BAF_xgboost_multicass12122020_0835pm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicción de existencia de penetracion de Banda Ancha fija (fibra óptica o cable coaxial) en municipios\n",
    "\n",
    "El problema a evaluar es si dentro de los municipios hay penetracion de BAF basada en fibra óptica o cable coaxial en municipios, independientemente de su nivel.\n",
    "\n",
    "**Nota:** Se empleó un modelo de regresión logística, XGBoost y Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preámbulo de paquetes a utilizarse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "\n",
    "import xgboost as xgb\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Carga de datos de penetracion BAF y municipios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cargamos los datos en crudo y creamos una copia para trabajar\n",
    "raw_data = pd.read_csv ('BAF_06209_P1.csv')\n",
    "data = raw_data\n",
    "\n",
    "# Elimina columnas de municipio y clases de penetracion (solo estudiaremos si hay o no penetracion de F.O y Cable Coax.)\n",
    "del data['K_ENTIDAD_MUNICIPIO']\n",
    "del data['CLASS_PEN_BAF_HABS_COAXFO'] #\n",
    "del data['NUM_OPS'] #\n",
    "del data['DISP_INTERNET'] #\n",
    "del data['ANALF'] #\n",
    "\n",
    "# Renombramos una columna para que no genere problemas con Scikit Learn\n",
    "data.rename(columns={\"PL<5000\": \"PL5000\"}, inplace=True)\n",
    "\n",
    "# Renombramos una columnas para facilitar manipulacion\n",
    "data.rename(columns={\"ANOS_PROMEDIO_DE_ESCOLARIDAD\": \"AVG_ESCOLARIDAD\"}, inplace=True)\n",
    "data.rename(columns={\"IS_PEN_BAF_HABS_COAXFO\": \"EXIST_PEN\"}, inplace=True)\n",
    "\n",
    "# Sustitumos valores de la columnas NUM_OPS (1= Hay mas de dos operadores, 0 = en otro caso)\n",
    "#data['NUM_OPS'] = np.where(data['NUM_OPS']>1,1,0)\n",
    "\n",
    "# Descartamos los municipios que no poseen indicadores de ingreso anual bruto per capita\n",
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resumen de las variables en estudio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>HOGARES</th>\n",
       "      <th>POBLACION</th>\n",
       "      <th>SUPERFICIE</th>\n",
       "      <th>DENS_HOGS</th>\n",
       "      <th>DENS_HABS</th>\n",
       "      <th>SPRIM</th>\n",
       "      <th>AVG_ESCOLARIDAD</th>\n",
       "      <th>OVSAE</th>\n",
       "      <th>OVSEE</th>\n",
       "      <th>PL5000</th>\n",
       "      <th>PO2SM</th>\n",
       "      <th>INGRESOPC_ANUAL</th>\n",
       "      <th>DISP_TV_PAGA</th>\n",
       "      <th>DISP_TEL_CELULAR</th>\n",
       "      <th>DISP_TEL_FIJO</th>\n",
       "      <th>EXIST_PEN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2446.000000</td>\n",
       "      <td>2.446000e+03</td>\n",
       "      <td>2446.000000</td>\n",
       "      <td>2446.000000</td>\n",
       "      <td>2446.000000</td>\n",
       "      <td>2446.000000</td>\n",
       "      <td>2446.000000</td>\n",
       "      <td>2446.000000</td>\n",
       "      <td>2446.000000</td>\n",
       "      <td>2446.000000</td>\n",
       "      <td>2446.000000</td>\n",
       "      <td>2446.000000</td>\n",
       "      <td>2446.000000</td>\n",
       "      <td>2446.000000</td>\n",
       "      <td>2446.000000</td>\n",
       "      <td>2446.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12991.372036</td>\n",
       "      <td>4.860599e+04</td>\n",
       "      <td>791.321885</td>\n",
       "      <td>7916.018110</td>\n",
       "      <td>7916.018110</td>\n",
       "      <td>29.213050</td>\n",
       "      <td>6.423426</td>\n",
       "      <td>8.651132</td>\n",
       "      <td>2.169366</td>\n",
       "      <td>71.898684</td>\n",
       "      <td>55.381063</td>\n",
       "      <td>1935.145724</td>\n",
       "      <td>31.158202</td>\n",
       "      <td>57.439859</td>\n",
       "      <td>20.204734</td>\n",
       "      <td>0.321341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>38143.144359</td>\n",
       "      <td>1.389142e+05</td>\n",
       "      <td>2104.590464</td>\n",
       "      <td>35001.243252</td>\n",
       "      <td>35001.243252</td>\n",
       "      <td>11.862555</td>\n",
       "      <td>1.767869</td>\n",
       "      <td>11.487796</td>\n",
       "      <td>3.410109</td>\n",
       "      <td>34.685724</td>\n",
       "      <td>16.985484</td>\n",
       "      <td>1020.473455</td>\n",
       "      <td>18.829657</td>\n",
       "      <td>25.072398</td>\n",
       "      <td>14.044259</td>\n",
       "      <td>0.467087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>8.700000e+01</td>\n",
       "      <td>2.210000</td>\n",
       "      <td>4.995331</td>\n",
       "      <td>4.995331</td>\n",
       "      <td>2.490000</td>\n",
       "      <td>1.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.250000</td>\n",
       "      <td>185.290000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1111.750000</td>\n",
       "      <td>4.253000e+03</td>\n",
       "      <td>85.765000</td>\n",
       "      <td>521.867640</td>\n",
       "      <td>521.867640</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>1.492500</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>42.687500</td>\n",
       "      <td>42.930000</td>\n",
       "      <td>1198.730000</td>\n",
       "      <td>15.313693</td>\n",
       "      <td>42.198730</td>\n",
       "      <td>9.047367</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3471.500000</td>\n",
       "      <td>1.340400e+04</td>\n",
       "      <td>233.485000</td>\n",
       "      <td>1387.721595</td>\n",
       "      <td>1387.721595</td>\n",
       "      <td>29.405000</td>\n",
       "      <td>6.280000</td>\n",
       "      <td>4.060000</td>\n",
       "      <td>1.175000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>57.020000</td>\n",
       "      <td>1789.905000</td>\n",
       "      <td>29.018092</td>\n",
       "      <td>64.877177</td>\n",
       "      <td>17.645177</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8877.500000</td>\n",
       "      <td>3.439950e+04</td>\n",
       "      <td>654.895000</td>\n",
       "      <td>3462.189955</td>\n",
       "      <td>3462.189955</td>\n",
       "      <td>37.345000</td>\n",
       "      <td>7.520000</td>\n",
       "      <td>10.865000</td>\n",
       "      <td>2.560000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>68.470000</td>\n",
       "      <td>2447.497500</td>\n",
       "      <td>45.173174</td>\n",
       "      <td>76.445478</td>\n",
       "      <td>29.454433</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>495665.000000</td>\n",
       "      <td>1.827868e+06</td>\n",
       "      <td>53138.790000</td>\n",
       "      <td>598127.340824</td>\n",
       "      <td>598127.340824</td>\n",
       "      <td>71.240000</td>\n",
       "      <td>13.830000</td>\n",
       "      <td>98.880000</td>\n",
       "      <td>57.960000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>94.120000</td>\n",
       "      <td>9748.530000</td>\n",
       "      <td>85.097192</td>\n",
