random_forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d25443f-a253-46cc-8400-0446df896f64",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "np.set_printoptions(threshold=sys.maxsize)\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_colwidth', 1000)\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from scipy.stats import uniform"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d4c3251-3127-4680-abb3-5af813b17555",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/competitions/icr-identify-age-related-conditions/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8b580034-3bdb-4aca-a267-4b6a899a1866",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(617, 57)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('train.csv', index_col=0)\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "812fcae1-fa3c-4813-bb77-04eb0f52c0f8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = train.drop(columns = [\"Class\"])\n",
    "y = train[\"Class\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29cc241-bded-473a-a51d-d59016274457",
   "metadata": {},
   "source": [
    "Vamos seguir o exemplo de código encontrado em https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8c6fefde-66d1-47e3-8654-70a7a8e4398a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "numeric_features = X.select_dtypes(include=['float']).columns.to_list()\n",
    "numeric_transformer = Pipeline(\n",
    "    steps=[\n",
    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
    "        (\"scaler\", StandardScaler()),\n",
    "    ]\n",
    ")\n",
    "\n",
    "categorical_features = X.select_dtypes(include=['object']).columns.to_list()\n",
    "categorical_transformer = Pipeline(\n",
    "    steps=[\n",
    "        (\"encoder\", OneHotEncoder(drop='first')),\n",
    "    ]\n",
    ")\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\"num\", numeric_transformer, numeric_features),\n",
    "        (\"cat\", categorical_transformer, categorical_features),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "acc1f00c-8bdf-4350-9835-ad3dd7040815",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "clf = Pipeline(\n",
    "    steps=[\n",
    "        (\"preprocessor\", preprocessor),\n",
    "        (\"forest\", RandomForestClassifier())\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e49a707f-1cbe-43b0-b884-524c0d858bc0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;AB&#x27;, &#x27;AF&#x27;, &#x27;AH&#x27;, &#x27;AM&#x27;, &#x27;AR&#x27;,\n",
       "                                                   &#x27;AX&#x27;, &#x27;AY&#x27;, &#x27;AZ&#x27;, &#x27;BC&#x27;,\n",
       "                                                   &#x27;BD &#x27;, &#x27;BN&#x27;, &#x27;BP&#x27;, &#x27;BQ&#x27;,\n",
       "                                                   &#x27;BR&#x27;, &#x27;BZ&#x27;, &#x27;CB&#x27;, &#x27;CC&#x27;,\n",
       "                                                   &#x27;CD &#x27;, &#x27;CF&#x27;, &#x27;CH&#x27;, &#x27;CL&#x27;,\n",
       "                                                   &#x27;CR&#x27;, &#x27;CS&#x27;, &#x27;CU&#x27;, &#x27;CW &#x27;,\n",
       "                                                   &#x27;DA&#x27;, &#x27;DE&#x27;, &#x27;DF&#x27;, &#x27;DH&#x27;, &#x27;DI&#x27;, ...]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;encoder&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                                  [&#x27;EJ&#x27;])])),\n",
       "                (&#x27;forest&#x27;, RandomForestClassifier())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;AB&#x27;, &#x27;AF&#x27;, &#x27;AH&#x27;, &#x27;AM&#x27;, &#x27;AR&#x27;,\n",
       "                                                   &#x27;AX&#x27;, &#x27;AY&#x27;, &#x27;AZ&#x27;, &#x27;BC&#x27;,\n",
       "                                                   &#x27;BD &#x27;, &#x27;BN&#x27;, &#x27;BP&#x27;, &#x27;BQ&#x27;,\n",
       "                                                   &#x27;BR&#x27;, &#x27;BZ&#x27;, &#x27;CB&#x27;, &#x27;CC&#x27;,\n",
       "                                                   &#x27;CD &#x27;, &#x27;CF&#x27;, &#x27;CH&#x27;, &#x27;CL&#x27;,\n",
       "                                                   &#x27;CR&#x27;, &#x27;CS&#x27;, &#x27;CU&#x27;, &#x27;CW &#x27;,\n",
       "                                                   &#x27;DA&#x27;, &#x27;DE&#x27;, &#x27;DF&#x27;, &#x27;DH&#x27;, &#x27;DI&#x27;, ...]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;encoder&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                                  [&#x27;EJ&#x27;])])),\n",
       "                (&#x27;forest&#x27;, RandomForestClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;AB&#x27;, &#x27;AF&#x27;, &#x27;AH&#x27;, &#x27;AM&#x27;, &#x27;AR&#x27;, &#x27;AX&#x27;, &#x27;AY&#x27;,\n",
       "                                  &#x27;AZ&#x27;, &#x27;BC&#x27;, &#x27;BD &#x27;, &#x27;BN&#x27;, &#x27;BP&#x27;, &#x27;BQ&#x27;, &#x27;BR&#x27;,\n",
       "                                  &#x27;BZ&#x27;, &#x27;CB&#x27;, &#x27;CC&#x27;, &#x27;CD &#x27;, &#x27;CF&#x27;, &#x27;CH&#x27;, &#x27;CL&#x27;,\n",
       "                                  &#x27;CR&#x27;, &#x27;CS&#x27;, &#x27;CU&#x27;, &#x27;CW &#x27;, &#x27;DA&#x27;, &#x27;DE&#x27;, &#x27;DF&#x27;,\n",
       "                                  &#x27;DH&#x27;, &#x27;DI&#x27;, ...]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;encoder&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                 [&#x27;EJ&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;AB&#x27;, &#x27;AF&#x27;, &#x27;AH&#x27;, &#x27;AM&#x27;, &#x27;AR&#x27;, &#x27;AX&#x27;, &#x27;AY&#x27;, &#x27;AZ&#x27;, &#x27;BC&#x27;, &#x27;BD &#x27;, &#x27;BN&#x27;, &#x27;BP&#x27;, &#x27;BQ&#x27;, &#x27;BR&#x27;, &#x27;BZ&#x27;, &#x27;CB&#x27;, &#x27;CC&#x27;, &#x27;CD &#x27;, &#x27;CF&#x27;, &#x27;CH&#x27;, &#x27;CL&#x27;, &#x27;CR&#x27;, &#x27;CS&#x27;, &#x27;CU&#x27;, &#x27;CW &#x27;, &#x27;DA&#x27;, &#x27;DE&#x27;, &#x27;DF&#x27;, &#x27;DH&#x27;, &#x27;DI&#x27;, &#x27;DL&#x27;, &#x27;DN&#x27;, &#x27;DU&#x27;, &#x27;DV&#x27;, &#x27;DY&#x27;, &#x27;EB&#x27;, &#x27;EE&#x27;, &#x27;EG&#x27;, &#x27;EH&#x27;, &#x27;EL&#x27;, &#x27;EP&#x27;, &#x27;EU&#x27;, &#x27;FC&#x27;, &#x27;FD &#x27;, &#x27;FE&#x27;, &#x27;FI&#x27;, &#x27;FL&#x27;, &#x27;FR&#x27;, &#x27;FS&#x27;, &#x27;GB&#x27;, &#x27;GE&#x27;, &#x27;GF&#x27;, &#x27;GH&#x27;, &#x27;GI&#x27;, &#x27;GL&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;EJ&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['AB', 'AF', 'AH', 'AM', 'AR',\n",
       "                                                   'AX', 'AY', 'AZ', 'BC',\n",
       "                                                   'BD ', 'BN', 'BP', 'BQ',\n",
       "                                                   'BR', 'BZ', 'CB', 'CC',\n",
       "                                                   'CD ', 'CF', 'CH', 'CL',\n",
       "                                                   'CR', 'CS', 'CU', 'CW ',\n",
       "                                                   'DA', 'DE', 'DF', 'DH', 'DI', ...]),\n",
       "                                                 ('cat',\n",
       "                                                  Pipeline(steps=[('encoder',\n",
       "                                                                   OneHotEncoder(drop='first'))]),\n",
       "                                                  ['EJ'])])),\n",
       "                ('forest', RandomForestClassifier())])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "14831c5d-7df0-46f2-b68a-0ae0d223d01c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'forest__n_estimators': [1000],\n",
    "    'forest__criterion': ['log_loss', 'gini'],\n",
    "    'forest__max_depth': [None],\n",
    "    'forest__min_samples_split': [2],\n",
    "    'forest__min_samples_leaf': [1],\n",
    "    'forest__min_weight_fraction_leaf': [0.0],\n",
    "    'forest__max_features': uniform(),\n",
    "    'forest__max_leaf_nodes': [None],\n",
    "    'forest__min_impurity_decrease': [0.0],\n",
    "    'forest__bootstrap': [True],\n",
    "    'forest__oob_score': [False],\n",
    "    'forest__n_jobs': [-1],\n",
    "    'forest__random_state': [42],\n",
    "    'forest__verbose': [10],\n",
    "    'forest__warm_start': [True],\n",
    "    'forest__class_weight': [{0: 1, 1: 1}, {0: 1, 1: 2}],\n",
    "    'forest__ccp_alpha': [0.0],\n",
    "    'forest__max_samples': [None],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b6b89e92-e3dd-475c-8fdf-5b643bd089a1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "metrics = ['accuracy', 'f1', 'neg_log_loss', 'precision', 'recall', 'roc_auc',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b1e36ef5-8801-469d-a368-6dc5b70608bf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "grid = RandomizedSearchCV(clf,\n",
    "                    param_distributions=param_grid,\n",
    "                    n_iter = 50,\n",
    "                    scoring=metrics,\n",
    "                    random_state = 42,\n",
    "                    n_jobs = -1,\n",
    "                    verbose=10,\n",
    "                    refit=\"neg_log_loss\",\n",
    "                    return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dcffc9ac-342f-4e24-ab8a-84ecf61f86a3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 16 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   9 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  29 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  40 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  53 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  66 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  81 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  96 tasks      | elapsed:    0.0s\n",
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    },
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     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 936 tasks      | elapsed:    1.0s\n",
      "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:    1.1s finished\n"
     ]
    },
    {
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       "                                              ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                                SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                               (&#x27;scaler&#x27;,\n",
       "                                                                                                StandardScaler())]),\n",
       "                                                                               [&#x27;AB&#x27;,\n",
       "                                                                                &#x27;AF&#x27;,\n",
       "                                                                                &#x27;AH&#x27;,\n",
       "                                                                                &#x27;AM&#x27;,\n",
       "                                                                                &#x27;AR&#x27;,\n",
       "                                                                                &#x27;AX&#x27;,\n",
       "                                                                                &#x27;AY&#x27;,\n",
       "                                                                                &#x27;AZ&#x27;,\n",
       "                                                                                &#x27;BC&#x27;,\n",
       "                                                                                &#x27;BD &#x27;,\n",
       "                                                                                &#x27;BN&#x27;,\n",
       "                                                                                &#x27;BP&#x27;,\n",
       "                                                                                &#x27;BQ&#x27;,\n",
       "                                                                                &#x27;BR&#x27;,\n",
       "                                                                                &#x27;BZ&#x27;,\n",
       "                                                                                &#x27;CB&#x27;,\n",
       "                                                                                &#x27;CC&#x27;,\n",
       "                                                                                &#x27;CD &#x27;,\n",
       "                                                                                &#x27;CF&#x27;,\n",
       "                                                                                &#x27;CH&#x27;,\n",
       "                                                                                &#x27;CL&#x27;,\n",
       "                                                                                &#x27;CR&#x27;,\n",
       "                                                                                &#x27;CS&#x27;,\n",
       "                                                                                &#x27;CU&#x27;,\n",
       "                                                                                &#x27;CW &#x27;,\n",
       "                                                                                &#x27;DA&#x27;,\n",
       "                                                                                &#x27;DE&#x27;,\n",
       "                                                                                &#x27;DF&#x27;,\n",
       "                                                                                &#x27;DH&#x27;,\n",
       "                                                                                &#x27;DI&#x27;, ...]),\n",
       "                                                                              (&#x27;...\n",
       "                                        &#x27;forest__min_samples_split&#x27;: [2],\n",
       "                                        &#x27;forest__min_weight_fraction_leaf&#x27;: [0.0],\n",
       "                                        &#x27;forest__n_estimators&#x27;: [1000],\n",
       "                                        &#x27;forest__n_jobs&#x27;: [-1],\n",
       "                                        &#x27;forest__oob_score&#x27;: [False],\n",
       "                                        &#x27;forest__random_state&#x27;: [42],\n",
       "                                        &#x27;forest__verbose&#x27;: [10],\n",
       "                                        &#x27;forest__warm_start&#x27;: [True]},\n",
       "                   random_state=42, refit=&#x27;neg_log_loss&#x27;,\n",
       "                   return_train_score=True,\n",
       "                   scoring=[&#x27;accuracy&#x27;, &#x27;f1&#x27;, &#x27;neg_log_loss&#x27;, &#x27;precision&#x27;,\n",
       "                            &#x27;recall&#x27;, &#x27;roc_auc&#x27;],\n",
       "                   verbose=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                                              ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                                SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                                               (&#x27;scaler&#x27;,\n",
       "                                                                                                StandardScaler())]),\n",
       "                                                                               [&#x27;AB&#x27;,\n",
       "                                                                                &#x27;AF&#x27;,\n",
       "                                                                                &#x27;AH&#x27;,\n",
       "                                                                                &#x27;AM&#x27;,\n",
       "                                                                                &#x27;AR&#x27;,\n",
       "                                                                                &#x27;AX&#x27;,\n",
       "                                                                                &#x27;AY&#x27;,\n",
       "                                                                                &#x27;AZ&#x27;,\n",
       "                                                                                &#x27;BC&#x27;,\n",
       "                                                                                &#x27;BD &#x27;,\n",
       "                                                                                &#x27;BN&#x27;,\n",
       "                                                                                &#x27;BP&#x27;,\n",
       "                                                                                &#x27;BQ&#x27;,\n",
       "                                                                                &#x27;BR&#x27;,\n",
       "                                                                                &#x27;BZ&#x27;,\n",
       "                                                                                &#x27;CB&#x27;,\n",
       "                                                                                &#x27;CC&#x27;,\n",
       "                                                                                &#x27;CD &#x27;,\n",
       "                                                                                &#x27;CF&#x27;,\n",
       "                                                                                &#x27;CH&#x27;,\n",
       "                                                                                &#x27;CL&#x27;,\n",
       "                                                                                &#x27;CR&#x27;,\n",
       "                                                                                &#x27;CS&#x27;,\n",
       "                                                                                &#x27;CU&#x27;,\n",
       "                                                                                &#x27;CW &#x27;,\n",
       "                                                                                &#x27;DA&#x27;,\n",
       "                                                                                &#x27;DE&#x27;,\n",
       "                                                                                &#x27;DF&#x27;,\n",
       "                                                                                &#x27;DH&#x27;,\n",
       "                                                                                &#x27;DI&#x27;, ...]),\n",
       "                                                                              (&#x27;...\n",
       "                                        &#x27;forest__min_samples_split&#x27;: [2],\n",
       "                                        &#x27;forest__min_weight_fraction_leaf&#x27;: [0.0],\n",
       "                                        &#x27;forest__n_estimators&#x27;: [1000],\n",
       "                                        &#x27;forest__n_jobs&#x27;: [-1],\n",
       "                                        &#x27;forest__oob_score&#x27;: [False],\n",
       "                                        &#x27;forest__random_state&#x27;: [42],\n",
       "                                        &#x27;forest__verbose&#x27;: [10],\n",
       "                                        &#x27;forest__warm_start&#x27;: [True]},\n",
       "                   random_state=42, refit=&#x27;neg_log_loss&#x27;,\n",
       "                   return_train_score=True,\n",
       "                   scoring=[&#x27;accuracy&#x27;, &#x27;f1&#x27;, &#x27;neg_log_loss&#x27;, &#x27;precision&#x27;,\n",
       "                            &#x27;recall&#x27;, &#x27;roc_auc&#x27;],\n",
       "                   verbose=10)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;AB&#x27;, &#x27;AF&#x27;, &#x27;AH&#x27;, &#x27;AM&#x27;, &#x27;AR&#x27;,\n",
       "                                                   &#x27;AX&#x27;, &#x27;AY&#x27;, &#x27;AZ&#x27;, &#x27;BC&#x27;,\n",
       "                                                   &#x27;BD &#x27;, &#x27;BN&#x27;, &#x27;BP&#x27;, &#x27;BQ&#x27;,\n",
       "                                                   &#x27;BR&#x27;, &#x27;BZ&#x27;, &#x27;CB&#x27;, &#x27;CC&#x27;,\n",
       "                                                   &#x27;CD &#x27;, &#x27;CF&#x27;, &#x27;CH&#x27;, &#x27;CL&#x27;,\n",
       "                                                   &#x27;CR&#x27;, &#x27;CS&#x27;, &#x27;CU&#x27;, &#x27;CW &#x27;,\n",
       "                                                   &#x27;DA&#x27;, &#x27;DE&#x27;, &#x27;DF&#x27;, &#x27;DH&#x27;, &#x27;DI&#x27;, ...]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;encoder&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                                  [&#x27;EJ&#x27;])])),\n",
       "                (&#x27;forest&#x27;, RandomForestClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;AB&#x27;, &#x27;AF&#x27;, &#x27;AH&#x27;, &#x27;AM&#x27;, &#x27;AR&#x27;, &#x27;AX&#x27;, &#x27;AY&#x27;,\n",
       "                                  &#x27;AZ&#x27;, &#x27;BC&#x27;, &#x27;BD &#x27;, &#x27;BN&#x27;, &#x27;BP&#x27;, &#x27;BQ&#x27;, &#x27;BR&#x27;,\n",
       "                                  &#x27;BZ&#x27;, &#x27;CB&#x27;, &#x27;CC&#x27;, &#x27;CD &#x27;, &#x27;CF&#x27;, &#x27;CH&#x27;, &#x27;CL&#x27;,\n",
       "                                  &#x27;CR&#x27;, &#x27;CS&#x27;, &#x27;CU&#x27;, &#x27;CW &#x27;, &#x27;DA&#x27;, &#x27;DE&#x27;, &#x27;DF&#x27;,\n",
