base DecisionTree with these params\n",
    "    base_est = DecisionTreeClassifier(\n",
    "        max_depth=params['max_depth'],\n",
    "        min_samples_split=params['min_samples_split'],\n",
    "        min_samples_leaf=params['min_samples_leaf'],\n",
    "        random_state=42\n",
    "    )\n",
    "    # Prepare AdaBoost kwargs dynamically\n",
    "    ada_kwargs = {\n",
    "        'n_estimators': params['n_estimators'],\n",
    "        'learning_rate': params['learning_rate'],\n",
    "        'random_state': 42,\n",
    "        est_kw: base_est\n",
    "    }\n",
    "    clf = AdaBoostClassifier(**ada_kwargs)\n",
    "\n",
    "    # Train & evaluate\n",
    "    clf.fit(concatenated_preds, y_train)\n",
    "    y_pred = clf.predict(concatenated_preds_test)\n",
    "    acc = accuracy_score(y_test, y_pred)\n",
    "    print(acc)\n",
    "    print(params)\n",
    "    print(\"*\"*80)\n",
    "    # Record results\n",
    "    record = params.copy()\n",
    "    record['accuracy'] = acc\n",
    "    results.append(record)\n",
    "\n",
    "# 5) Create DataFrame sorted by descending accuracy\n",
    "df = pd.DataFrame(results)\n",
    "df_sorted = df.sort_values('accuracy', ascending=False).reset_index(drop=True)\n",
    "df_sorted\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c98e8cf3-b485-4bdf-a5e3-30d6aa16b42b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "024c9e04-93bf-4e2f-b060-15491d42e63d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.600625\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "    'n_estimators': 150,\n",
    "    'learning_rate': 0.1,\n",
    "    'max_depth': 4,\n",
    "    'min_samples_split': 10,\n",
    "    'min_samples_leaf': 4\n",
    "}\n",
    "# Build base DecisionTree with these params\n",
    "base_est = DecisionTreeClassifier(\n",
    "    max_depth=params['max_depth'],\n",
    "    min_samples_split=params['min_samples_split'],\n",
    "    min_samples_leaf=params['min_samples_leaf'],\n",
    "    random_state=42\n",
    ")\n",
    "# Prepare AdaBoost kwargs dynamically\n",
    "ada_kwargs = {\n",
    "    'n_estimators': params['n_estimators'],\n",
    "    'learning_rate': params['learning_rate'],\n",
    "    'random_state': 42,\n",
    "    est_kw: base_est\n",
    "}\n",
    "clf = AdaBoostClassifier(**ada_kwargs)\n",
    "\n",
    "clf.fit(concatenated_preds, y_train)\n",
    "y_pred = clf.predict(concatenated_preds_test)\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "print(acc)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "b472d3a8-5ad2-4237-9287-059de5bd0276",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_indices = test_data.class_indices\n",
    "\n",
    "index_to_class = {v:k for k,v in class_indices.items()}\n",
    "class_names = [index_to_class[i] for i in range(len(index_to_class))]\n",
    "\n",
    "# 3) Compute your confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# 4) Plot with labels\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap='Blues', xticks_rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "7fbb511c-31f2-433b-9d3c-9a7ed10918e6",
   "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>n_estimators</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>min_samples_split</th>\n",
       "      <th>min_samples_leaf</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0.600625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0.599375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.598125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.598125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.598125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.598125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>50</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0.597500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.597500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>50</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.597500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0.597500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>200</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.597500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>50</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.596875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>150</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.596875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.596875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>50</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.596250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>100</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.595625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>100</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.595625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>100</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0.595000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>100</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.595000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n_estimators  learning_rate  max_depth  min_samples_split  \\\n",
       "0            150           0.10          4                 10   \n",
       "1            200           0.01          4                 10   \n",
       "2            200           0.10          4                 10   \n",
       "3            150           0.10          4                  2   \n",
       "4            150           0.10          4                  5   \n",
       "5             50           0.50          4                  2   \n",
       "6             50           0.50          4                  5   \n",
       "7             50           0.10          4                 10   \n",
       "8             50           0.10          4                  2   \n",
       "9             50           0.10          4                  5   \n",
       "10           100           0.10          4                 10   \n",
       "11           200           0.01          4                 10   \n",
       "12            50           0.10          4                 10   \n",
       "13           150           0.10          4                  5   \n",