       "      <td>95.002027</td>\n",
       "      <td>84.047612</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             HOGARES     POBLACION    SUPERFICIE      DENS_HOGS  \\\n",
       "count    2446.000000  2.446000e+03   2446.000000    2446.000000   \n",
       "mean    12991.372036  4.860599e+04    791.321885    7916.018110   \n",
       "std     38143.144359  1.389142e+05   2104.590464   35001.243252   \n",
       "min        31.000000  8.700000e+01      2.210000       4.995331   \n",
       "25%      1111.750000  4.253000e+03     85.765000     521.867640   \n",
       "50%      3471.500000  1.340400e+04    233.485000    1387.721595   \n",
       "75%      8877.500000  3.439950e+04    654.895000    3462.189955   \n",
       "max    495665.000000  1.827868e+06  53138.790000  598127.340824   \n",
       "\n",
       "           DENS_HABS        SPRIM  AVG_ESCOLARIDAD        OVSAE        OVSEE  \\\n",
       "count    2446.000000  2446.000000      2446.000000  2446.000000  2446.000000   \n",
       "mean     7916.018110    29.213050         6.423426     8.651132     2.169366   \n",
       "std     35001.243252    11.862555         1.767869    11.487796     3.410109   \n",
       "min         4.995331     2.490000         1.460000     0.000000     0.000000   \n",
       "25%       521.867640    20.500000         5.190000     1.492500     0.490000   \n",
       "50%      1387.721595    29.405000         6.280000     4.060000     1.175000   \n",
       "75%      3462.189955    37.345000         7.520000    10.865000     2.560000   \n",
       "max    598127.340824    71.240000        13.830000    98.880000    57.960000   \n",
       "\n",
       "            PL5000        PO2SM  INGRESOPC_ANUAL  DISP_TV_PAGA  \\\n",
       "count  2446.000000  2446.000000      2446.000000   2446.000000   \n",
       "mean     71.898684    55.381063      1935.145724     31.158202   \n",
       "std      34.685724    16.985484      1020.473455     18.829657   \n",
       "min       0.000000     8.250000       185.290000      0.000000   \n",
       "25%      42.687500    42.930000      1198.730000     15.313693   \n",
       "50%     100.000000    57.020000      1789.905000     29.018092   \n",
       "75%     100.000000    68.470000      2447.497500     45.173174   \n",
       "max     100.000000    94.120000      9748.530000     85.097192   \n",
       "\n",
       "       DISP_TEL_CELULAR  DISP_TEL_FIJO    EXIST_PEN  \n",
       "count       2446.000000    2446.000000  2446.000000  \n",
       "mean          57.439859      20.204734     0.321341  \n",
       "std           25.072398      14.044259     0.467087  \n",
       "min            0.000000       0.000000     0.000000  \n",
       "25%           42.198730       9.047367     0.000000  \n",
       "50%           64.877177      17.645177     0.000000  \n",
       "75%           76.445478      29.454433     1.000000  \n",
       "max           95.002027      84.047612     1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variables de la base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HOGARES',\n",
       " 'POBLACION',\n",
       " 'SUPERFICIE',\n",
       " 'DENS_HOGS',\n",
       " 'DENS_HABS',\n",
       " 'SPRIM',\n",
       " 'AVG_ESCOLARIDAD',\n",
       " 'OVSAE',\n",
       " 'OVSEE',\n",
       " 'PL5000',\n",
       " 'PO2SM',\n",
       " 'INGRESOPC_ANUAL',\n",
       " 'DISP_TV_PAGA',\n",
       " 'DISP_TEL_CELULAR',\n",
       " 'DISP_TEL_FIJO',\n",
       " 'EXIST_PEN']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dimensiones de la base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "m, n = data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matrices de correlación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fadb33d6fd0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = data.corr() \n",
    "  \n",
    "f, ax = plt.subplots(figsize =(9, 8)) \n",
    "sns.heatmap(corrmat, ax = ax, cmap =\"YlGn\", linewidths = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7fada9f976d8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#corrmat = data.corr()   \n",
    "cg = sns.clustermap(corrmat, cmap =\"RdYlBu\", linewidths = 0.1); \n",
    "plt.setp(cg.ax_heatmap.yaxis.get_majorticklabels(), rotation = 0) \n",
    "  \n",
    "cg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EXIST_PEN\n",
       "0    1660\n",
       "1     786\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='EXIST_PEN',data=data,palette='hls')\n",
    "data.groupby(['EXIST_PEN']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparamos los datos para entrenamiento y prueba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cesar/.local/lib/python3.7/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "seed = 721 # Seed para poder replicar\n",
    "X_train, X_test, y_train, y_test = train_test_split(data.iloc[:, :n-1], data.iloc[:, n-1], random_state=seed)\n",
    "\n",
    "xg_train = xgb.DMatrix(X_train, label=y_train)\n",
    "xg_test = xgb.DMatrix(X_test, label=y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelo con regresion logística"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lr = LogisticRegression(random_state=seed,solver='lbfgs', max_iter=10000, class_weight = \"balanced\").fit(preprocessing.scale(X_train), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_model_lr_predicted = model_lr.predict(preprocessing.scale(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8676470588235294\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.91      0.90       387\n",
      "           1       0.84      0.79      0.81       225\n",
      "\n",
      "    accuracy                           0.87       612\n",
      "   macro avg       0.86      0.85      0.86       612\n",
      "weighted avg       0.87      0.87      0.87       612\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(91.68, 0.5, 'Valor real')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2rsPX/3yH/w6dyDnDbqH5zEfBjFUDx5C1ZSebZy0nrVY1mr45EID9u3/gw77DIddf//yseeoNzv3HHVTv2ZYfvt3MB73/CsAZ/bpSvHwadR65CQCXvZ8FHfuHdOTHjjl37EfsZjYKeM45NzePdWOccz0Le40T7VTiRDepfMuoS5Aj0Gn9OMurPZQRg3OuVwHrCg0FEYmW3q4UEY+CQUQ8CgYR8SgYRMSjYBARj4JBRDwKBhHxKBhExKNgEBGPgkFEPAoGEfEoGETEo2AQEY+CQUQ8CgYR8SgYRMSjYBARj4JBRDwKBhHxKBhExKNgEBGPgkFEPAoGEfEoGETEo2AQEY+CQUQ8CgYR8SgYRMSjYBARj4JBRDwKBhHxKBhExKNgEBGPgkFEPAoGEfEoGETEo2AQEY+CQUQ8CgYR8SgYRMSjYBARj4JBRDwKBhHxKBhExKNgEBGPgkFEPAoGEfEoGETEo2AQEY8556Ku4aRjZn2ccyOirkPiczL+vjRiiEafqAuQH+Wk+30pGETEo2AQEY+CIRon1fnqCeCk+31p8lFEPBoxiIhHwSAiHgVDAplZJzNbZWarzezeqOuRgpnZaDPbYGYfRV1LoikYEsTMkoHhQGegDtDDzOpEW5UU4nmgU9RFREHBkDgXAKudc2ucc/uAcUCXiGuSAjjnZgNboq4jCgqGxKkGfJ3r+TdBm0iRo2BIHMujTe8VS5GkYEicb4Cf5HpeHfguolpECqRgSJzFwFlmdrqZFQeuAf4dcU0ieVIwJIhzLhu4DZgKfAqMd859HG1VUhAzGwssAM42s2/MrFfUNSWKLokWEY9GDCLiUTCIiEfBICIeBYOIeBQMIuJRMBzHzGymmXU8rO0OM3umkP12hVvZj2NmX5pZRrA8v5BtZ5pZ48RUdvJSMBzfxhK7UCq3a4L2YyL4VOiR7JdyJPs55y48kv3k2FIwHN9eBS41sxIAZlYDqArMNbM0M5tuZsvMbIWZeZ/ktJjHzOyjYJvuQXsbM5thZmOAFXnst8vMhgSvPd3MKgXtM83sITObBdxuZpXMbIKZLQ4ezYPtKprZNDN738z+Tq7PkeQezZjZ74O6PjSzwblKuMrMFpnZZ2bWMtg21cyeC7Z/38zaHuV/25Obc06P4/gBTAa6BMv3Ao8FyylA2WA5A1jN/y5o2xX87Aq8DSQDlYGvgCpAG2A3cHo+fTrg2mB5APB0sDwTeCbXdmOAFsHyacCnwfIwYECwfEnwehmH1dYZmA+UCp5XyNXHkGD5YuCdYLkf8FywXDs4ltSofz/H6+OIhntSpBw4nXgj+HlT0G7AQ2bWCsgh9hHvysC6XPu2AMY65/YD64O/9OcDO4BFzrkv8ukzB3g5WP4XMDHXupdzLV8E1DE7OCAoa2ZlgFbAlQDOuclmtjWPPi4i9g99T7Bd7vsiHOhvKVAj17E8FWy70szWArWA5fkcgxRAwXD8ex0YamYNgZLOuWVB+7VAJaCRcy7LzL4EUg/bN6+Pgh+w+0fUkPu6+tz7JQHNnHN7D+k0FhSFXYtvBWyTGfzcz//+Hy7oWORH0hzDcc45t4vY8Ho0h046pgMbglBoC/w0j91nA93NLDmYJ2gFLIqj2ySgW7DcE5ibz3bTiH1wDAAzOzdXv9cGbZ2B8vnse5OZlQq2q1BITblfsxaxU5dVhR2I5E3BcGIYCzQgdru4A14CGpvZEmL/YFbmsd9rxIbaHwLvAr93zq3LY7vD7QbqmtlSoB3wYD7b/TaoYbmZfQL8Omh/AGhlZsuADsTmAw7hnJtC7GPpS8zsA+CuQmp6Bkg2sxXETmdudM5lFrKP5EOfrpQfzcx2OefSoq5DwqMRg4h4NGIQEY9GDCLiUTCIiEfBICIeBYOIeBQMIuL5f8bbYWvz1zj6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Accuracy: {}\".format(accuracy_score(y_test, y_model_lr_predicted)))\n",