       "                                  &#x27;DH&#x27;, &#x27;DI&#x27;, ...]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;encoder&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                 [&#x27;EJ&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;AB&#x27;, &#x27;AF&#x27;, &#x27;AH&#x27;, &#x27;AM&#x27;, &#x27;AR&#x27;, &#x27;AX&#x27;, &#x27;AY&#x27;, &#x27;AZ&#x27;, &#x27;BC&#x27;, &#x27;BD &#x27;, &#x27;BN&#x27;, &#x27;BP&#x27;, &#x27;BQ&#x27;, &#x27;BR&#x27;, &#x27;BZ&#x27;, &#x27;CB&#x27;, &#x27;CC&#x27;, &#x27;CD &#x27;, &#x27;CF&#x27;, &#x27;CH&#x27;, &#x27;CL&#x27;, &#x27;CR&#x27;, &#x27;CS&#x27;, &#x27;CU&#x27;, &#x27;CW &#x27;, &#x27;DA&#x27;, &#x27;DE&#x27;, &#x27;DF&#x27;, &#x27;DH&#x27;, &#x27;DI&#x27;, &#x27;DL&#x27;, &#x27;DN&#x27;, &#x27;DU&#x27;, &#x27;DV&#x27;, &#x27;DY&#x27;, &#x27;EB&#x27;, &#x27;EE&#x27;, &#x27;EG&#x27;, &#x27;EH&#x27;, &#x27;EL&#x27;, &#x27;EP&#x27;, &#x27;EU&#x27;, &#x27;FC&#x27;, &#x27;FD &#x27;, &#x27;FE&#x27;, &#x27;FI&#x27;, &#x27;FL&#x27;, &#x27;FR&#x27;, &#x27;FS&#x27;, &#x27;GB&#x27;, &#x27;GE&#x27;, &#x27;GF&#x27;, &#x27;GH&#x27;, &#x27;GI&#x27;, &#x27;GL&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;EJ&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(estimator=Pipeline(steps=[('preprocessor',\n",
       "                                              ColumnTransformer(transformers=[('num',\n",
       "                                                                               Pipeline(steps=[('imputer',\n",
       "                                                                                                SimpleImputer(strategy='median')),\n",
       "                                                                                               ('scaler',\n",
       "                                                                                                StandardScaler())]),\n",
       "                                                                               ['AB',\n",
       "                                                                                'AF',\n",
       "                                                                                'AH',\n",
       "                                                                                'AM',\n",
       "                                                                                'AR',\n",
       "                                                                                'AX',\n",
       "                                                                                'AY',\n",
       "                                                                                'AZ',\n",
       "                                                                                'BC',\n",
       "                                                                                'BD ',\n",
       "                                                                                'BN',\n",
       "                                                                                'BP',\n",
       "                                                                                'BQ',\n",
       "                                                                                'BR',\n",
       "                                                                                'BZ',\n",
       "                                                                                'CB',\n",
       "                                                                                'CC',\n",
       "                                                                                'CD ',\n",
       "                                                                                'CF',\n",
       "                                                                                'CH',\n",
       "                                                                                'CL',\n",
       "                                                                                'CR',\n",
       "                                                                                'CS',\n",
       "                                                                                'CU',\n",
       "                                                                                'CW ',\n",
       "                                                                                'DA',\n",
       "                                                                                'DE',\n",
       "                                                                                'DF',\n",
       "                                                                                'DH',\n",
       "                                                                                'DI', ...]),\n",
       "                                                                              ('...\n",
       "                                        'forest__min_samples_split': [2],\n",
       "                                        'forest__min_weight_fraction_leaf': [0.0],\n",
       "                                        'forest__n_estimators': [1000],\n",
       "                                        'forest__n_jobs': [-1],\n",
       "                                        'forest__oob_score': [False],\n",
       "                                        'forest__random_state': [42],\n",
       "                                        'forest__verbose': [10],\n",
       "                                        'forest__warm_start': [True]},\n",
       "                   random_state=42, refit='neg_log_loss',\n",
       "                   return_train_score=True,\n",
       "                   scoring=['accuracy', 'f1', 'neg_log_loss', 'precision',\n",
       "                            'recall', 'roc_auc'],\n",
       "                   verbose=10)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bafae994-5998-4338-987d-35b1b761791c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_forest__bootstrap</th>\n",
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       "      <th>param_forest__class_weight</th>\n",
       "      <th>param_forest__criterion</th>\n",
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       "      <th>param_forest__max_samples</th>\n",
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       "      <th>param_forest__min_samples_leaf</th>\n",
       "      <th>param_forest__min_samples_split</th>\n",
       "      <th>param_forest__min_weight_fraction_leaf</th>\n",
       "      <th>param_forest__n_estimators</th>\n",
       "      <th>param_forest__n_jobs</th>\n",
       "      <th>param_forest__oob_score</th>\n",
       "      <th>param_forest__random_state</th>\n",
       "      <th>param_forest__verbose</th>\n",
       "      <th>param_forest__warm_start</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_accuracy</th>\n",
       "      <th>split1_test_accuracy</th>\n",
       "      <th>split2_test_accuracy</th>\n",
       "      <th>split3_test_accuracy</th>\n",
       "      <th>split4_test_accuracy</th>\n",
       "      <th>mean_test_accuracy</th>\n",
       "      <th>std_test_accuracy</th>\n",
       "      <th>rank_test_accuracy</th>\n",
       "      <th>split0_train_accuracy</th>\n",
       "      <th>split1_train_accuracy</th>\n",
       "      <th>split2_train_accuracy</th>\n",
       "      <th>split3_train_accuracy</th>\n",
       "      <th>split4_train_accuracy</th>\n",
       "      <th>mean_train_accuracy</th>\n",
       "      <th>std_train_accuracy</th>\n",
       "      <th>split0_test_f1</th>\n",
       "      <th>split1_test_f1</th>\n",
       "      <th>split2_test_f1</th>\n",
       "      <th>split3_test_f1</th>\n",
       "      <th>split4_test_f1</th>\n",
       "      <th>mean_test_f1</th>\n",
       "      <th>std_test_f1</th>\n",
       "      <th>rank_test_f1</th>\n",
       "      <th>split0_train_f1</th>\n",
       "      <th>split1_train_f1</th>\n",
       "      <th>split2_train_f1</th>\n",
       "      <th>split3_train_f1</th>\n",
       "      <th>split4_train_f1</th>\n",
       "      <th>mean_train_f1</th>\n",
       "      <th>std_train_f1</th>\n",
       "      <th>split0_test_neg_log_loss</th>\n",
       "      <th>split1_test_neg_log_loss</th>\n",
       "      <th>split2_test_neg_log_loss</th>\n",
       "      <th>split3_test_neg_log_loss</th>\n",
       "      <th>split4_test_neg_log_loss</th>\n",
       "      <th>mean_test_neg_log_loss</th>\n",
       "      <th>std_test_neg_log_loss</th>\n",
       "      <th>rank_test_neg_log_loss</th>\n",
       "      <th>split0_train_neg_log_loss</th>\n",
       "      <th>split1_train_neg_log_loss</th>\n",
       "      <th>split2_train_neg_log_loss</th>\n",
       "      <th>split3_train_neg_log_loss</th>\n",
       "      <th>split4_train_neg_log_loss</th>\n",
       "      <th>mean_train_neg_log_loss</th>\n",
       "      <th>std_train_neg_log_loss</th>\n",
       "      <th>split0_test_precision</th>\n",
       "      <th>split1_test_precision</th>\n",
       "      <th>split2_test_precision</th>\n",
       "      <th>split3_test_precision</th>\n",
       "      <th>split4_test_precision</th>\n",
       "      <th>mean_test_precision</th>\n",
       "      <th>std_test_precision</th>\n",
       "      <th>rank_test_precision</th>\n",
       "      <th>split0_train_precision</th>\n",
       "      <th>split1_train_precision</th>\n",
       "      <th>split2_train_precision</th>\n",
       "      <th>split3_train_precision</th>\n",
       "      <th>split4_train_precision</th>\n",
       "      <th>mean_train_precision</th>\n",
       "      <th>std_train_precision</th>\n",
       "      <th>split0_test_recall</th>\n",
       "      <th>split1_test_recall</th>\n",
       "      <th>split2_test_recall</th>\n",
       "      <th>split3_test_recall</th>\n",
       "      <th>split4_test_recall</th>\n",
       "      <th>mean_test_recall</th>\n",
       "      <th>std_test_recall</th>\n",
       "      <th>rank_test_recall</th>\n",
       "      <th>split0_train_recall</th>\n",
       "      <th>split1_train_recall</th>\n",
       "      <th>split2_train_recall</th>\n",
       "      <th>split3_train_recall</th>\n",
       "      <th>split4_train_recall</th>\n",
       "      <th>mean_train_recall</th>\n",
       "      <th>std_train_recall</th>\n",
       "      <th>split0_test_roc_auc</th>\n",
       "      <th>split1_test_roc_auc</th>\n",
       "      <th>split2_test_roc_auc</th>\n",
       "      <th>split3_test_roc_auc</th>\n",
       "      <th>split4_test_roc_auc</th>\n",
       "      <th>mean_test_roc_auc</th>\n",
       "      <th>std_test_roc_auc</th>\n",
       "      <th>rank_test_roc_auc</th>\n",
       "      <th>split0_train_roc_auc</th>\n",
       "      <th>split1_train_roc_auc</th>\n",
       "      <th>split2_train_roc_auc</th>\n",
       "      <th>split3_train_roc_auc</th>\n",
       "      <th>split4_train_roc_auc</th>\n",
       "      <th>mean_train_roc_auc</th>\n",
       "      <th>std_train_roc_auc</th>\n",
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       "      <th>44</th>\n",
       "      <td>9.588275</td>\n",
       "      <td>0.757163</td>\n",
       "      <td>0.558042</td>\n",
       "      <td>0.100988</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.472215</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
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       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.4722149251619493, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.943548</td>\n",
       "      <td>0.862903</td>\n",
       "      <td>0.934959</td>\n",
       "      <td>0.910569</td>\n",
       "      <td>0.967480</td>\n",
       "      <td>0.923892</td>\n",
       "      <td>0.035513</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.604651</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>0.768430</td>\n",
       "      <td>0.104049</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.188384</td>\n",
       "      <td>-0.246548</td>\n",
       "      <td>-0.227353</td>\n",
       "      <td>-0.230374</td>\n",
       "      <td>-0.194891</td>\n",
       "      <td>-0.217510</td>\n",
       "      <td>0.022206</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.063184</td>\n",
       "      <td>-0.057840</td>\n",
       "      <td>-0.060752</td>\n",
       "      <td>-0.061706</td>\n",
       "      <td>-0.062456</td>\n",
       "      <td>-0.061188</td>\n",
       "      <td>0.001858</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>0.825497</td>\n",
       "      <td>0.112299</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>0.722944</td>\n",
       "      <td>0.112945</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.983957</td>\n",
       "      <td>0.940508</td>\n",
       "      <td>0.943069</td>\n",
       "      <td>0.956349</td>\n",
       "      <td>0.953081</td>\n",
       "      <td>0.955393</td>\n",
       "      <td>0.015464</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.965068e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>6.326918</td>\n",
       "      <td>0.050977</td>\n",
       "      <td>0.544000</td>\n",
       "      <td>0.015184</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.623298</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.6232981268275579, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>0.862903</td>\n",
       "      <td>0.934959</td>\n",
       "      <td>0.910569</td>\n",
       "      <td>0.959350</td>\n",
       "      <td>0.920653</td>\n",
       "      <td>0.032738</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.604651</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.756880</td>\n",
       "      <td>0.094472</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.189127</td>\n",
       "      <td>-0.259877</td>\n",
       "      <td>-0.222542</td>\n",
       "      <td>-0.225217</td>\n",
       "      <td>-0.197657</td>\n",
       "      <td>-0.218884</td>\n",
       "      <td>0.024779</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.061855</td>\n",
       "      <td>-0.056088</td>\n",
       "      <td>-0.060006</td>\n",
       "      <td>-0.061709</td>\n",
       "      <td>-0.061415</td>\n",
       "      <td>-0.060215</td>\n",
       "      <td>0.002165</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.823810</td>\n",
       "      <td>0.110880</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.704329</td>\n",
       "      <td>0.098665</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.978610</td>\n",
       "      <td>0.928030</td>\n",
       "      <td>0.946895</td>\n",
       "      <td>0.959384</td>\n",
       "      <td>0.951447</td>\n",
       "      <td>0.952873</td>\n",
       "      <td>0.016491</td>\n",
       "      <td>11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.965068e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.209980</td>\n",
       "      <td>0.403286</td>\n",
       "      <td>0.543333</td>\n",
       "      <td>0.096212</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.291229</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.2912291401980419, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.927419</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.894309</td>\n",
       "      <td>0.959350</td>\n",
       "      <td>0.915775</td>\n",
       "      <td>0.030414</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.756757</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>0.628571</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.732582</td>\n",
       "      <td>0.097722</td>\n",
       "      <td>32</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.193291</td>\n",
       "      <td>-0.244133</td>\n",
       "      <td>-0.227640</td>\n",
       "      <td>-0.239728</td>\n",
       "      <td>-0.196789</td>\n",
       "      <td>-0.220316</td>\n",
       "      <td>0.021362</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.064018</td>\n",
       "      <td>-0.060149</td>\n",
       "      <td>-0.063239</td>\n",
       "      <td>-0.062927</td>\n",
       "      <td>-0.064846</td>\n",
       "      <td>-0.063036</td>\n",
       "      <td>0.001589</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.822231</td>\n",
       "      <td>0.099783</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.667100</td>\n",
       "      <td>0.115775</td>\n",
       "      <td>33</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.988191</td>\n",
       "      <td>0.944519</td>\n",
       "      <td>0.946670</td>\n",
       "      <td>0.956116</td>\n",
       "      <td>0.956816</td>\n",
       "      <td>0.958462</td>\n",
       "      <td>0.015656</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.021667e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>10.988012</td>\n",
       "      <td>0.069876</td>\n",
       "      <td>0.627750</td>\n",
       "      <td>0.079751</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.713245</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.713244787222995, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.943548</td>\n",
       "      <td>0.862903</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.910569</td>\n",
       "      <td>0.959350</td>\n",
       "      <td>0.923892</td>\n",
       "      <td>0.034378</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.604651</td>\n",
       "      <td>0.837209</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.768625</td>\n",
       "      <td>0.100601</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.184958</td>\n",
       "      <td>-0.262567</td>\n",
       "      <td>-0.222470</td>\n",
       "      <td>-0.221762</td>\n",
       "      <td>-0.213737</td>\n",
       "      <td>-0.221099</td>\n",
       "      <td>0.024829</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.061075</td>\n",
       "      <td>-0.054832</td>\n",
       "      <td>-0.059435</td>\n",
       "      <td>-0.062055</td>\n",
       "      <td>-0.060835</td>\n",
       "      <td>-0.059646</td>\n",
       "      <td>0.002548</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.825973</td>\n",
       "      <td>0.112003</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.722511</td>\n",
       "      <td>0.105190</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.980838</td>\n",
       "      <td>0.920677</td>\n",
       "      <td>0.944869</td>\n",
       "      <td>0.957983</td>\n",
       "      <td>0.946312</td>\n",
       "      <td>0.950136</td>\n",
       "      <td>0.019567</td>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.965068e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>10.915706</td>\n",
       "      <td>0.108944</td>\n",
       "      <td>0.591122</td>\n",
       "      <td>0.093380</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.561277</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.5612771975694962, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>0.862903</td>\n",
       "      <td>0.934959</td>\n",
       "      <td>0.910569</td>\n",
       "      <td>0.959350</td>\n",
       "      <td>0.920653</td>\n",
       "      <td>0.032738</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.604651</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.756880</td>\n",
       "      <td>0.094472</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.188583</td>\n",
       "      <td>-0.256138</td>\n",
       "      <td>-0.225058</td>\n",
       "      <td>-0.226624</td>\n",
       "      <td>-0.209373</td>\n",
       "      <td>-0.221155</td>\n",
       "      <td>0.022220</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.062107</td>\n",
       "      <td>-0.055962</td>\n",
       "      <td>-0.059975</td>\n",
       "      <td>-0.061801</td>\n",
       "      <td>-0.061772</td>\n",
       "      <td>-0.060323</td>\n",
       "      <td>0.002307</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.823810</td>\n",
       "      <td>0.110880</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.704329</td>\n",
       "      <td>0.098665</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.982175</td>\n",
       "      <td>0.929590</td>\n",
       "      <td>0.944869</td>\n",
       "      <td>0.955182</td>\n",
       "      <td>0.945845</td>\n",
       "      <td>0.951532</td>\n",
       "      <td>0.017381</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>3.940238</td>\n",
       "      <td>0.040669</td>\n",
       "      <td>0.340701</td>\n",
       "      <td>0.015029</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.427541</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.42754101835854963, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.943548</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.934959</td>\n",
       "      <td>0.910569</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.922253</td>\n",
       "      <td>0.029054</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.763821</td>\n",
       "      <td>0.080883</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.191479</td>\n",
       "      <td>-0.250005</td>\n",
       "      <td>-0.231059</td>\n",
       "      <td>-0.235000</td>\n",
       "      <td>-0.199185</td>\n",
       "      <td>-0.221346</td>\n",
       "      <td>0.022295</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.062562</td>\n",
       "      <td>-0.058090</td>\n",
       "      <td>-0.061433</td>\n",
       "      <td>-0.061881</td>\n",
       "      <td>-0.063491</td>\n",
       "      <td>-0.061492</td>\n",
       "      <td>0.001837</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.826955</td>\n",
       "      <td>0.104600</td>\n",
       "      <td>14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.712987</td>\n",
       "      <td>0.074549</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.981283</td>\n",
       "      <td>0.933155</td>\n",
       "      <td>0.937669</td>\n",
       "      <td>0.952614</td>\n",
       "      <td>0.949113</td>\n",
       "      <td>0.950767</td>\n",
       "      <td>0.016847</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.021667e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>12.743233</td>\n",
       "      <td>0.092604</td>\n",
       "      <td>0.542062</td>\n",
       "      <td>0.026254</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.802197</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.8021969807540397, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.927419</td>\n",
       "      <td>0.854839</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.910569</td>\n",
       "      <td>0.959350</td>\n",
       "      <td>0.919053</td>\n",
       "      <td>0.035964</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.837209</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.755620</td>\n",