       "14            50           0.50          4                  2   \n",
       "15            50           0.01          4                 10   \n",
       "16           100           0.10          4                  2   \n",
       "17           100           0.01          4                 10   \n",
       "18           100           0.01          4                 10   \n",
       "19           100           0.01          4                  2   \n",
       "\n",
       "    min_samples_leaf  accuracy  \n",
       "0                  4  0.600625  \n",
       "1                  2  0.600000  \n",
       "2                  4  0.599375  \n",
       "3                  4  0.598125  \n",
       "4                  4  0.598125  \n",
       "5                  4  0.598125  \n",
       "6                  4  0.598125  \n",
       "7                  4  0.597500  \n",
       "8                  4  0.597500  \n",
       "9                  4  0.597500  \n",
       "10                 4  0.597500  \n",
       "11                 1  0.597500  \n",
       "12                 1  0.596875  \n",
       "13                 2  0.596875  \n",
       "14                 2  0.596875  \n",
       "15                 2  0.596250  \n",
       "16                 1  0.595625  \n",
       "17                 2  0.595625  \n",
       "18                 4  0.595000  \n",
       "19                 1  0.595000  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sorted[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "4b5ea65c-3761-40a5-95af-7416ec03e964",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_estimators</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>min_samples_split</th>\n",
       "      <th>min_samples_leaf</th>\n",
       "      <th>accuracy</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>150</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
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       "      <th>451</th>\n",
       "      <td>150</td>\n",
       "      <td>0.01</td>\n",
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       "      <td>2</td>\n",
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       "      <th>452</th>\n",
       "      <td>150</td>\n",
       "      <td>0.01</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>100</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.482500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>100</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.480625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>100</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.480625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>200</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.478750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>150</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.478125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>150</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.478125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>150</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.476875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>200</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.473750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>200</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.473750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>200</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.473750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>200</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.473750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>200</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0.473750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>200</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.471875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>200</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.471250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>200</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.471250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>200</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0.449375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>200</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.449375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     n_estimators  learning_rate  max_depth  min_samples_split  \\\n",
       "450           150           0.01          3                 10   \n",
       "451           150           0.01          3                  2   \n",
       "452           150           0.01          3                  2   \n",
       "453           100           0.01          3                  2   \n",
       "454           100           0.01          3                  5   \n",
       "455           100           0.01          3                 10   \n",
       "456           200           0.01          3                  2   \n",
       "457           150           0.01          3                 10   \n",
       "458           150           0.01          3                  5   \n",
       "459           150           0.01          3                  2   \n",
       "460           200           0.01          3                  2   \n",
       "461           200           0.01          3                  5   \n",
       "462           200           0.01          3                  5   \n",
       "463           200           0.01          3                 10   \n",
       "464           200           0.01          3                 10   \n",
       "465           200           0.01          3                  2   \n",
       "466           200           0.01          3                  5   \n",
       "467           200           0.01          3                 10   \n",
       "468           200           0.10          1                 10   \n",
       "469           200           0.10          1                  5   \n",
       "\n",
       "     min_samples_leaf  accuracy  \n",
       "450                 2  0.489375  \n",
       "451                 4  0.489375  \n",
       "452                 2  0.486875  \n",
       "453                 1  0.482500  \n",
       "454                 1  0.480625  \n",