    "\n",
    "## Métricas clasicas de Clasificadores\n",
    "from sklearn import metrics\n",
    "print(metrics.classification_report(y_model_lr_predicted, y_test))\n",
    "\n",
    "## Matriz de confusión\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "mat_lr = confusion_matrix(y_test, y_model_lr_predicted)\n",
    "\n",
    "plt.close()\n",
    "\n",
    "sns.heatmap(mat_lr, square = True, annot = True, cbar = False)\n",
    "plt.xlabel('Valor predicho')\n",
    "plt.ylabel('Valor real')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelo con XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 6 folds for each of 27 candidates, totalling 162 fits\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.3s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.5s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.3s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.9s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.4s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.5s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.9s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.9s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.9s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.5s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.9s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.5s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.3s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   2.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   2.7s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.6s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.5s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.4s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.3s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.5s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   2.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.5s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.4s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   2.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.6s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.5s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   2.3s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   2.4s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   2.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.5s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   2.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.01, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.8s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.3s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.4s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.4s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.9s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.9s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.9s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.9s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.9s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.9s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.4s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.9s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   2.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   3.5s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   2.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.4s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.4s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.05, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.4s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.0s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=6, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=7, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=10, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   0.3s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.1s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   1.2s\n",
      "[CV] colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5 \n",
      "[CV]  colsample_bytree=1.0, eta=0.1, gamma=0.5, max_depth=8, n_estimators=100, nthread=10, num_class=2, objective=multi:softmax, seed=27, silent=1, subsample=0.5, total=   3.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 162 out of 162 | elapsed:  2.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=6, error_score='raise-deprecating',\n",
       "             estimator=XGBClassifier(base_score=0.5, booster='gbtree',\n",
       "                                     colsample_bylevel=1, colsample_bynode=1,\n",
       "                                     colsample_bytree=1, gamma=0,\n",
       "                                     learning_rate=0.1, max_delta_step=0,\n",
       "                                     max_depth=3, min_child_weight=1,\n",
       "                                     missing=None, n_estimators=100, n_jobs=1,\n",
       "                                     nthread=None, objective='binary:logistic',\n",
       "                                     random_state=0, reg_alpha=0, reg_l...\n",
       "                                     subsample=1, verbosity=1),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'colsample_bytree': [1.0], 'eta': [0.01, 0.05, 0.1],\n",
       "                         'gamma': [0.5], 'max_depth': [6, 7, 8],\n",
       "                         'n_estimators': [10, 100, 100], 'nthread': [10],\n",
       "                         'num_class': [2], 'objective': ['multi:softmax'],\n",
       "                         'seed': [27], 'silent': [1], 'subsample': [0.5]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=2)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Instanciamos el clasificador\n",
    "classifier_xgb = xgb.XGBClassifier() \n",
    "\n",
    "# Hiper-parametros para hacer el grid search\n",
    "hyper_param_grid = {\n",
    "    'objective':['multi:softmax'],\n",
    "    'eta':[0.01, 0.05,0.1], # Tasa de aprendizaje\n",
    "    'gamma': [0.5],\n",
    "    'max_depth':[6,7,8],\n",
    "    'subsample':[0.8],\n",
    "    'silent':[1],\n",
    "    'nthread':[10],\n",
    "    'num_class':[2],\n",
    "    'subsample':[0.5],\n",