       "      <td>0.101746</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.189803</td>\n",
       "      <td>-0.269909</td>\n",
       "      <td>-0.225682</td>\n",
       "      <td>-0.220571</td>\n",
       "      <td>-0.209907</td>\n",
       "      <td>-0.223174</td>\n",
       "      <td>0.026408</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.060615</td>\n",
       "      <td>-0.054166</td>\n",
       "      <td>-0.059160</td>\n",
       "      <td>-0.061120</td>\n",
       "      <td>-0.060546</td>\n",
       "      <td>-0.059122</td>\n",
       "      <td>0.002562</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.808581</td>\n",
       "      <td>0.112743</td>\n",
       "      <td>34</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.713420</td>\n",
       "      <td>0.106344</td>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.975045</td>\n",
       "      <td>0.915330</td>\n",
       "      <td>0.941944</td>\n",
       "      <td>0.957983</td>\n",
       "      <td>0.945612</td>\n",
       "      <td>0.947183</td>\n",
       "      <td>0.019685</td>\n",
       "      <td>26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7.838939</td>\n",
       "      <td>0.080844</td>\n",
       "      <td>0.503775</td>\n",
       "      <td>0.012319</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.271349</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.2713490317738959, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.919355</td>\n",
       "      <td>0.862903</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.894309</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.910923</td>\n",
       "      <td>0.030099</td>\n",
       "      <td>43</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.604651</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>0.628571</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.717187</td>\n",
       "      <td>0.091857</td>\n",
       "      <td>40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.196897</td>\n",
       "      <td>-0.247174</td>\n",
       "      <td>-0.228466</td>\n",
       "      <td>-0.243350</td>\n",
       "      <td>-0.201142</td>\n",
       "      <td>-0.223406</td>\n",
       "      <td>0.020912</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.064201</td>\n",
       "      <td>-0.060140</td>\n",
       "      <td>-0.063600</td>\n",
       "      <td>-0.062928</td>\n",
       "      <td>-0.065730</td>\n",
       "      <td>-0.063320</td>\n",
       "      <td>0.001840</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.814035</td>\n",
       "      <td>0.108850</td>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.648485</td>\n",
       "      <td>0.104226</td>\n",
       "      <td>38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.987299</td>\n",
       "      <td>0.941622</td>\n",
       "      <td>0.949820</td>\n",
       "      <td>0.947012</td>\n",
       "      <td>0.952148</td>\n",
       "      <td>0.955580</td>\n",
       "      <td>0.016244</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.360978</td>\n",
       "      <td>0.352741</td>\n",
       "      <td>0.543934</td>\n",
       "      <td>0.070100</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.866176</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.8661761457749352, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.927419</td>\n",
       "      <td>0.854839</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.910569</td>\n",
       "      <td>0.959350</td>\n",
       "      <td>0.919053</td>\n",
       "      <td>0.035964</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.837209</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.759177</td>\n",
       "      <td>0.096078</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.186048</td>\n",
       "      <td>-0.275502</td>\n",
       "      <td>-0.223417</td>\n",
       "      <td>-0.219385</td>\n",
       "      <td>-0.214369</td>\n",
       "      <td>-0.223744</td>\n",
       "      <td>0.029007</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.060751</td>\n",
       "      <td>-0.053647</td>\n",
       "      <td>-0.058928</td>\n",
       "      <td>-0.061150</td>\n",
       "      <td>-0.060107</td>\n",
       "      <td>-0.058916</td>\n",
       "      <td>0.002740</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.807066</td>\n",
       "      <td>0.115671</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.722511</td>\n",
       "      <td>0.097016</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.976381</td>\n",
       "      <td>0.910873</td>\n",
       "      <td>0.943519</td>\n",
       "      <td>0.961251</td>\n",
       "      <td>0.946312</td>\n",
       "      <td>0.947667</td>\n",
       "      <td>0.021826</td>\n",
       "      <td>24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>10.395394</td>\n",
       "      <td>0.128204</td>\n",
       "      <td>0.681702</td>\n",
       "      <td>0.084981</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.815461</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.8154614284548342, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.927419</td>\n",
       "      <td>0.854839</td>\n",
       "      <td>0.934959</td>\n",
       "      <td>0.910569</td>\n",
       "      <td>0.959350</td>\n",
       "      <td>0.917427</td>\n",
       "      <td>0.035012</td>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.753640</td>\n",
       "      <td>0.092138</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.188121</td>\n",
       "      <td>-0.280086</td>\n",
       "      <td>-0.226318</td>\n",
       "      <td>-0.221191</td>\n",
       "      <td>-0.203775</td>\n",
       "      <td>-0.223898</td>\n",
       "      <td>0.031161</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.060538</td>\n",
       "      <td>-0.054007</td>\n",
       "      <td>-0.058743</td>\n",
       "      <td>-0.061201</td>\n",
       "      <td>-0.060214</td>\n",
       "      <td>-0.058941</td>\n",
       "      <td>0.002595</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.805637</td>\n",
       "      <td>0.115086</td>\n",
       "      <td>38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.713420</td>\n",
       "      <td>0.089461</td>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.978610</td>\n",
       "      <td>0.911542</td>\n",
       "      <td>0.939919</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.950980</td>\n",
       "      <td>0.948367</td>\n",
       "      <td>0.022375</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12.206363</td>\n",
       "      <td>0.115937</td>\n",
       "      <td>0.548270</td>\n",
       "      <td>0.047225</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.921874</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.9218742350231168, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.919355</td>\n",
       "      <td>0.846774</td>\n",
       "      <td>0.934959</td>\n",
       "      <td>0.902439</td>\n",
       "      <td>0.959350</td>\n",
       "      <td>0.912575</td>\n",
       "      <td>0.037861</td>\n",
       "      <td>34</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.595745</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.739997</td>\n",
       "      <td>0.100178</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.189183</td>\n",
       "      <td>-0.292531</td>\n",
       "      <td>-0.221965</td>\n",
       "      <td>-0.220392</td>\n",
       "      <td>-0.204445</td>\n",
       "      <td>-0.225703</td>\n",
       "      <td>0.035479</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.059372</td>\n",
       "      <td>-0.053354</td>\n",
       "      <td>-0.058384</td>\n",
       "      <td>-0.061280</td>\n",
       "      <td>-0.059293</td>\n",
       "      <td>-0.058337</td>\n",
       "      <td>0.002664</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.788667</td>\n",
       "      <td>0.118801</td>\n",
       "      <td>44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.703896</td>\n",
       "      <td>0.100815</td>\n",
       "      <td>24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.974599</td>\n",
       "      <td>0.903743</td>\n",
       "      <td>0.944419</td>\n",
       "      <td>0.961718</td>\n",
       "      <td>0.955182</td>\n",
       "      <td>0.947932</td>\n",
       "      <td>0.024161</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>11.062069</td>\n",
       "      <td>0.824333</td>\n",
       "      <td>0.560568</td>\n",
       "      <td>0.059657</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.198716</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.1987156815341724, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.919355</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.934959</td>\n",
       "      <td>0.894309</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.914162</td>\n",
       "      <td>0.028610</td>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.628571</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.723968</td>\n",
       "      <td>0.091408</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.200671</td>\n",
       "      <td>-0.245605</td>\n",
       "      <td>-0.233451</td>\n",
       "      <td>-0.244237</td>\n",
       "      <td>-0.204951</td>\n",
       "      <td>-0.225783</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.066376</td>\n",
       "      <td>-0.062905</td>\n",
       "      <td>-0.065090</td>\n",
       "      <td>-0.064503</td>\n",
       "      <td>-0.067612</td>\n",
       "      <td>-0.065297</td>\n",
       "      <td>0.001607</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.829582</td>\n",
       "      <td>0.101721</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.648485</td>\n",
       "      <td>0.104226</td>\n",
       "      <td>38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.990196</td>\n",
       "      <td>0.944296</td>\n",
       "      <td>0.951845</td>\n",
       "      <td>0.953315</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.958407</td>\n",
       "      <td>0.016217</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.965068e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>12.567641</td>\n",
       "      <td>0.096218</td>\n",
       "      <td>0.577294</td>\n",
       "      <td>0.082639</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.986887</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.9868869366005173, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.927419</td>\n",
       "      <td>0.846774</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.910569</td>\n",
       "      <td>0.959350</td>\n",
       "      <td>0.917440</td>\n",
       "      <td>0.038871</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.595745</td>\n",
       "      <td>0.837209</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.756587</td>\n",
       "      <td>0.100186</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.186212</td>\n",
       "      <td>-0.287698</td>\n",
       "      <td>-0.226869</td>\n",
       "      <td>-0.216048</td>\n",
       "      <td>-0.214592</td>\n",
       "      <td>-0.226284</td>\n",
       "      <td>0.033524</td>\n",
       "      <td>13</td>\n",
       "      <td>-0.060005</td>\n",
       "      <td>-0.052982</td>\n",
       "      <td>-0.058267</td>\n",
       "      <td>-0.061633</td>\n",
       "      <td>-0.059447</td>\n",
       "      <td>-0.058467</td>\n",
       "      <td>0.002949</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.802399</td>\n",
       "      <td>0.124720</td>\n",
       "      <td>40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.722511</td>\n",
       "      <td>0.097016</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.975045</td>\n",
       "      <td>0.907086</td>\n",
       "      <td>0.939019</td>\n",
       "      <td>0.961018</td>\n",
       "      <td>0.946779</td>\n",
       "      <td>0.945789</td>\n",
       "      <td>0.022946</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.021667e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.378783</td>\n",
       "      <td>0.059603</td>\n",
       "      <td>0.501049</td>\n",
       "      <td>0.038388</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.96991</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.9699098521619943, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.919355</td>\n",
       "      <td>0.854839</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.910569</td>\n",
       "      <td>0.959350</td>\n",
       "      <td>0.914188</td>\n",
       "      <td>0.033946</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.744616</td>\n",
       "      <td>0.083924</td>\n",
       "      <td>24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.189725</td>\n",
       "      <td>-0.296592</td>\n",
       "      <td>-0.225357</td>\n",
       "      <td>-0.218292</td>\n",
       "      <td>-0.207291</td>\n",
       "      <td>-0.227452</td>\n",
       "      <td>0.036602</td>\n",
       "      <td>14</td>\n",
       "      <td>-0.059401</td>\n",
       "      <td>-0.053169</td>\n",
       "      <td>-0.058142</td>\n",
       "      <td>-0.061601</td>\n",
       "      <td>-0.059602</td>\n",
       "      <td>-0.058383</td>\n",
       "      <td>0.002833</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.576923</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.801306</td>\n",
       "      <td>0.116025</td>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.704329</td>\n",
       "      <td>0.085179</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.973708</td>\n",
       "      <td>0.900401</td>\n",
       "      <td>0.939019</td>\n",
       "      <td>0.961718</td>\n",
       "      <td>0.951914</td>\n",
       "      <td>0.945352</td>\n",
       "      <td>0.025202</td>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7.649173</td>\n",
       "      <td>0.085965</td>\n",
       "      <td>0.553975</td>\n",
       "      <td>0.099921</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.366362</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.3663618432936917, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.927419</td>\n",
       "      <td>0.879032</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.894309</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.915762</td>\n",
       "      <td>0.025787</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.756757</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>0.628571</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.736497</td>\n",
       "      <td>0.079690</td>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.202658</td>\n",
       "      <td>-0.256360</td>\n",
       "      <td>-0.236049</td>\n",
       "      <td>-0.242741</td>\n",
       "      <td>-0.209118</td>\n",
       "      <td>-0.229385</td>\n",
       "      <td>0.020374</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.065303</td>\n",
       "      <td>-0.061007</td>\n",
       "      <td>-0.064309</td>\n",
       "      <td>-0.063497</td>\n",
       "      <td>-0.066154</td>\n",
       "      <td>-0.064054</td>\n",
       "      <td>0.001768</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.821613</td>\n",
       "      <td>0.098231</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.675758</td>\n",
       "      <td>0.095104</td>\n",
       "      <td>32</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.978387</td>\n",
       "      <td>0.924242</td>\n",
       "      <td>0.936094</td>\n",
       "      <td>0.952848</td>\n",
       "      <td>0.945845</td>\n",
       "      <td>0.947483</td>\n",
       "      <td>0.018201</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6.433379</td>\n",
       "      <td>0.037266</td>\n",
       "      <td>0.489659</td>\n",
       "      <td>0.024110</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.170524</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.17052412368729153, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.927419</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.886179</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.915749</td>\n",
       "      <td>0.025347</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.756757</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.790698</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.730471</td>\n",
       "      <td>0.092636</td>\n",
       "      <td>33</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.202730</td>\n",
       "      <td>-0.246241</td>\n",
       "      <td>-0.239447</td>\n",
       "      <td>-0.252377</td>\n",
       "      <td>-0.207504</td>\n",
       "      <td>-0.229660</td>\n",
       "      <td>0.020508</td>\n",
       "      <td>16</td>\n",
       "      <td>-0.067223</td>\n",
       "      <td>-0.063484</td>\n",
       "      <td>-0.066446</td>\n",
       "      <td>-0.064816</td>\n",
       "      <td>-0.068418</td>\n",
       "      <td>-0.066077</td>\n",
       "      <td>0.001746</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.821365</td>\n",
       "      <td>0.084271</td>\n",
       "      <td>24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.666234</td>\n",
       "      <td>0.118130</td>\n",
       "      <td>37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.991533</td>\n",
       "      <td>0.944296</td>\n",
       "      <td>0.943744</td>\n",
       "      <td>0.945845</td>\n",
       "      <td>0.952148</td>\n",
       "      <td>0.955513</td>\n",
       "      <td>0.018257</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.824133</td>\n",
       "      <td>0.049044</td>\n",
       "      <td>0.520624</td>\n",
       "      <td>0.011109</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.524756</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.5247564316322378, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.919355</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.894309</td>\n",
       "      <td>0.959350</td>\n",
       "      <td>0.914162</td>\n",
       "      <td>0.029964</td>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>0.628571</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.729139</td>\n",
       "      <td>0.093359</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.202977</td>\n",
       "      <td>-0.268550</td>\n",
       "      <td>-0.237213</td>\n",
       "      <td>-0.239945</td>\n",
       "      <td>-0.204363</td>\n",
       "      <td>-0.230610</td>\n",
       "      <td>0.024587</td>\n",
       "      <td>17</td>\n",
       "      <td>-0.064810</td>\n",
       "      <td>-0.058469</td>\n",
       "      <td>-0.063103</td>\n",
       "      <td>-0.063257</td>\n",
       "      <td>-0.064818</td>\n",
       "      <td>-0.062891</td>\n",
       "      <td>0.002329</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.818551</td>\n",
       "      <td>0.103502</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.667100</td>\n",
       "      <td>0.115775</td>\n",
       "      <td>33</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.974153</td>\n",
       "      <td>0.915107</td>\n",
       "      <td>0.936094</td>\n",
       "      <td>0.951447</td>\n",
       "      <td>0.948179</td>\n",
       "      <td>0.944996</td>\n",
       "      <td>0.019362</td>\n",
       "      <td>31</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.965068e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9.099431</td>\n",
       "      <td>0.070639</td>\n",
       "      <td>0.531481</td>\n",
       "      <td>0.049644</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.520068</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.5200680211778108, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.919355</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.894309</td>\n",
       "      <td>0.959350</td>\n",
       "      <td>0.914162</td>\n",
       "      <td>0.029964</td>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>0.628571</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.729139</td>\n",
       "      <td>0.093359</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.202977</td>\n",
       "      <td>-0.268550</td>\n",
       "      <td>-0.237213</td>\n",
       "      <td>-0.239945</td>\n",
       "      <td>-0.204363</td>\n",
       "      <td>-0.230610</td>\n",
       "      <td>0.024587</td>\n",
       "      <td>17</td>\n",
       "      <td>-0.064810</td>\n",
       "      <td>-0.058469</td>\n",
       "      <td>-0.063103</td>\n",
       "      <td>-0.063257</td>\n",
       "      <td>-0.064818</td>\n",
       "      <td>-0.062891</td>\n",
       "      <td>0.002329</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.818551</td>\n",
       "      <td>0.103502</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.667100</td>\n",
       "      <td>0.115775</td>\n",
       "      <td>33</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.974153</td>\n",
       "      <td>0.915107</td>\n",
       "      <td>0.936094</td>\n",
       "      <td>0.951447</td>\n",
       "      <td>0.948179</td>\n",
       "      <td>0.944996</td>\n",
       "      <td>0.019362</td>\n",
       "      <td>31</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.965068e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>12.791049</td>\n",
       "      <td>0.063078</td>\n",
       "      <td>0.565234</td>\n",
       "      <td>0.044141</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.542696</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.5426960831582485, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.911290</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.902439</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.912549</td>\n",
       "      <td>0.026579</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.685714</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.727009</td>\n",
       "      <td>0.076070</td>\n",
       "      <td>37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.202642</td>\n",
       "      <td>-0.271624</td>\n",
       "      <td>-0.238415</td>\n",