       "455                 1  0.480625  \n",
       "456                 2  0.478750  \n",
       "457                 1  0.478125  \n",
       "458                 1  0.478125  \n",
       "459                 1  0.476875  \n",
       "460                 4  0.473750  \n",
       "461                 4  0.473750  \n",
       "462                 2  0.473750  \n",
       "463                 2  0.473750  \n",
       "464                 4  0.473750  \n",
       "465                 1  0.471875  \n",
       "466                 1  0.471250  \n",
       "467                 1  0.471250  \n",
       "468                 4  0.449375  \n",
       "469                 2  0.449375  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sorted[450:470]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "ea588a7a-b470-4f0f-a24d-c59aa240cc65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RepoUrl('https://huggingface.co/madhavsinghabcde/ensemble_complete_models', endpoint='https://huggingface.co', repo_type='model', repo_id='madhavsinghabcde/ensemble_complete_models')"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.create_repo(repo_id = 'madhavsinghabcde/ensemble_complete_models', repo_type='model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "1330d69e-50a1-4be4-80b0-7d5b1f1ba7bf",
   "metadata": {},
   "outputs": [
    {
     "ename": "HFValidationError",
     "evalue": "Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/workspace/Ensemble_complete_models'. Use `repo_type` argument if needed.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mHFValidationError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[162], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mapi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupload_folder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/workspace/Ensemble_complete_models\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmadhavsinghabcde/ensemble_complete_models\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_in_repo\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m.\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcommit_message\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel and notebook, where new classifiers were not made, complete finetuned models were used.\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m      6\u001b[0m \u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py:106\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m arg_name, arg_value \u001b[38;5;129;01min\u001b[39;00m chain(\n\u001b[1;32m    102\u001b[0m     \u001b[38;5;28mzip\u001b[39m(signature\u001b[38;5;241m.\u001b[39mparameters, args),  \u001b[38;5;66;03m# Args values\u001b[39;00m\n\u001b[1;32m    103\u001b[0m     kwargs\u001b[38;5;241m.\u001b[39mitems(),  \u001b[38;5;66;03m# Kwargs values\u001b[39;00m\n\u001b[1;32m    104\u001b[0m ):\n\u001b[1;32m    105\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m arg_name \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrepo_id\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfrom_id\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto_id\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m--> 106\u001b[0m         \u001b[43mvalidate_repo_id\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    108\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m arg_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtoken\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m arg_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    109\u001b[0m         has_token \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py:154\u001b[0m, in \u001b[0;36mvalidate_repo_id\u001b[0;34m(repo_id)\u001b[0m\n\u001b[1;32m    151\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HFValidationError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRepo id must be a string, not \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(repo_id)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    153\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m repo_id\u001b[38;5;241m.\u001b[39mcount(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 154\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HFValidationError(\n\u001b[1;32m    155\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRepo id must be in the form \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrepo_name\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m or \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnamespace/repo_name\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    156\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m. Use `repo_type` argument if needed.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    157\u001b[0m     )\n\u001b[1;32m    159\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m REPO_ID_REGEX\u001b[38;5;241m.\u001b[39mmatch(repo_id):\n\u001b[1;32m    160\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HFValidationError(\n\u001b[1;32m    161\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRepo id must use alphanumeric chars or \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m--\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m..\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m are\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    162\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m forbidden, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m cannot start or end the name, max length is 96:\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    163\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    164\u001b[0m     )\n",
      "\u001b[0;31mHFValidationError\u001b[0m: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/workspace/Ensemble_complete_models'. Use `repo_type` argument if needed."
     ]
    }
   ],
   "source": [
    "api.upload_folder(\n",
    "    foldef'/workspace/Ensemble_complete_models',\n",
    "    repo_id = \"madhavsinghabcde/ensemble_complete_models\",\n",
    "    path_in_repo = \".\",\n",
    "    commit_message = \"model and notebook, where new classifiers were not made, complete finetuned models were used.\"\n",
    ")"
   ]
  }
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