    "    'colsample_bytree':[1.0],\n",
    "    'n_estimators': [10, 100, 100], #number of trees, change it to 1000 for better results\n",
    "    'seed':[27]\n",
    "    }\n",
    "\n",
    "model_gsearch_xgb = GridSearchCV(classifier_xgb, hyper_param_grid, cv = 6, verbose = 2)\n",
    "\n",
    "model_gsearch_xgb.fit(X_train, y_train, eval_metric='auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': 1.0,\n",
       " 'eta': 0.01,\n",
       " 'gamma': 0.5,\n",
       " 'max_depth': 6,\n",
       " 'n_estimators': 100,\n",
       " 'nthread': 10,\n",
       " 'num_class': 2,\n",
       " 'objective': 'multi:softmax',\n",
       " 'seed': 27,\n",
       " 'silent': 1,\n",
       " 'subsample': 0.5}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_gsearch_xgb.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8816793893129771"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_gsearch_xgb.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8627450980392157\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.87      0.90       424\n",
      "           1       0.75      0.84      0.79       188\n",
      "\n",
      "    accuracy                           0.86       612\n",
      "   macro avg       0.84      0.86      0.84       612\n",
      "weighted avg       0.87      0.86      0.86       612\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(91.68, 0.5, 'Valor real')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_model_xgb = model_gsearch_xgb.predict(X_test)\n",
    "## Accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Accuracy: {}\".format(accuracy_score(y_test, y_model_xgb)))\n",
    "\n",
    "## Métricas clasicas de Clasificadores\n",
    "from sklearn import metrics\n",
    "print(metrics.classification_report(y_model_xgb, y_test))\n",
    "\n",
    "## Matriz de confusión\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "mat = confusion_matrix(y_test, y_model_xgb)\n",
    "\n",
    "plt.close()\n",
    "\n",
    "sns.heatmap(mat, square = True, annot = True, cbar = False)\n",
    "plt.xlabel('Valor predicho')\n",
    "plt.ylabel('Valor real')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How the importance is calculated: either \"weight\", \"gain\", or \"cover\" \n",
    "#    \"weight\" is the number of times a feature appears in a tree \n",
    "#    \"gain\" is the average\"gain\"of splits which use the feature \n",
    "#    \"cover\" is the average coverage of splits which use the feature\n",
    "#      where coverage is defined as the number of samples affected by the split\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "xgb.plot_importance(model_gsearch_xgb.best_estimator_, ax=ax, importance_type='weight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How the importance is calculated: either \"weight\", \"gain\", or \"cover\" \n",
    "#    \"weight\" is the number of times a feature appears in a tree \n",
    "#    \"gain\" is the average\"gain\"of splits which use the feature \n",
    "#    \"cover\" is the average coverage of splits which use the feature\n",
    "#      where coverage is defined as the number of samples affected by the split\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "xgb.plot_importance(model_gsearch_xgb.best_estimator_, ax=ax, importance_type='gain')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How the importance is calculated: either \"weight\", \"gain\", or \"cover\" \n",
    "#    \"weight\" is the number of times a feature appears in a tree \n",
    "#    \"gain\" is the average\"gain\"of splits which use the feature \n",
    "#    \"cover\" is the average coverage of splits which use the feature\n",
    "#      where coverage is defined as the number of samples affected by the split\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "xgb.plot_importance(model_gsearch_xgb.best_estimator_, ax=ax, importance_type='cover')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shap\n",
    "\n",
    "# Create object that can calculate shap values\n",
    "explainer = shap.TreeExplainer(model_gsearch_xgb.best_estimator_)\n",
    "\n",
    "# Calculate shap_values for all of val_X rather than a single row, to have more data for plot.\n",
    "shap_values = explainer.shap_values(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values, X_test) # Plot de los que no tienen conectividad de BAF (f.o. + coaxial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x540 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make plot. Index of [0] is explained in text below.\n",
    "shap.summary_plot(shap_values[0], X_test) # Plot de los que no tienen conectividad de BAF (f.o. + coaxial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x540 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values[1], X_test) # Plot de los que si tienen conectividad de BAF (f.o. + coaxial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://www.kaggle.com/dansbecker/advanced-uses-of-shap-values\n",
    "\n",
    "shap.dependence_plot('DENS_HOGS', shap_values[1], X_test, interaction_index=\"INGRESOPC_ANUAL\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 6 folds for each of 90 candidates, totalling 540 fits\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.827, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.843, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.863, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.905, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.823, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.849, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.853, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.879, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.889, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.908, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.872, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.879, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.866, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.925, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.866, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.872, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.827, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.824, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.863, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.905, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.826, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.856, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.873, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.895, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.908, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.879, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.875, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.859, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.863, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.925, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.872, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.872, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