       "      <td>-0.237890</td>\n",
       "      <td>-0.204268</td>\n",
       "      <td>-0.230968</td>\n",
       "      <td>0.025579</td>\n",
       "      <td>19</td>\n",
       "      <td>-0.064252</td>\n",
       "      <td>-0.058087</td>\n",
       "      <td>-0.063196</td>\n",
       "      <td>-0.062840</td>\n",
       "      <td>-0.065199</td>\n",
       "      <td>-0.062715</td>\n",
       "      <td>0.002457</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.816984</td>\n",
       "      <td>0.104956</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.667100</td>\n",
       "      <td>0.098054</td>\n",
       "      <td>33</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.973930</td>\n",
       "      <td>0.914661</td>\n",
       "      <td>0.934293</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.947012</td>\n",
       "      <td>0.944456</td>\n",
       "      <td>0.019642</td>\n",
       "      <td>33</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11.181804</td>\n",
       "      <td>0.068067</td>\n",
       "      <td>0.494118</td>\n",
       "      <td>0.030218</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.440152</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.4401524937396013, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.919355</td>\n",
       "      <td>0.879032</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.902439</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.915775</td>\n",
       "      <td>0.024163</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.737209</td>\n",
       "      <td>0.070387</td>\n",
       "      <td>28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.203958</td>\n",
       "      <td>-0.267809</td>\n",
       "      <td>-0.236955</td>\n",
       "      <td>-0.243657</td>\n",
       "      <td>-0.205636</td>\n",
       "      <td>-0.231603</td>\n",
       "      <td>0.024180</td>\n",
       "      <td>20</td>\n",
       "      <td>-0.065144</td>\n",
       "      <td>-0.059214</td>\n",
       "      <td>-0.064127</td>\n",
       "      <td>-0.062981</td>\n",
       "      <td>-0.066075</td>\n",
       "      <td>-0.063508</td>\n",
       "      <td>0.002382</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.823517</td>\n",
       "      <td>0.096297</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.676190</td>\n",
       "      <td>0.087940</td>\n",
       "      <td>31</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.978610</td>\n",
       "      <td>0.915998</td>\n",
       "      <td>0.937669</td>\n",
       "      <td>0.949346</td>\n",
       "      <td>0.948880</td>\n",
       "      <td>0.946101</td>\n",
       "      <td>0.020260</td>\n",
       "      <td>28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.965068e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.768688</td>\n",
       "      <td>0.078492</td>\n",
       "      <td>0.581195</td>\n",
       "      <td>0.062904</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.139494</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.13949386065204183, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.911290</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.886179</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.907671</td>\n",
       "      <td>0.026244</td>\n",
       "      <td>46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.691181</td>\n",
       "      <td>0.094037</td>\n",
       "      <td>46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.210134</td>\n",
       "      <td>-0.249459</td>\n",
       "      <td>-0.240784</td>\n",
       "      <td>-0.254386</td>\n",
       "      <td>-0.210878</td>\n",
       "      <td>-0.233128</td>\n",
       "      <td>0.018979</td>\n",
       "      <td>21</td>\n",
       "      <td>-0.068921</td>\n",
       "      <td>-0.066549</td>\n",
       "      <td>-0.067740</td>\n",
       "      <td>-0.066006</td>\n",
       "      <td>-0.069591</td>\n",
       "      <td>-0.067761</td>\n",
       "      <td>0.001360</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.833378</td>\n",
       "      <td>0.112096</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.602165</td>\n",
       "      <td>0.118905</td>\n",
       "      <td>46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.990642</td>\n",
       "      <td>0.946078</td>\n",
       "      <td>0.940819</td>\n",
       "      <td>0.949580</td>\n",
       "      <td>0.952848</td>\n",
       "      <td>0.955993</td>\n",
       "      <td>0.017777</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.965068e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7.858336</td>\n",
       "      <td>0.068936</td>\n",
       "      <td>0.531150</td>\n",
       "      <td>0.024365</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.32533</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.32533033076326434, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.918699</td>\n",
       "      <td>0.902439</td>\n",
       "      <td>0.967480</td>\n",
       "      <td>0.922240</td>\n",
       "      <td>0.027791</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>0.763692</td>\n",
       "      <td>0.083201</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.205300</td>\n",
       "      <td>-0.254905</td>\n",
       "      <td>-0.248784</td>\n",
       "      <td>-0.249789</td>\n",
       "      <td>-0.209133</td>\n",
       "      <td>-0.233582</td>\n",
       "      <td>0.021661</td>\n",
       "      <td>22</td>\n",
       "      <td>-0.065871</td>\n",
       "      <td>-0.062073</td>\n",
       "      <td>-0.064291</td>\n",
       "      <td>-0.063781</td>\n",
       "      <td>-0.066545</td>\n",
       "      <td>-0.064512</td>\n",
       "      <td>0.001582</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>0.821786</td>\n",
       "      <td>0.095169</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>0.722511</td>\n",
       "      <td>0.107464</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.974599</td>\n",
       "      <td>0.927807</td>\n",
       "      <td>0.925518</td>\n",
       "      <td>0.939776</td>\n",
       "      <td>0.947246</td>\n",
       "      <td>0.942989</td>\n",
       "      <td>0.017683</td>\n",
       "      <td>34</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>9.288925</td>\n",
       "      <td>0.131031</td>\n",
       "      <td>0.532982</td>\n",
       "      <td>0.045693</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.325183</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.32518332202674705, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.918699</td>\n",
       "      <td>0.902439</td>\n",
       "      <td>0.967480</td>\n",
       "      <td>0.922240</td>\n",
       "      <td>0.027791</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>0.763692</td>\n",
       "      <td>0.083201</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.205300</td>\n",
       "      <td>-0.254905</td>\n",
       "      <td>-0.248784</td>\n",
       "      <td>-0.249789</td>\n",
       "      <td>-0.209133</td>\n",
       "      <td>-0.233582</td>\n",
       "      <td>0.021661</td>\n",
       "      <td>22</td>\n",
       "      <td>-0.065871</td>\n",
       "      <td>-0.062073</td>\n",
       "      <td>-0.064291</td>\n",
       "      <td>-0.063781</td>\n",
       "      <td>-0.066545</td>\n",
       "      <td>-0.064512</td>\n",
       "      <td>0.001582</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>0.821786</td>\n",
       "      <td>0.095169</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>0.722511</td>\n",
       "      <td>0.107464</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.974599</td>\n",
       "      <td>0.927807</td>\n",
       "      <td>0.925518</td>\n",
       "      <td>0.939776</td>\n",
       "      <td>0.947246</td>\n",
       "      <td>0.942989</td>\n",
       "      <td>0.017683</td>\n",
       "      <td>34</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.943363</td>\n",
       "      <td>0.047121</td>\n",
       "      <td>0.490711</td>\n",
       "      <td>0.015945</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.183405</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.18340450985343382, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.919355</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.934959</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.912510</td>\n",
       "      <td>0.025766</td>\n",
       "      <td>37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.710971</td>\n",
       "      <td>0.099409</td>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.211243</td>\n",
       "      <td>-0.251684</td>\n",
       "      <td>-0.241572</td>\n",
       "      <td>-0.254599</td>\n",
       "      <td>-0.212186</td>\n",
       "      <td>-0.234257</td>\n",
       "      <td>0.018909</td>\n",
       "      <td>24</td>\n",
       "      <td>-0.068032</td>\n",
       "      <td>-0.064064</td>\n",
       "      <td>-0.067504</td>\n",
       "      <td>-0.065871</td>\n",
       "      <td>-0.068626</td>\n",
       "      <td>-0.066819</td>\n",
       "      <td>0.001655</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.831270</td>\n",
       "      <td>0.089380</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.629004</td>\n",
       "      <td>0.117472</td>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.986854</td>\n",
       "      <td>0.941622</td>\n",
       "      <td>0.941719</td>\n",
       "      <td>0.944678</td>\n",
       "      <td>0.949346</td>\n",
       "      <td>0.952844</td>\n",
       "      <td>0.017235</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>11.465425</td>\n",
       "      <td>1.696956</td>\n",
       "      <td>0.499988</td>\n",
       "      <td>0.026603</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.195983</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.1959828624191452, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.919355</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.934959</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.912510</td>\n",
       "      <td>0.025766</td>\n",
       "      <td>37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.710971</td>\n",
       "      <td>0.099409</td>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.211243</td>\n",
       "      <td>-0.251684</td>\n",
       "      <td>-0.241572</td>\n",
       "      <td>-0.254599</td>\n",
       "      <td>-0.212186</td>\n",
       "      <td>-0.234257</td>\n",
       "      <td>0.018909</td>\n",
       "      <td>24</td>\n",
       "      <td>-0.068032</td>\n",
       "      <td>-0.064064</td>\n",
       "      <td>-0.067504</td>\n",
       "      <td>-0.065871</td>\n",
       "      <td>-0.068626</td>\n",
       "      <td>-0.066819</td>\n",
       "      <td>0.001655</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.831270</td>\n",
       "      <td>0.089380</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.629004</td>\n",
       "      <td>0.117472</td>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.986854</td>\n",
       "      <td>0.941622</td>\n",
       "      <td>0.941719</td>\n",
       "      <td>0.944678</td>\n",
       "      <td>0.949346</td>\n",
       "      <td>0.952844</td>\n",
       "      <td>0.017235</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>9.050263</td>\n",
       "      <td>0.184995</td>\n",
       "      <td>0.564084</td>\n",
       "      <td>0.098950</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.184854</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.18485445552552704, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.919355</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.934959</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.912510</td>\n",
       "      <td>0.025766</td>\n",
       "      <td>37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.710971</td>\n",
       "      <td>0.099409</td>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.211243</td>\n",
       "      <td>-0.251684</td>\n",
       "      <td>-0.241572</td>\n",
       "      <td>-0.254599</td>\n",
       "      <td>-0.212186</td>\n",
       "      <td>-0.234257</td>\n",
       "      <td>0.018909</td>\n",
       "      <td>24</td>\n",
       "      <td>-0.068032</td>\n",
       "      <td>-0.064064</td>\n",
       "      <td>-0.067504</td>\n",
       "      <td>-0.065871</td>\n",
       "      <td>-0.068626</td>\n",
       "      <td>-0.066819</td>\n",
       "      <td>0.001655</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.831270</td>\n",
       "      <td>0.089380</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.629004</td>\n",
       "      <td>0.117472</td>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.986854</td>\n",
       "      <td>0.941622</td>\n",
       "      <td>0.941719</td>\n",
       "      <td>0.944678</td>\n",
       "      <td>0.949346</td>\n",
       "      <td>0.952844</td>\n",
       "      <td>0.017235</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.420383</td>\n",
       "      <td>0.141249</td>\n",
       "      <td>0.550584</td>\n",
       "      <td>0.090604</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.708073</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.7080725777960455, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.919355</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.910569</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.915788</td>\n",
       "      <td>0.026177</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.744441</td>\n",
       "      <td>0.067461</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.201897</td>\n",
       "      <td>-0.281896</td>\n",
       "      <td>-0.238170</td>\n",
       "      <td>-0.235774</td>\n",
       "      <td>-0.215754</td>\n",
       "      <td>-0.234698</td>\n",
       "      <td>0.027113</td>\n",
       "      <td>27</td>\n",
       "      <td>-0.063075</td>\n",
       "      <td>-0.056922</td>\n",
       "      <td>-0.062008</td>\n",
       "      <td>-0.062966</td>\n",
       "      <td>-0.064503</td>\n",
       "      <td>-0.061895</td>\n",
       "      <td>0.002611</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.809868</td>\n",
       "      <td>0.096610</td>\n",
       "      <td>33</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.694805</td>\n",
       "      <td>0.068612</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.968806</td>\n",
       "      <td>0.907531</td>\n",
       "      <td>0.932943</td>\n",
       "      <td>0.951214</td>\n",
       "      <td>0.940243</td>\n",
       "      <td>0.940147</td>\n",
       "      <td>0.020295</td>\n",
       "      <td>37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>11.036400</td>\n",
       "      <td>0.091750</td>\n",
       "      <td>0.501910</td>\n",
       "      <td>0.019297</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.493796</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.49379559636439074, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.943548</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.918699</td>\n",
       "      <td>0.902439</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.920601</td>\n",
       "      <td>0.024166</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.763885</td>\n",
       "      <td>0.067693</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.201670</td>\n",
       "      <td>-0.266115</td>\n",
       "      <td>-0.252411</td>\n",
       "      <td>-0.246154</td>\n",
       "      <td>-0.208780</td>\n",
       "      <td>-0.235026</td>\n",
       "      <td>0.025275</td>\n",
       "      <td>28</td>\n",
       "      <td>-0.065417</td>\n",
       "      <td>-0.059958</td>\n",
       "      <td>-0.063536</td>\n",
       "      <td>-0.063017</td>\n",
       "      <td>-0.065593</td>\n",
       "      <td>-0.063504</td>\n",
       "      <td>0.002041</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.805938</td>\n",
       "      <td>0.091699</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.731602</td>\n",
       "      <td>0.075479</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.973708</td>\n",
       "      <td>0.918895</td>\n",
       "      <td>0.920342</td>\n",
       "      <td>0.938609</td>\n",
       "      <td>0.946779</td>\n",
       "      <td>0.939666</td>\n",
       "      <td>0.020077</td>\n",
       "      <td>38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.965068e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11.058028</td>\n",
       "      <td>0.123231</td>\n",
       "      <td>0.520777</td>\n",
       "      <td>0.079112</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.495177</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.4951769101112702, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.943548</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.918699</td>\n",
       "      <td>0.902439</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.920601</td>\n",
       "      <td>0.024166</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.763885</td>\n",
       "      <td>0.067693</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.201670</td>\n",
       "      <td>-0.266115</td>\n",
       "      <td>-0.252411</td>\n",
       "      <td>-0.246154</td>\n",
       "      <td>-0.208780</td>\n",
       "      <td>-0.235026</td>\n",
       "      <td>0.025275</td>\n",
       "      <td>28</td>\n",
       "      <td>-0.065417</td>\n",
       "      <td>-0.059958</td>\n",
       "      <td>-0.063536</td>\n",
       "      <td>-0.063017</td>\n",
       "      <td>-0.065593</td>\n",
       "      <td>-0.063504</td>\n",
       "      <td>0.002041</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.805938</td>\n",
       "      <td>0.091699</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.731602</td>\n",
       "      <td>0.075479</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.973708</td>\n",
       "      <td>0.918895</td>\n",
       "      <td>0.920342</td>\n",
       "      <td>0.938609</td>\n",
       "      <td>0.946779</td>\n",
       "      <td>0.939666</td>\n",
       "      <td>0.020077</td>\n",
       "      <td>38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.965068e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11.034512</td>\n",
       "      <td>0.059276</td>\n",
       "      <td>0.492306</td>\n",
       "      <td>0.023881</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.808397</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.8083973481164611, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.919355</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.918699</td>\n",
       "      <td>0.902439</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.910910</td>\n",
       "      <td>0.023810</td>\n",
       "      <td>44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.729622</td>\n",
       "      <td>0.064376</td>\n",
       "      <td>34</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.202676</td>\n",
       "      <td>-0.291838</td>\n",
       "      <td>-0.239878</td>\n",
       "      <td>-0.234025</td>\n",
       "      <td>-0.207240</td>\n",
       "      <td>-0.235131</td>\n",
       "      <td>0.031845</td>\n",
       "      <td>30</td>\n",
       "      <td>-0.063580</td>\n",
       "      <td>-0.057088</td>\n",
       "      <td>-0.061790</td>\n",
       "      <td>-0.062884</td>\n",
       "      <td>-0.064526</td>\n",
       "      <td>-0.061974</td>\n",
       "      <td>0.002601</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.788333</td>\n",
       "      <td>0.083267</td>\n",
       "      <td>45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.685281</td>\n",
       "      <td>0.075412</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.966132</td>\n",
       "      <td>0.901961</td>\n",
       "      <td>0.932268</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.945378</td>\n",
       "      <td>0.939624</td>\n",
       "      <td>0.021777</td>\n",
       "      <td>40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.094939</td>\n",
       "      <td>2.649044</td>\n",
       "      <td>0.502028</td>\n",
       "      <td>0.031890</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.212339</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.21233911067827616, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.919355</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.934959</td>\n",
       "      <td>0.886179</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.915762</td>\n",
       "      <td>0.025828</td>\n",
       "      <td>26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.725425</td>\n",
       "      <td>0.093128</td>\n",
       "      <td>38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.210221</td>\n",
       "      <td>-0.251306</td>\n",
       "      <td>-0.244210</td>\n",
       "      <td>-0.253968</td>\n",
       "      <td>-0.217123</td>\n",
       "      <td>-0.235366</td>\n",
       "      <td>0.018130</td>\n",
       "      <td>31</td>\n",
       "      <td>-0.066945</td>\n",
       "      <td>-0.064214</td>\n",
       "      <td>-0.067089</td>\n",
       "      <td>-0.064779</td>\n",
       "      <td>-0.068601</td>\n",
       "      <td>-0.066326</td>\n",
       "      <td>0.001612</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.836286</td>\n",
       "      <td>0.086914</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.648052</td>\n",
       "      <td>0.114269</td>\n",
       "      <td>40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.987299</td>\n",
       "      <td>0.938280</td>\n",
       "      <td>0.941044</td>\n",
       "      <td>0.942810</td>\n",
       "      <td>0.945845</td>\n",
       "      <td>0.951056</td>\n",
       "      <td>0.018288</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>7.747515</td>\n",
       "      <td>0.066473</td>\n",
       "      <td>0.498658</td>\n",
       "      <td>0.028479</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.356753</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.3567533266935893, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.943548</td>\n",
       "      <td>0.895161</td>\n",
       "      <td>0.918699</td>\n",
       "      <td>0.902439</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.920587</td>\n",
       "      <td>0.020065</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.723404</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.760351</td>\n",
       "      <td>0.060849</td>\n",
       "      <td>11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.204520</td>\n",
       "      <td>-0.254541</td>\n",
       "      <td>-0.249963</td>\n",
       "      <td>-0.250668</td>\n",
       "      <td>-0.218199</td>\n",
       "      <td>-0.235578</td>\n",
       "      <td>0.020302</td>\n",
       "      <td>32</td>\n",
       "      <td>-0.065771</td>\n",
       "      <td>-0.061289</td>\n",
       "      <td>-0.064755</td>\n",
       "      <td>-0.063722</td>\n",
       "      <td>-0.066422</td>\n",
       "      <td>-0.064392</td>\n",
       "      <td>0.001801</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.814235</td>\n",
       "      <td>0.084638</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.721645</td>\n",
       "      <td>0.081192</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.974822</td>\n",
       "      <td>0.927807</td>\n",
       "      <td>0.926418</td>\n",
       "      <td>0.937675</td>\n",
       "      <td>0.942810</td>\n",
       "      <td>0.941906</td>\n",
       "      <td>0.017555</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7.446534</td>\n",
       "      <td>0.395198</td>\n",
       "      <td>0.565598</td>\n",
       "      <td>0.080060</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.785176</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.7851759613930136, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.911290</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.910569</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.912549</td>\n",
       "      <td>0.023963</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.717949</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.736516</td>\n",
       "      <td>0.061868</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.202103</td>\n",
       "      <td>-0.288987</td>\n",
       "      <td>-0.240117</td>\n",
       "      <td>-0.237177</td>\n",
       "      <td>-0.209642</td>\n",
       "      <td>-0.235605</td>\n",
       "      <td>0.030558</td>\n",
       "      <td>33</td>\n",
       "      <td>-0.063030</td>\n",
       "      <td>-0.057085</td>\n",
       "      <td>-0.061839</td>\n",
       "      <td>-0.063005</td>\n",
       "      <td>-0.064560</td>\n",
       "      <td>-0.061904</td>\n",
       "      <td>0.002560</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.790627</td>\n",
       "      <td>0.083867</td>\n",
       "      <td>43</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.694805</td>\n",
       "      <td>0.068612</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.965686</td>\n",
       "      <td>0.903966</td>\n",
       "      <td>0.929793</td>\n",
       "      <td>0.949346</td>\n",
       "      <td>0.942577</td>\n",
       "      <td>0.938274</td>\n",
       "      <td>0.020695</td>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.849723</td>\n",
       "      <td>0.924541</td>\n",
       "      <td>0.570452</td>\n",
       "      <td>0.091598</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.662522</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.662522284353982, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.927419</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.910569</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.915775</td>\n",
       "      <td>0.024652</td>\n",
       "      <td>22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.756757</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.744278</td>\n",
       "      <td>0.061485</td>\n",
       "      <td>26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.202547</td>\n",
       "      <td>-0.280458</td>\n",
       "      <td>-0.244536</td>\n",
       "      <td>-0.239006</td>\n",
       "      <td>-0.214901</td>\n",
       "      <td>-0.236290</td>\n",
       "      <td>0.026916</td>\n",
       "      <td>34</td>\n",
       "      <td>-0.064061</td>\n",
       "      <td>-0.057431</td>\n",
       "      <td>-0.062101</td>\n",
       "      <td>-0.063203</td>\n",
       "      <td>-0.064597</td>\n",
       "      <td>-0.062279</td>\n",
       "      <td>0.002567</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.812588</td>\n",
       "      <td>0.102019</td>\n",
       "      <td>32</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.694805</td>\n",
       "      <td>0.068612</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.910205</td>\n",
       "      <td>0.923492</td>\n",
       "      <td>0.948646</td>\n",
       "      <td>0.942810</td>\n",
       "      <td>0.938970</td>\n",
       "      <td>0.020589</td>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.487520</td>\n",
       "      <td>0.066567</td>\n",
       "      <td>0.491478</td>\n",
       "      <td>0.018379</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.514234</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.5142344384136116, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.943548</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.918699</td>\n",
       "      <td>0.910569</td>\n",
       "      <td>0.959350</td>\n",
       "      <td>0.923853</td>\n",
       "      <td>0.025306</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.883721</td>\n",
       "      <td>0.772899</td>\n",
       "      <td>0.071504</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.203506</td>\n",
       "      <td>-0.261182</td>\n",
       "      <td>-0.259176</td>\n",
       "      <td>-0.245316</td>\n",
       "      <td>-0.212683</td>\n",
       "      <td>-0.236373</td>\n",
       "      <td>0.023904</td>\n",
       "      <td>35</td>\n",
       "      <td>-0.065109</td>\n",
       "      <td>-0.060309</td>\n",
       "      <td>-0.063672</td>\n",
       "      <td>-0.062594</td>\n",
       "      <td>-0.065144</td>\n",
       "      <td>-0.063366</td>\n",
       "      <td>0.001802</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.816796</td>\n",
       "      <td>0.090004</td>\n",
       "      <td>28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>0.741126</td>\n",
       "      <td>0.091930</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.969029</td>\n",
       "      <td>0.924688</td>\n",
       "      <td>0.911791</td>\n",
       "      <td>0.938375</td>\n",
       "      <td>0.946312</td>\n",
       "      <td>0.938039</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>43</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.720870</td>\n",
       "      <td>0.103758</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.027555</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.598658</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.5986584841970366, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.943548</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.918699</td>\n",
       "      <td>0.902439</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.920601</td>\n",
       "      <td>0.024166</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.762456</td>\n",
       "      <td>0.065757</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.202434</td>\n",
       "      <td>-0.268272</td>\n",
       "      <td>-0.260161</td>\n",
       "      <td>-0.245065</td>\n",
       "      <td>-0.206455</td>\n",
       "      <td>-0.236477</td>\n",
       "      <td>0.027225</td>\n",
       "      <td>36</td>\n",
       "      <td>-0.064756</td>\n",
       "      <td>-0.058710</td>\n",
       "      <td>-0.063179</td>\n",
       "      <td>-0.062716</td>\n",
       "      <td>-0.065390</td>\n",
       "      <td>-0.062950</td>\n",
       "      <td>0.002337</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.813457</td>\n",
       "      <td>0.096978</td>\n",
       "      <td>31</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.722078</td>\n",
       "      <td>0.060569</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.967914</td>\n",
       "      <td>0.919786</td>\n",
       "      <td>0.911566</td>\n",
       "      <td>0.935808</td>\n",
       "      <td>0.948880</td>\n",
       "      <td>0.936791</td>\n",
       "      <td>0.020205</td>\n",
       "      <td>44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.965068e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.839932</td>\n",
       "      <td>0.056858</td>\n",
       "      <td>0.479022</td>\n",
       "      <td>0.023852</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.155995</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.15599452033620265, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.927419</td>\n",
       "      <td>0.911290</td>\n",
       "      <td>0.910569</td>\n",
       "      <td>0.902439</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.920587</td>\n",
       "      <td>0.017327</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.731707</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.749357</td>\n",
       "      <td>0.058922</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.214412</td>\n",
       "      <td>-0.247631</td>\n",
       "      <td>-0.251159</td>\n",
       "      <td>-0.260514</td>\n",
       "      <td>-0.214479</td>\n",
       "      <td>-0.237639</td>\n",
       "      <td>0.019400</td>\n",
       "      <td>37</td>\n",
       "      <td>-0.068995</td>\n",
       "      <td>-0.065272</td>\n",
       "      <td>-0.067779</td>\n",
       "      <td>-0.066980</td>\n",
       "      <td>-0.069831</td>\n",
       "      <td>-0.067771</td>\n",
       "      <td>0.001588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.739130</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.844668</td>\n",
       "      <td>0.092536</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.685281</td>\n",
       "      <td>0.094830</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.981283</td>\n",
       "      <td>0.944519</td>\n",
       "      <td>0.928443</td>\n",
       "      <td>0.940476</td>\n",
       "      <td>0.948413</td>\n",
       "      <td>0.948627</td>\n",
       "      <td>0.017648</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>12.348436</td>\n",
       "      <td>0.101040</td>\n",
       "      <td>0.548394</td>\n",
       "      <td>0.058050</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.894827</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.8948273504276488, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.911290</td>\n",
       "      <td>0.862903</td>\n",
       "      <td>0.926829</td>\n",
       "      <td>0.910569</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.910936</td>\n",
       "      <td>0.026810</td>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.717949</td>\n",
       "      <td>0.638298</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.733741</td>\n",
       "      <td>0.065777</td>\n",
       "      <td>31</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.204319</td>\n",
       "      <td>-0.294625</td>\n",
       "      <td>-0.242603</td>\n",
       "      <td>-0.235843</td>\n",
       "      <td>-0.215385</td>\n",
       "      <td>-0.238555</td>\n",
       "      <td>0.031230</td>\n",
       "      <td>38</td>\n",
       "      <td>-0.062908</td>\n",
       "      <td>-0.056822</td>\n",
       "      <td>-0.061390</td>\n",
       "      <td>-0.062548</td>\n",
       "      <td>-0.064268</td>\n",
       "      <td>-0.061587</td>\n",
       "      <td>0.002553</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.785627</td>\n",
       "      <td>0.093755</td>\n",
       "      <td>47</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.694805</td>\n",
       "      <td>0.068612</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.962344</td>\n",
       "      <td>0.900624</td>\n",
       "      <td>0.928218</td>\n",
       "      <td>0.950280</td>\n",
       "      <td>0.941410</td>\n",
       "      <td>0.936575</td>\n",
       "      <td>0.021155</td>\n",
       "      <td>45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.021667e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>9.244633</td>\n",
       "      <td>0.046252</td>\n",
       "      <td>0.510063</td>\n",
       "      <td>0.011509</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.637557</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.6375574713552131, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.943548</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.918699</td>\n",
       "      <td>0.894309</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.917349</td>\n",
       "      <td>0.023651</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.754801</td>\n",
       "      <td>0.065085</td>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.203806</td>\n",
       "      <td>-0.278336</td>\n",
       "      <td>-0.260761</td>\n",
       "      <td>-0.245538</td>\n",
       "      <td>-0.215860</td>\n",
       "      <td>-0.240860</td>\n",
       "      <td>0.027642</td>\n",
       "      <td>39</td>\n",
       "      <td>-0.064744</td>\n",
       "      <td>-0.058412</td>\n",
       "      <td>-0.063292</td>\n",
       "      <td>-0.062198</td>\n",
       "      <td>-0.065232</td>\n",
       "      <td>-0.062775</td>\n",
       "      <td>0.002431</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.796013</td>\n",
       "      <td>0.096103</td>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.722078</td>\n",
       "      <td>0.060569</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.965463</td>\n",
       "      <td>0.912433</td>\n",
       "      <td>0.912016</td>\n",
       "      <td>0.937208</td>\n",
       "      <td>0.943744</td>\n",
       "      <td>0.934173</td>\n",
       "      <td>0.020216</td>\n",
       "      <td>46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>3.899914</td>\n",
       "      <td>0.030608</td>\n",
       "      <td>0.333621</td>\n",
       "      <td>0.006268</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.107891</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.10789142699330445, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.903226</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.902439</td>\n",
       "      <td>0.886179</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.906032</td>\n",
       "      <td>0.023729</td>\n",
       "      <td>47</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.669254</td>\n",
       "      <td>0.093391</td>\n",
       "      <td>47</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.221266</td>\n",
       "      <td>-0.252215</td>\n",
       "      <td>-0.252510</td>\n",
       "      <td>-0.261969</td>\n",
       "      <td>-0.216773</td>\n",
       "      <td>-0.240947</td>\n",
       "      <td>0.018299</td>\n",
       "      <td>40</td>\n",
       "      <td>-0.071303</td>\n",
       "      <td>-0.067726</td>\n",
       "      <td>-0.069953</td>\n",
       "      <td>-0.067978</td>\n",
       "      <td>-0.071737</td>\n",
       "      <td>-0.069739</td>\n",
       "      <td>0.001652</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.867745</td>\n",
       "      <td>0.096571</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.556277</td>\n",
       "      <td>0.118539</td>\n",
       "      <td>47</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.989528</td>\n",
       "      <td>0.945633</td>\n",
       "      <td>0.933843</td>\n",
       "      <td>0.944911</td>\n",
       "      <td>0.953081</td>\n",
       "      <td>0.953399</td>\n",
       "      <td>0.019080</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.021667e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>10.442903</td>\n",
       "      <td>0.101354</td>\n",
       "      <td>0.573494</td>\n",
       "      <td>0.109785</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.729007</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.7290071680409873, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.918699</td>\n",
       "      <td>0.894309</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.917362</td>\n",
       "      <td>0.024182</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.756273</td>\n",
       "      <td>0.069026</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.203267</td>\n",
       "      <td>-0.280565</td>\n",
       "      <td>-0.262882</td>\n",
       "      <td>-0.244516</td>\n",
       "      <td>-0.214796</td>\n",
       "      <td>-0.241205</td>\n",
       "      <td>0.028868</td>\n",
       "      <td>41</td>\n",
       "      <td>-0.064630</td>\n",
       "      <td>-0.057446</td>\n",
       "      <td>-0.063084</td>\n",
       "      <td>-0.062069</td>\n",
       "      <td>-0.064820</td>\n",
       "      <td>-0.062410</td>\n",
       "      <td>0.002681</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.786984</td>\n",
       "      <td>0.082640</td>\n",
       "      <td>46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.731602</td>\n",
       "      <td>0.075479</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.959225</td>\n",
       "      <td>0.910428</td>\n",
       "      <td>0.913816</td>\n",
       "      <td>0.935574</td>\n",
       "      <td>0.944678</td>\n",
       "      <td>0.932744</td>\n",
       "      <td>0.018482</td>\n",
       "      <td>47</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>6.380080</td>\n",
       "      <td>0.028943</td>\n",
       "      <td>0.553672</td>\n",
       "      <td>0.025920</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.115869</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.11586905952512971, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.903226</td>\n",
       "      <td>0.911290</td>\n",
       "      <td>0.902439</td>\n",
       "      <td>0.886179</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.910871</td>\n",
       "      <td>0.021760</td>\n",
       "      <td>45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.744186</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.701484</td>\n",
       "      <td>0.092258</td>\n",
       "      <td>45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.219745</td>\n",
       "      <td>-0.249577</td>\n",
       "      <td>-0.254977</td>\n",
       "      <td>-0.264054</td>\n",
       "      <td>-0.221026</td>\n",
       "      <td>-0.241876</td>\n",
       "      <td>0.018151</td>\n",
       "      <td>42</td>\n",
       "      <td>-0.070242</td>\n",
       "      <td>-0.067618</td>\n",
       "      <td>-0.068804</td>\n",
       "      <td>-0.067863</td>\n",
       "      <td>-0.071076</td>\n",
       "      <td>-0.069121</td>\n",
       "      <td>0.001343</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.840730</td>\n",
       "      <td>0.093351</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.620779</td>\n",
       "      <td>0.138382</td>\n",
       "      <td>44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.983066</td>\n",
       "      <td>0.949421</td>\n",
       "      <td>0.923267</td>\n",
       "      <td>0.936741</td>\n",
       "      <td>0.948413</td>\n",
       "      <td>0.948182</td>\n",
       "      <td>0.019850</td>\n",
       "      <td>22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10.243565</td>\n",
       "      <td>1.557131</td>\n",
       "      <td>0.535053</td>\n",
       "      <td>0.076921</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.775133</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.7751328233611146, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.918699</td>\n",
       "      <td>0.894309</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.915736</td>\n",
       "      <td>0.022029</td>\n",
       "      <td>28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.837209</td>\n",
       "      <td>0.752287</td>\n",
       "      <td>0.063434</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.204399</td>\n",
       "      <td>-0.280863</td>\n",
       "      <td>-0.265144</td>\n",
       "      <td>-0.246280</td>\n",
       "      <td>-0.214252</td>\n",
       "      <td>-0.242188</td>\n",
       "      <td>0.029148</td>\n",
       "      <td>43</td>\n",
       "      <td>-0.064554</td>\n",
       "      <td>-0.057638</td>\n",
       "      <td>-0.061908</td>\n",
       "      <td>-0.061841</td>\n",
       "      <td>-0.065085</td>\n",
       "      <td>-0.062205</td>\n",
       "      <td>0.002642</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.779192</td>\n",
       "      <td>0.077323</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.731602</td>\n",
       "      <td>0.075479</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.962567</td>\n",
       "      <td>0.912879</td>\n",
       "      <td>0.908641</td>\n",
       "      <td>0.934174</td>\n",
       "      <td>0.944911</td>\n",
       "      <td>0.932634</td>\n",
       "      <td>0.020075</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.965068e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.118825</td>\n",
       "      <td>0.023532</td>\n",
       "      <td>0.501602</td>\n",
       "      <td>0.021867</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.097672</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.09767211400638387, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.911290</td>\n",
       "      <td>0.911290</td>\n",
       "      <td>0.910569</td>\n",
       "      <td>0.894309</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.914110</td>\n",
       "      <td>0.015877</td>\n",
       "      <td>33</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.744186</td>\n",
       "      <td>0.717949</td>\n",
       "      <td>0.606061</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.711075</td>\n",
       "      <td>0.072278</td>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.221578</td>\n",
       "      <td>-0.250834</td>\n",
       "      <td>-0.259787</td>\n",
       "      <td>-0.269190</td>\n",
       "      <td>-0.223585</td>\n",