.827, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.824, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.866, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.905, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.826, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.853, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.869, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.886, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.918, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.879, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.863, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.866, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.922, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.875, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.872, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.827, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.843, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.863, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.905, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.823, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.849, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.853, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.879, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.889, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.908, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.872, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.879, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.866, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.925, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.866, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.872, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.827, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.824, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.863, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.905, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.826, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.856, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.873, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.895, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.908, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.879, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.875, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.859, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.863, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.925, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.872, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.872, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.827, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.824, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.866, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.905, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.826, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.853, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.869, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.886, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.918, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.879, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.863, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.866, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.922, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.875, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=5, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.872, total=   0.4s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.846, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.833, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.817, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.863, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.830, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.859, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.889, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.922, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.885, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.866, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.866, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.866, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.925, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.875, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.885, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.801, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.833, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.810, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.863, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.830, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.823, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.859, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.876, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.869, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.925, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.879, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.879, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.882, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.863, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.918, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.875, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.895, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.827, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.843, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.837, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.889, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.820, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.843, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.886, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.869, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.846, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.915, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.879, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.872, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.876, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.876, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.925, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.882, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.846, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.833, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.817, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.863, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.830, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.859, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.889, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.922, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.885, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.866, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.866, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.866, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.925, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.875, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.885, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.801, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.833, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.810, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.863, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.830, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.823, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.859, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.876, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.869, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.925, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.879, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.879, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.882, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.863, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.918, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.875, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.895, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.827, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.843, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.837, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.889, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.820, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.843, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.886, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.869, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.846, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.915, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.879, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.872, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.876, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.876, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.925, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=10, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.882, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.824, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.833, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.830, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.889, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.836, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.836, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.856, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.876, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.935, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.866, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.879, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.859, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.931, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.892, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.827, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.827, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.846, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.892, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.863, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.873, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.866, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.908, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.875, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.889, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.859, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.925, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.875, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.895, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.833, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.843, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.817, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.846, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.793, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.823, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.863, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.876, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.869, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.918, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.889, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.885, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.876, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.915, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.879, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.882, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.824, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.833, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.830, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.889, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.836, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.836, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.856, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.876, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.935, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.866, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.879, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.859, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.931, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.892, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.827, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.827, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.846, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.892, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.863, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.873, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.866, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.908, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.875, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.889, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.859, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.925, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.875, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.895, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.833, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.843, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.817, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.846, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.793, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.823, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.863, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.876, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.869, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.918, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.889, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.885, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.876, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