       "      <td>-0.244995</td>\n",
       "      <td>0.019209</td>\n",
       "      <td>44</td>\n",
       "      <td>-0.071666</td>\n",
       "      <td>-0.069258</td>\n",
       "      <td>-0.069895</td>\n",
       "      <td>-0.068604</td>\n",
       "      <td>-0.073306</td>\n",
       "      <td>-0.070546</td>\n",
       "      <td>0.001716</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.861531</td>\n",
       "      <td>0.080108</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.620346</td>\n",
       "      <td>0.115752</td>\n",
       "      <td>45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.983957</td>\n",
       "      <td>0.952986</td>\n",
       "      <td>0.921242</td>\n",
       "      <td>0.935808</td>\n",
       "      <td>0.949346</td>\n",
       "      <td>0.948668</td>\n",
       "      <td>0.020890</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>8.028299</td>\n",
       "      <td>1.892440</td>\n",
       "      <td>0.547618</td>\n",
       "      <td>0.008955</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.074045</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.07404465173409036, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.903226</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.894309</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>0.902780</td>\n",
       "      <td>0.025597</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.628571</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.640008</td>\n",
       "      <td>0.108835</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.226146</td>\n",
       "      <td>-0.256195</td>\n",
       "      <td>-0.253226</td>\n",
       "      <td>-0.267870</td>\n",
       "      <td>-0.226986</td>\n",
       "      <td>-0.246085</td>\n",
       "      <td>0.016674</td>\n",
       "      <td>45</td>\n",
       "      <td>-0.072823</td>\n",
       "      <td>-0.070059</td>\n",
       "      <td>-0.070878</td>\n",
       "      <td>-0.069382</td>\n",
       "      <td>-0.072836</td>\n",
       "      <td>-0.071196</td>\n",
       "      <td>0.001416</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.884133</td>\n",
       "      <td>0.074539</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.510390</td>\n",
       "      <td>0.131422</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.990642</td>\n",
       "      <td>0.953877</td>\n",
       "      <td>0.942169</td>\n",
       "      <td>0.944911</td>\n",
       "      <td>0.951681</td>\n",
       "      <td>0.956656</td>\n",
       "      <td>0.017523</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10.969703</td>\n",
       "      <td>0.091686</td>\n",
       "      <td>0.527240</td>\n",
       "      <td>0.035242</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.948886</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.9488855372533332, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.918699</td>\n",
       "      <td>0.894309</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.912510</td>\n",
       "      <td>0.026677</td>\n",
       "      <td>37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.744901</td>\n",
       "      <td>0.067356</td>\n",
       "      <td>22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.206337</td>\n",
       "      <td>-0.300663</td>\n",
       "      <td>-0.275821</td>\n",
       "      <td>-0.247293</td>\n",
       "      <td>-0.220008</td>\n",
       "      <td>-0.250024</td>\n",
       "      <td>0.034776</td>\n",
       "      <td>46</td>\n",
       "      <td>-0.064265</td>\n",
       "      <td>-0.056333</td>\n",
       "      <td>-0.061530</td>\n",
       "      <td>-0.062128</td>\n",
       "      <td>-0.064316</td>\n",
       "      <td>-0.061714</td>\n",
       "      <td>0.002913</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.775299</td>\n",
       "      <td>0.097450</td>\n",
       "      <td>49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.722078</td>\n",
       "      <td>0.060569</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.957219</td>\n",
       "      <td>0.903298</td>\n",
       "      <td>0.904815</td>\n",
       "      <td>0.931373</td>\n",
       "      <td>0.941410</td>\n",
       "      <td>0.927623</td>\n",
       "      <td>0.020938</td>\n",
       "      <td>49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.557072</td>\n",
       "      <td>0.057681</td>\n",
       "      <td>0.543983</td>\n",
       "      <td>0.014857</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>0.950714</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.9507143064099162, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>0.918699</td>\n",
       "      <td>0.894309</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.912510</td>\n",
       "      <td>0.026677</td>\n",
       "      <td>37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.744901</td>\n",
       "      <td>0.067356</td>\n",
       "      <td>22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.206337</td>\n",
       "      <td>-0.300663</td>\n",
       "      <td>-0.275821</td>\n",
       "      <td>-0.247293</td>\n",
       "      <td>-0.220008</td>\n",
       "      <td>-0.250024</td>\n",
       "      <td>0.034776</td>\n",
       "      <td>46</td>\n",
       "      <td>-0.064265</td>\n",
       "      <td>-0.056333</td>\n",
       "      <td>-0.061530</td>\n",
       "      <td>-0.062128</td>\n",
       "      <td>-0.064316</td>\n",
       "      <td>-0.061714</td>\n",
       "      <td>0.002913</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.775299</td>\n",
       "      <td>0.097450</td>\n",
       "      <td>49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.722078</td>\n",
       "      <td>0.060569</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.957219</td>\n",
       "      <td>0.903298</td>\n",
       "      <td>0.904815</td>\n",
       "      <td>0.931373</td>\n",
       "      <td>0.941410</td>\n",
       "      <td>0.927623</td>\n",
       "      <td>0.020938</td>\n",
       "      <td>49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>7.543915</td>\n",
       "      <td>1.482365</td>\n",
       "      <td>0.576233</td>\n",
       "      <td>0.045685</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.063558</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.06355835028602363, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.879032</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.869919</td>\n",
       "      <td>0.902439</td>\n",
       "      <td>0.883307</td>\n",
       "      <td>0.011005</td>\n",
       "      <td>49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.532317</td>\n",
       "      <td>0.043189</td>\n",
       "      <td>49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.233811</td>\n",
       "      <td>-0.262821</td>\n",
       "      <td>-0.261573</td>\n",
       "      <td>-0.278747</td>\n",
       "      <td>-0.235324</td>\n",
       "      <td>-0.254455</td>\n",
       "      <td>0.017337</td>\n",
       "      <td>48</td>\n",
       "      <td>-0.075108</td>\n",
       "      <td>-0.072985</td>\n",
       "      <td>-0.074612</td>\n",
       "      <td>-0.072683</td>\n",
       "      <td>-0.075574</td>\n",
       "      <td>-0.074192</td>\n",
       "      <td>0.001154</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.896970</td>\n",
       "      <td>0.091335</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.379654</td>\n",
       "      <td>0.034404</td>\n",
       "      <td>49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.993316</td>\n",
       "      <td>0.953877</td>\n",
       "      <td>0.937669</td>\n",
       "      <td>0.941877</td>\n",
       "      <td>0.951914</td>\n",
       "      <td>0.955730</td>\n",
       "      <td>0.019742</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6.531376</td>\n",
       "      <td>0.044929</td>\n",
       "      <td>0.485382</td>\n",
       "      <td>0.027477</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 2}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.04645</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.046450412719997725, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.862903</td>\n",
       "      <td>0.862903</td>\n",
       "      <td>0.869919</td>\n",
       "      <td>0.853659</td>\n",
       "      <td>0.894309</td>\n",
       "      <td>0.868739</td>\n",
       "      <td>0.013789</td>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.551724</td>\n",
       "      <td>0.423255</td>\n",
       "      <td>0.075304</td>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.248008</td>\n",
       "      <td>-0.271295</td>\n",
       "      <td>-0.271016</td>\n",
       "      <td>-0.290419</td>\n",
       "      <td>-0.250062</td>\n",
       "      <td>-0.266160</td>\n",
       "      <td>0.015666</td>\n",
       "      <td>49</td>\n",
       "      <td>-0.078929</td>\n",
       "      <td>-0.076782</td>\n",
       "      <td>-0.076937</td>\n",
       "      <td>-0.076150</td>\n",
       "      <td>-0.078959</td>\n",
       "      <td>-0.077551</td>\n",
       "      <td>0.001167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.917857</td>\n",
       "      <td>0.112712</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.278355</td>\n",
       "      <td>0.061566</td>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.991979</td>\n",
       "      <td>0.962567</td>\n",
       "      <td>0.945545</td>\n",
       "      <td>0.936975</td>\n",
       "      <td>0.950047</td>\n",
       "      <td>0.957422</td>\n",
       "      <td>0.019153</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.965068e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11.028822</td>\n",
       "      <td>0.070761</td>\n",
       "      <td>0.512857</td>\n",
       "      <td>0.020690</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{0: 1, 1: 1}</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>None</td>\n",
       "      <td>0.90932</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>{'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.9093204020787821, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}</td>\n",
       "      <td>0.927419</td>\n",
       "      <td>0.846774</td>\n",
       "      <td>0.943089</td>\n",
       "      <td>0.918699</td>\n",
       "      <td>0.959350</td>\n",
       "      <td>0.919066</td>\n",
       "      <td>0.038719</td>\n",
       "      <td>14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.595745</td>\n",
       "      <td>0.837209</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.763415</td>\n",
       "      <td>0.097406</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.185723</td>\n",
       "      <td>-0.275228</td>\n",
       "      <td>-0.226293</td>\n",
       "      <td>-0.217613</td>\n",
       "      <td>-0.450749</td>\n",
       "      <td>-0.271121</td>\n",
       "      <td>0.094285</td>\n",
       "      <td>50</td>\n",
       "      <td>-0.060398</td>\n",
       "      <td>-0.053580</td>\n",
       "      <td>-0.058312</td>\n",
       "      <td>-0.061488</td>\n",
       "      <td>-0.059363</td>\n",
       "      <td>-0.058628</td>\n",
       "      <td>0.002736</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.804605</td>\n",
       "      <td>0.124976</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.732035</td>\n",
       "      <td>0.088341</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.977273</td>\n",
       "      <td>0.914216</td>\n",
       "      <td>0.939244</td>\n",
       "      <td>0.959384</td>\n",
       "      <td>0.944678</td>\n",
       "      <td>0.946959</td>\n",
       "      <td>0.021024</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "44       9.588275      0.757163         0.558042        0.100988   \n",
       "40       6.326918      0.050977         0.544000        0.015184   \n",
       "9        7.209980      0.403286         0.543333        0.096212   \n",
       "45      10.988012      0.069876         0.627750        0.079751   \n",
       "46      10.915706      0.108944         0.591122        0.093380   \n",
       "48       3.940238      0.040669         0.340701        0.015029   \n",
       "33      12.743233      0.092604         0.542062        0.026254   \n",
       "30       7.838939      0.080844         0.503775        0.012319   \n",
       "3       12.360978      0.352741         0.543934        0.070100   \n",
       "36      10.395394      0.128204         0.681702        0.084981   \n",
       "27      12.206363      0.115937         0.548270        0.047225   \n",
       "35      11.062069      0.824333         0.560568        0.059657   \n",
       "34      12.567641      0.096218         0.577294        0.082639   \n",
       "5       12.378783      0.059603         0.501049        0.038388   \n",
       "11       7.649173      0.085965         0.553975        0.099921   \n",
       "15       6.433379      0.037266         0.489659        0.024110   \n",
       "8        6.824133      0.049044         0.520624        0.011109   \n",
       "23       9.099431      0.070639         0.531481        0.049644   \n",
       "32      12.791049      0.063078         0.565234        0.044141   \n",
       "19      11.181804      0.068067         0.494118        0.030218   \n",
       "10       7.768688      0.078492         0.581195        0.062904   \n",
       "29       7.858336      0.068936         0.531150        0.024365   \n",
       "42       9.288925      0.131031         0.532982        0.045693   \n",
       "7        6.943363      0.047121         0.490711        0.015945   \n",
       "28      11.465425      1.696956         0.499988        0.026603   \n",
       "24       9.050263      0.184995         0.564084        0.098950   \n",
       "4       12.420383      0.141249         0.550584        0.090604   \n",
       "47      11.036400      0.091750         0.501910        0.019297   \n",
       "20      11.058028      0.123231         0.520777        0.079112   \n",
       "17      11.034512      0.059276         0.492306        0.023881   \n",
       "6        9.094939      2.649044         0.502028        0.031890   \n",
       "31       7.747515      0.066473         0.498658        0.028479   \n",
       "12       7.446534      0.395198         0.565598        0.080060   \n",
       "22       9.849723      0.924541         0.570452        0.091598   \n",
       "13       6.487520      0.066567         0.491478        0.018379   \n",
       "1       11.720870      0.103758         0.498854        0.027555   \n",
       "2       11.839932      0.056858         0.479022        0.023852   \n",
       "26      12.348436      0.101040         0.548394        0.058050   \n",
       "43       9.244633      0.046252         0.510063        0.011509   \n",
       "49       3.899914      0.030608         0.333621        0.006268   \n",
       "37      10.442903      0.101354         0.573494        0.109785   \n",
       "39       6.380080      0.028943         0.553672        0.025920   \n",
       "25      10.243565      1.557131         0.535053        0.076921   \n",
       "18      11.118825      0.023532         0.501602        0.021867   \n",
       "38       8.028299      1.892440         0.547618        0.008955   \n",
       "16      10.969703      0.091686         0.527240        0.035242   \n",
       "0       11.557072      0.057681         0.543983        0.014857   \n",
       "41       7.543915      1.482365         0.576233        0.045685   \n",
       "14       6.531376      0.044929         0.485382        0.027477   \n",
       "21      11.028822      0.070761         0.512857        0.020690   \n",
       "\n",
       "   param_forest__bootstrap param_forest__ccp_alpha param_forest__class_weight  \\\n",
       "44                    True                     0.0               {0: 1, 1: 1}   \n",
       "40                    True                     0.0               {0: 1, 1: 1}   \n",
       "9                     True                     0.0               {0: 1, 1: 2}   \n",
       "45                    True                     0.0               {0: 1, 1: 1}   \n",
       "46                    True                     0.0               {0: 1, 1: 1}   \n",
       "48                    True                     0.0               {0: 1, 1: 1}   \n",
       "33                    True                     0.0               {0: 1, 1: 1}   \n",
       "30                    True                     0.0               {0: 1, 1: 2}   \n",
       "3                     True                     0.0               {0: 1, 1: 1}   \n",
       "36                    True                     0.0               {0: 1, 1: 2}   \n",
       "27                    True                     0.0               {0: 1, 1: 2}   \n",
       "35                    True                     0.0               {0: 1, 1: 2}   \n",
       "34                    True                     0.0               {0: 1, 1: 1}   \n",
       "5                     True                     0.0               {0: 1, 1: 2}   \n",
       "11                    True                     0.0               {0: 1, 1: 2}   \n",
       "15                    True                     0.0               {0: 1, 1: 1}   \n",
       "8                     True                     0.0               {0: 1, 1: 2}   \n",
       "23                    True                     0.0               {0: 1, 1: 2}   \n",
       "32                    True                     0.0               {0: 1, 1: 2}   \n",
       "19                    True                     0.0               {0: 1, 1: 2}   \n",
       "10                    True                     0.0               {0: 1, 1: 1}   \n",
       "29                    True                     0.0               {0: 1, 1: 1}   \n",
       "42                    True                     0.0               {0: 1, 1: 1}   \n",
       "7                     True                     0.0               {0: 1, 1: 2}   \n",
       "28                    True                     0.0               {0: 1, 1: 2}   \n",
       "24                    True                     0.0               {0: 1, 1: 2}   \n",
       "4                     True                     0.0               {0: 1, 1: 2}   \n",
       "47                    True                     0.0               {0: 1, 1: 1}   \n",
       "20                    True                     0.0               {0: 1, 1: 1}   \n",
       "17                    True                     0.0               {0: 1, 1: 2}   \n",
       "6                     True                     0.0               {0: 1, 1: 2}   \n",
       "31                    True                     0.0               {0: 1, 1: 1}   \n",
       "12                    True                     0.0               {0: 1, 1: 2}   \n",
       "22                    True                     0.0               {0: 1, 1: 2}   \n",
       "13                    True                     0.0               {0: 1, 1: 1}   \n",
       "1                     True                     0.0               {0: 1, 1: 1}   \n",
       "2                     True                     0.0               {0: 1, 1: 1}   \n",
       "26                    True                     0.0               {0: 1, 1: 2}   \n",
       "43                    True                     0.0               {0: 1, 1: 1}   \n",
       "49                    True                     0.0               {0: 1, 1: 2}   \n",
       "37                    True                     0.0               {0: 1, 1: 1}   \n",
       "39                    True                     0.0               {0: 1, 1: 1}   \n",
       "25                    True                     0.0               {0: 1, 1: 1}   \n",
       "18                    True                     0.0               {0: 1, 1: 1}   \n",
       "38                    True                     0.0               {0: 1, 1: 1}   \n",
       "16                    True                     0.0               {0: 1, 1: 1}   \n",
       "0                     True                     0.0               {0: 1, 1: 1}   \n",
       "41                    True                     0.0               {0: 1, 1: 2}   \n",
       "14                    True                     0.0               {0: 1, 1: 2}   \n",
       "21                    True                     0.0               {0: 1, 1: 1}   \n",
       "\n",
       "   param_forest__criterion param_forest__max_depth param_forest__max_features  \\\n",
       "44                log_loss                    None                   0.472215   \n",
       "40                log_loss                    None                   0.623298   \n",
       "9                 log_loss                    None                   0.291229   \n",
       "45                log_loss                    None                   0.713245   \n",
       "46                log_loss                    None                   0.561277   \n",
       "48                log_loss                    None                   0.427541   \n",
       "33                log_loss                    None                   0.802197   \n",
       "30                log_loss                    None                   0.271349   \n",
       "3                 log_loss                    None                   0.866176   \n",
       "36                log_loss                    None                   0.815461   \n",
       "27                log_loss                    None                   0.921874   \n",
       "35                log_loss                    None                   0.198716   \n",
       "34                log_loss                    None                   0.986887   \n",
       "5                 log_loss                    None                    0.96991   \n",
       "11                    gini                    None                   0.366362   \n",
       "15                log_loss                    None                   0.170524   \n",
       "8                     gini                    None                   0.524756   \n",
       "23                    gini                    None                   0.520068   \n",