.915, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.879, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=20, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.882, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.824, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.833, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.830, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.889, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.836, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.836, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.859, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.876, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.935, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.866, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.876, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.879, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.859, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.931, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.892, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.827, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.807, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.846, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.876, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.873, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.873, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.866, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.908, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.875, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.889, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.859, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.928, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.875, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.895, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.833, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.843, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.817, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.846, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.793, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.823, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.863, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.876, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.869, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.918, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.889, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.885, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.876, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.915, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.879, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.882, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.824, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.833, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.830, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.889, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.836, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.836, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.859, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.876, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.935, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.866, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.876, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.879, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.859, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.931, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.892, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.827, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.807, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.846, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.876, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.873, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.873, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.866, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.908, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.875, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.889, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.859, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.928, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.875, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.895, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.833, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.843, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.817, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.846, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.793, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.823, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.863, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.876, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.869, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.918, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.889, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.885, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.876, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.915, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.879, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=50, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.882, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.824, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.833, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.830, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.889, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.836, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.836, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.859, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.876, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.935, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.866, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.876, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.879, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.859, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.931, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.892, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.827, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.807, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.846, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.876, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.873, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.873, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.866, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.908, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.875, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.889, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.859, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.928, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.875, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.895, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.833, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.843, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.817, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.846, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.793, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.823, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.863, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.876, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.869, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.918, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.889, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.885, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.876, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.915, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.879, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=sqrt, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.882, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.824, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.833, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.830, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.889, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.836, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=1, random_state=712, warm_start=TRUE, score=0.836, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.859, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.876, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.935, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.866, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=10, random_state=712, warm_start=TRUE, score=0.882, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.876, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.879, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.859, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.931, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=2, n_estimators=100, random_state=712, warm_start=TRUE, score=0.892, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.827, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.807, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.846, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.876, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=1, random_state=712, warm_start=TRUE, score=0.852, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.873, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.873, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.866, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.908, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.875, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=10, random_state=712, warm_start=TRUE, score=0.889, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.859, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.928, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.875, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=5, n_estimators=100, random_state=712, warm_start=TRUE, score=0.895, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.833, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.843, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.817, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.846, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.793, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=1, random_state=712, warm_start=TRUE, score=0.823, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.863, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.876, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.869, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.918, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.889, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=10, random_state=712, warm_start=TRUE, score=0.885, total=   0.1s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.869, total=   0.7s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.876, total=   0.6s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.873, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.915, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.879, total=   0.5s\n",
      "[CV] criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE \n",
      "[CV]  criterion=entropy, max_depth=100, max_features=log2, min_samples_split=10, n_estimators=100, random_state=712, warm_start=TRUE, score=0.882, total=   0.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 540 out of 540 | elapsed:  1.8min finished\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "rf_classifier = RandomForestClassifier() \n",
    "hyper_param_grid = {'n_estimators': [1,10,100],#[1,10,100,1000,10000], \n",
    "                    'max_depth': [5,10,20,50,100],#5,10,20,50,100], \n",
    "                    'max_features': ['sqrt','log2'],\n",
    "                    'min_samples_split': [2,5,10],\n",
    "                    'criterion':['entropy'],\n",
    "                    #'class_weight':['balanced'],\n",
    "                    'warm_start' :['TRUE'],\n",
    "                    'random_state':[712]\n",
    "                   },\n",
    "\n",
    "\n",
    "model_RF_gsearch = GridSearchCV(rf_classifier, hyper_param_grid, cv = 6, verbose = 3)\n",
    "\n",
    "model_RF_gsearch.fit(X_train, y_train)\n",
    "\n",
    "y_model_forest = model_RF_gsearch.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy',\n",
       " 'max_depth': 10,\n",
       " 'max_features': 'sqrt',\n",
       " 'min_samples_split': 2,\n",
       " 'n_estimators': 10,\n",
       " 'random_state': 712,\n",
       " 'warm_start': 'TRUE'}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_RF_gsearch.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8865866957470011"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_RF_gsearch.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8643790849673203\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.87      0.90       425\n",
      "           1       0.75      0.84      0.79       187\n",
      "\n",
      "    accuracy                           0.86       612\n",
      "   macro avg       0.84      0.86      0.85       612\n",
      "weighted avg       0.87      0.86      0.87       612\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(91.68, 0.5, 'Valor real')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Accuracy: {}\".format(accuracy_score(y_test, y_model_forest)))\n",
    "\n",
    "## Métricas clasicas de Clasificadores\n",
    "from sklearn import metrics\n",
    "print(metrics.classification_report(y_model_forest, y_test))\n",
    "\n",
    "## Matriz de confusión\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "mat = confusion_matrix(y_test, y_model_forest)\n",
    "\n",
    "plt.close()\n",
    "\n",
    "sns.heatmap(mat, square = True, annot = True, cbar = False)\n",
    "plt.xlabel('Valor predicho')\n",
    "plt.ylabel('Valor real')"
   ]
  }
 ],
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