       "32                    gini                    None                   0.542696   \n",
       "19                    gini                    None                   0.440152   \n",
       "10                log_loss                    None                   0.139494   \n",
       "29                    gini                    None                    0.32533   \n",
       "42                    gini                    None                   0.325183   \n",
       "7                     gini                    None                   0.183405   \n",
       "28                    gini                    None                   0.195983   \n",
       "24                    gini                    None                   0.184854   \n",
       "4                     gini                    None                   0.708073   \n",
       "47                    gini                    None                   0.493796   \n",
       "20                    gini                    None                   0.495177   \n",
       "17                    gini                    None                   0.808397   \n",
       "6                     gini                    None                   0.212339   \n",
       "31                    gini                    None                   0.356753   \n",
       "12                    gini                    None                   0.785176   \n",
       "22                    gini                    None                   0.662522   \n",
       "13                    gini                    None                   0.514234   \n",
       "1                     gini                    None                   0.598658   \n",
       "2                     gini                    None                   0.155995   \n",
       "26                    gini                    None                   0.894827   \n",
       "43                    gini                    None                   0.637557   \n",
       "49                    gini                    None                   0.107891   \n",
       "37                    gini                    None                   0.729007   \n",
       "39                    gini                    None                   0.115869   \n",
       "25                    gini                    None                   0.775133   \n",
       "18                    gini                    None                   0.097672   \n",
       "38                log_loss                    None                   0.074045   \n",
       "16                    gini                    None                   0.948886   \n",
       "0                     gini                    None                   0.950714   \n",
       "41                log_loss                    None                   0.063558   \n",
       "14                log_loss                    None                    0.04645   \n",
       "21                log_loss                    None                    0.90932   \n",
       "\n",
       "   param_forest__max_leaf_nodes param_forest__max_samples  \\\n",
       "44                         None                      None   \n",
       "40                         None                      None   \n",
       "9                          None                      None   \n",
       "45                         None                      None   \n",
       "46                         None                      None   \n",
       "48                         None                      None   \n",
       "33                         None                      None   \n",
       "30                         None                      None   \n",
       "3                          None                      None   \n",
       "36                         None                      None   \n",
       "27                         None                      None   \n",
       "35                         None                      None   \n",
       "34                         None                      None   \n",
       "5                          None                      None   \n",
       "11                         None                      None   \n",
       "15                         None                      None   \n",
       "8                          None                      None   \n",
       "23                         None                      None   \n",
       "32                         None                      None   \n",
       "19                         None                      None   \n",
       "10                         None                      None   \n",
       "29                         None                      None   \n",
       "42                         None                      None   \n",
       "7                          None                      None   \n",
       "28                         None                      None   \n",
       "24                         None                      None   \n",
       "4                          None                      None   \n",
       "47                         None                      None   \n",
       "20                         None                      None   \n",
       "17                         None                      None   \n",
       "6                          None                      None   \n",
       "31                         None                      None   \n",
       "12                         None                      None   \n",
       "22                         None                      None   \n",
       "13                         None                      None   \n",
       "1                          None                      None   \n",
       "2                          None                      None   \n",
       "26                         None                      None   \n",
       "43                         None                      None   \n",
       "49                         None                      None   \n",
       "37                         None                      None   \n",
       "39                         None                      None   \n",
       "25                         None                      None   \n",
       "18                         None                      None   \n",
       "38                         None                      None   \n",
       "16                         None                      None   \n",
       "0                          None                      None   \n",
       "41                         None                      None   \n",
       "14                         None                      None   \n",
       "21                         None                      None   \n",
       "\n",
       "   param_forest__min_impurity_decrease param_forest__min_samples_leaf  \\\n",
       "44                                 0.0                              1   \n",
       "40                                 0.0                              1   \n",
       "9                                  0.0                              1   \n",
       "45                                 0.0                              1   \n",
       "46                                 0.0                              1   \n",
       "48                                 0.0                              1   \n",
       "33                                 0.0                              1   \n",
       "30                                 0.0                              1   \n",
       "3                                  0.0                              1   \n",
       "36                                 0.0                              1   \n",
       "27                                 0.0                              1   \n",
       "35                                 0.0                              1   \n",
       "34                                 0.0                              1   \n",
       "5                                  0.0                              1   \n",
       "11                                 0.0                              1   \n",
       "15                                 0.0                              1   \n",
       "8                                  0.0                              1   \n",
       "23                                 0.0                              1   \n",
       "32                                 0.0                              1   \n",
       "19                                 0.0                              1   \n",
       "10                                 0.0                              1   \n",
       "29                                 0.0                              1   \n",
       "42                                 0.0                              1   \n",
       "7                                  0.0                              1   \n",
       "28                                 0.0                              1   \n",
       "24                                 0.0                              1   \n",
       "4                                  0.0                              1   \n",
       "47                                 0.0                              1   \n",
       "20                                 0.0                              1   \n",
       "17                                 0.0                              1   \n",
       "6                                  0.0                              1   \n",
       "31                                 0.0                              1   \n",
       "12                                 0.0                              1   \n",
       "22                                 0.0                              1   \n",
       "13                                 0.0                              1   \n",
       "1                                  0.0                              1   \n",
       "2                                  0.0                              1   \n",
       "26                                 0.0                              1   \n",
       "43                                 0.0                              1   \n",
       "49                                 0.0                              1   \n",
       "37                                 0.0                              1   \n",
       "39                                 0.0                              1   \n",
       "25                                 0.0                              1   \n",
       "18                                 0.0                              1   \n",
       "38                                 0.0                              1   \n",
       "16                                 0.0                              1   \n",
       "0                                  0.0                              1   \n",
       "41                                 0.0                              1   \n",
       "14                                 0.0                              1   \n",
       "21                                 0.0                              1   \n",
       "\n",
       "   param_forest__min_samples_split param_forest__min_weight_fraction_leaf  \\\n",
       "44                               2                                    0.0   \n",
       "40                               2                                    0.0   \n",
       "9                                2                                    0.0   \n",
       "45                               2                                    0.0   \n",
       "46                               2                                    0.0   \n",
       "48                               2                                    0.0   \n",
       "33                               2                                    0.0   \n",
       "30                               2                                    0.0   \n",
       "3                                2                                    0.0   \n",
       "36                               2                                    0.0   \n",
       "27                               2                                    0.0   \n",
       "35                               2                                    0.0   \n",
       "34                               2                                    0.0   \n",
       "5                                2                                    0.0   \n",
       "11                               2                                    0.0   \n",
       "15                               2                                    0.0   \n",
       "8                                2                                    0.0   \n",
       "23                               2                                    0.0   \n",
       "32                               2                                    0.0   \n",
       "19                               2                                    0.0   \n",
       "10                               2                                    0.0   \n",
       "29                               2                                    0.0   \n",
       "42                               2                                    0.0   \n",
       "7                                2                                    0.0   \n",
       "28                               2                                    0.0   \n",
       "24                               2                                    0.0   \n",
       "4                                2                                    0.0   \n",
       "47                               2                                    0.0   \n",
       "20                               2                                    0.0   \n",
       "17                               2                                    0.0   \n",
       "6                                2                                    0.0   \n",
       "31                               2                                    0.0   \n",
       "12                               2                                    0.0   \n",
       "22                               2                                    0.0   \n",
       "13                               2                                    0.0   \n",
       "1                                2                                    0.0   \n",
       "2                                2                                    0.0   \n",
       "26                               2                                    0.0   \n",
       "43                               2                                    0.0   \n",
       "49                               2                                    0.0   \n",
       "37                               2                                    0.0   \n",
       "39                               2                                    0.0   \n",
       "25                               2                                    0.0   \n",
       "18                               2                                    0.0   \n",
       "38                               2                                    0.0   \n",
       "16                               2                                    0.0   \n",
       "0                                2                                    0.0   \n",
       "41                               2                                    0.0   \n",
       "14                               2                                    0.0   \n",
       "21                               2                                    0.0   \n",
       "\n",
       "   param_forest__n_estimators param_forest__n_jobs param_forest__oob_score  \\\n",
       "44                       1000                   -1                   False   \n",
       "40                       1000                   -1                   False   \n",
       "9                        1000                   -1                   False   \n",
       "45                       1000                   -1                   False   \n",
       "46                       1000                   -1                   False   \n",
       "48                       1000                   -1                   False   \n",
       "33                       1000                   -1                   False   \n",
       "30                       1000                   -1                   False   \n",
       "3                        1000                   -1                   False   \n",
       "36                       1000                   -1                   False   \n",
       "27                       1000                   -1                   False   \n",
       "35                       1000                   -1                   False   \n",
       "34                       1000                   -1                   False   \n",
       "5                        1000                   -1                   False   \n",
       "11                       1000                   -1                   False   \n",
       "15                       1000                   -1                   False   \n",
       "8                        1000                   -1                   False   \n",
       "23                       1000                   -1                   False   \n",
       "32                       1000                   -1                   False   \n",
       "19                       1000                   -1                   False   \n",
       "10                       1000                   -1                   False   \n",
       "29                       1000                   -1                   False   \n",
       "42                       1000                   -1                   False   \n",
       "7                        1000                   -1                   False   \n",
       "28                       1000                   -1                   False   \n",
       "24                       1000                   -1                   False   \n",
       "4                        1000                   -1                   False   \n",
       "47                       1000                   -1                   False   \n",
       "20                       1000                   -1                   False   \n",
       "17                       1000                   -1                   False   \n",
       "6                        1000                   -1                   False   \n",
       "31                       1000                   -1                   False   \n",
       "12                       1000                   -1                   False   \n",
       "22                       1000                   -1                   False   \n",
       "13                       1000                   -1                   False   \n",
       "1                        1000                   -1                   False   \n",
       "2                        1000                   -1                   False   \n",
       "26                       1000                   -1                   False   \n",
       "43                       1000                   -1                   False   \n",
       "49                       1000                   -1                   False   \n",
       "37                       1000                   -1                   False   \n",
       "39                       1000                   -1                   False   \n",
       "25                       1000                   -1                   False   \n",
       "18                       1000                   -1                   False   \n",
       "38                       1000                   -1                   False   \n",
       "16                       1000                   -1                   False   \n",
       "0                        1000                   -1                   False   \n",
       "41                       1000                   -1                   False   \n",
       "14                       1000                   -1                   False   \n",
       "21                       1000                   -1                   False   \n",
       "\n",
       "   param_forest__random_state param_forest__verbose param_forest__warm_start  \\\n",
       "44                         42                    10                     True   \n",
       "40                         42                    10                     True   \n",
       "9                          42                    10                     True   \n",
       "45                         42                    10                     True   \n",
       "46                         42                    10                     True   \n",
       "48                         42                    10                     True   \n",
       "33                         42                    10                     True   \n",
       "30                         42                    10                     True   \n",
       "3                          42                    10                     True   \n",
       "36                         42                    10                     True   \n",
       "27                         42                    10                     True   \n",
       "35                         42                    10                     True   \n",
       "34                         42                    10                     True   \n",
       "5                          42                    10                     True   \n",
       "11                         42                    10                     True   \n",
       "15                         42                    10                     True   \n",
       "8                          42                    10                     True   \n",
       "23                         42                    10                     True   \n",
       "32                         42                    10                     True   \n",
       "19                         42                    10                     True   \n",
       "10                         42                    10                     True   \n",
       "29                         42                    10                     True   \n",
       "42                         42                    10                     True   \n",
       "7                          42                    10                     True   \n",
       "28                         42                    10                     True   \n",
       "24                         42                    10                     True   \n",
       "4                          42                    10                     True   \n",
       "47                         42                    10                     True   \n",
       "20                         42                    10                     True   \n",
       "17                         42                    10                     True   \n",
       "6                          42                    10                     True   \n",
       "31                         42                    10                     True   \n",
       "12                         42                    10                     True   \n",
       "22                         42                    10                     True   \n",
       "13                         42                    10                     True   \n",
       "1                          42                    10                     True   \n",
       "2                          42                    10                     True   \n",
       "26                         42                    10                     True   \n",
       "43                         42                    10                     True   \n",
       "49                         42                    10                     True   \n",
       "37                         42                    10                     True   \n",
       "39                         42                    10                     True   \n",
       "25                         42                    10                     True   \n",
       "18                         42                    10                     True   \n",
       "38                         42                    10                     True   \n",
       "16                         42                    10                     True   \n",
       "0                          42                    10                     True   \n",
       "41                         42                    10                     True   \n",
       "14                         42                    10                     True   \n",
       "21                         42                    10                     True   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             params  \\\n",
       "44    {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.4722149251619493, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "40    {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.6232981268275579, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "9     {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.2912291401980419, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "45     {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.713244787222995, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "46    {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.5612771975694962, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "48   {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.42754101835854963, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "33    {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.8021969807540397, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "30    {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.2713490317738959, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "3     {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.8661761457749352, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "36    {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.8154614284548342, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "27    {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.9218742350231168, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "35    {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.1987156815341724, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "34    {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.9868869366005173, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "5     {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.9699098521619943, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "11        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.3663618432936917, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "15   {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.17052412368729153, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "8         {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.5247564316322378, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "23        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.5200680211778108, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "32        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.5426960831582485, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "19        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.4401524937396013, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "10   {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.13949386065204183, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "29       {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.32533033076326434, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "42       {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.32518332202674705, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "7        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.18340450985343382, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "28        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.1959828624191452, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "24       {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.18485445552552704, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "4         {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.7080725777960455, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "47       {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.49379559636439074, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "20        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.4951769101112702, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "17        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.8083973481164611, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "6        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.21233911067827616, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "31        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.3567533266935893, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "12        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.7851759613930136, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "22         {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.662522284353982, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "13        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.5142344384136116, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "1         {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.5986584841970366, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "2        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.15599452033620265, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "26        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.8948273504276488, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "43        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.6375574713552131, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "49       {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.10789142699330445, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "37        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.7290071680409873, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "39       {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.11586905952512971, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "25        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.7751328233611146, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "18       {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.09767211400638387, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "38   {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.07404465173409036, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "16        {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.9488855372533332, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "0         {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'gini', 'forest__max_depth': None, 'forest__max_features': 0.9507143064099162, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "41   {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.06355835028602363, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "14  {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 2}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.046450412719997725, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "21    {'forest__bootstrap': True, 'forest__ccp_alpha': 0.0, 'forest__class_weight': {0: 1, 1: 1}, 'forest__criterion': 'log_loss', 'forest__max_depth': None, 'forest__max_features': 0.9093204020787821, 'forest__max_leaf_nodes': None, 'forest__max_samples': None, 'forest__min_impurity_decrease': 0.0, 'forest__min_samples_leaf': 1, 'forest__min_samples_split': 2, 'forest__min_weight_fraction_leaf': 0.0, 'forest__n_estimators': 1000, 'forest__n_jobs': -1, 'forest__oob_score': False, 'forest__random_state': 42, 'forest__verbose': 10, 'forest__warm_start': True}   \n",
       "\n",
       "    split0_test_accuracy  split1_test_accuracy  split2_test_accuracy  \\\n",
       "44              0.943548              0.862903              0.934959   \n",
       "40              0.935484              0.862903              0.934959   \n",
       "9               0.927419              0.870968              0.926829   \n",
       "45              0.943548              0.862903              0.943089   \n",
       "46              0.935484              0.862903              0.934959   \n",
       "48              0.943548              0.870968              0.934959   \n",
       "33              0.927419              0.854839              0.943089   \n",
       "30              0.919355              0.862903              0.926829   \n",
       "3               0.927419              0.854839              0.943089   \n",
       "36              0.927419              0.854839              0.934959   \n",
       "27              0.919355              0.846774              0.934959   \n",
       "35              0.919355              0.870968              0.934959   \n",
       "34              0.927419              0.846774              0.943089   \n",
       "5               0.919355              0.854839              0.926829   \n",
       "11              0.927419              0.879032              0.926829   \n",
       "15              0.927419              0.887097              0.926829   \n",
       "8               0.919355              0.870968              0.926829   \n",
       "23              0.919355              0.870968              0.926829   \n",
       "32              0.911290              0.870968              0.926829   \n",
       "19              0.919355              0.879032              0.926829   \n",
       "10              0.911290              0.870968              0.926829   \n",
       "29              0.935484              0.887097              0.918699   \n",
       "42              0.935484              0.887097              0.918699   \n",
       "7               0.919355              0.887097              0.934959   \n",
       "28              0.919355              0.887097              0.934959   \n",
       "24              0.919355              0.887097              0.934959   \n",
       "4               0.919355              0.870968              0.926829   \n",
       "47              0.943548              0.887097              0.918699   \n",
       "20              0.943548              0.887097              0.918699   \n",
       "17              0.919355              0.870968              0.918699   \n",
       "6               0.919355              0.887097              0.934959   \n",
       "31              0.943548              0.895161              0.918699   \n",
       "12              0.911290              0.870968              0.926829   \n",
       "22              0.927419              0.870968              0.926829   \n",
       "13              0.943548              0.887097              0.918699   \n",
       "1               0.943548              0.887097              0.918699   \n",
       "2               0.927419              0.911290              0.910569   \n",
       "26              0.911290              0.862903              0.926829   \n",
       "43              0.943548              0.887097              0.918699   \n",
       "49              0.903226              0.887097              0.902439   \n",
       "37              0.935484              0.887097              0.918699   \n",
       "39              0.903226              0.911290              0.902439   \n",
       "25              0.935484              0.887097              0.918699   \n",
       "18              0.911290              0.911290              0.910569   \n",
       "38              0.903226              0.887097              0.894309   \n",
       "16              0.935484              0.870968              0.918699   \n",
       "0               0.935484              0.870968              0.918699   \n",
       "41              0.887097              0.879032              0.878049   \n",
       "14              0.862903              0.862903              0.869919   \n",
       "21              0.927419              0.846774              0.943089   \n",
       "\n",
       "    split3_test_accuracy  split4_test_accuracy  mean_test_accuracy  \\\n",
       "44              0.910569              0.967480            0.923892   \n",
       "40              0.910569              0.959350            0.920653   \n",
       "9               0.894309              0.959350            0.915775   \n",
       "45              0.910569              0.959350            0.923892   \n",
       "46              0.910569              0.959350            0.920653   \n",
       "48              0.910569              0.951220            0.922253   \n",
       "33              0.910569              0.959350            0.919053   \n",
       "30              0.894309              0.951220            0.910923   \n",
       "3               0.910569              0.959350            0.919053   \n",
       "36              0.910569              0.959350            0.917427   \n",
       "27              0.902439              0.959350            0.912575   \n",
       "35              0.894309              0.951220            0.914162   \n",
       "34              0.910569              0.959350            0.917440   \n",
       "5               0.910569              0.959350            0.914188   \n",
       "11              0.894309              0.951220            0.915762   \n",
       "15              0.886179              0.951220            0.915749   \n",
       "8               0.894309              0.959350            0.914162   \n",
       "23              0.894309              0.959350            0.914162   \n",
       "32              0.902439              0.951220            0.912549   \n",
       "19              0.902439              0.951220            0.915775   \n",
       "10              0.886179              0.943089            0.907671   \n",
       "29              0.902439              0.967480            0.922240   \n",
       "42              0.902439              0.967480            0.922240   \n",
       "7               0.878049              0.943089            0.912510   \n",
       "28              0.878049              0.943089            0.912510   \n",
       "24              0.878049              0.943089            0.912510   \n",
       "4               0.910569              0.951220            0.915788   \n",
       "47              0.902439              0.951220            0.920601   \n",
       "20              0.902439              0.951220            0.920601   \n",
       "17              0.902439              0.943089            0.910910   \n",
       "6               0.886179              0.951220            0.915762   \n",
       "31              0.902439              0.943089            0.920587   \n",
       "12              0.910569              0.943089            0.912549   \n",
       "22              0.910569              0.943089            0.915775   \n",
       "13              0.910569              0.959350            0.923853   \n",
       "1               0.902439              0.951220            0.920601   \n",
       "2               0.902439              0.951220            0.920587   \n",
       "26              0.910569              0.943089            0.910936   \n",
       "43              0.894309              0.943089            0.917349   \n",
       "49              0.886179              0.951220            0.906032   \n",
       "37              0.894309              0.951220            0.917362   \n",
       "39              0.886179              0.951220            0.910871   \n",
       "25              0.894309              0.943089            0.915736   \n",
       "18              0.894309              0.943089            0.914110   \n",
       "38              0.878049              0.951220            0.902780   \n",
       "16              0.894309              0.943089            0.912510   \n",
       "0               0.894309              0.943089            0.912510   \n",
       "41              0.869919              0.902439            0.883307   \n",
       "14              0.853659              0.894309            0.868739   \n",
       "21              0.918699              0.959350            0.919066   \n",
       "\n",
       "    std_test_accuracy  rank_test_accuracy  split0_train_accuracy  \\\n",
       "44           0.035513                   1                    1.0   \n",
       "40           0.032738                   7                    1.0   \n",
       "9            0.030414                  23                    1.0   \n",
       "45           0.034378                   1                    1.0   \n",
       "46           0.032738                   7                    1.0   \n",
       "48           0.029054                   4                    1.0   \n",
       "33           0.035964                  15                    1.0   \n",
       "30           0.030099                  43                    1.0   \n",
       "3            0.035964                  15                    1.0   \n",
       "36           0.035012                  18                    1.0   \n",
       "27           0.037861                  34                    1.0   \n",
       "35           0.028610                  30                    1.0   \n",
       "34           0.038871                  17                    1.0   \n",
       "5            0.033946                  29                    1.0   \n",
       "11           0.025787                  25                    1.0   \n",
       "15           0.025347                  27                    1.0   \n",
       "8            0.029964                  30                    1.0   \n",
       "23           0.029964                  30                    1.0   \n",
       "32           0.026579                  35                    1.0   \n",
       "19           0.024163                  23                    1.0   \n",
       "10           0.026244                  46                    1.0   \n",
       "29           0.027791                   5                    1.0   \n",
       "42           0.027791                   5                    1.0   \n",
       "7            0.025766                  37                    1.0   \n",
       "28           0.025766                  37                    1.0   \n",
       "24           0.025766                  37                    1.0   \n",
       "4            0.026177                  21                    1.0   \n",
       "47           0.024166                   9                    1.0   \n",
       "20           0.024166                   9                    1.0   \n",
       "17           0.023810                  44                    1.0   \n",
       "6            0.025828                  26                    1.0   \n",
       "31           0.020065                  12                    1.0   \n",
       "12           0.023963                  35                    1.0   \n",
       "22           0.024652                  22                    1.0   \n",
       "13           0.025306                   3                    1.0   \n",
       "1            0.024166                   9                    1.0   \n",
       "2            0.017327                  13                    1.0   \n",
       "26           0.026810                  42                    1.0   \n",
       "43           0.023651                  20                    1.0   \n",
       "49           0.023729                  47                    1.0   \n",
       "37           0.024182                  19                    1.0   \n",
       "39           0.021760                  45                    1.0   \n",
       "25           0.022029                  28                    1.0   \n",
       "18           0.015877                  33                    1.0   \n",
       "38           0.025597                  48                    1.0   \n",
       "16           0.026677                  37                    1.0   \n",
       "0            0.026677                  37                    1.0   \n",
       "41           0.011005                  49                    1.0   \n",
       "14           0.013789                  50                    1.0   \n",
       "21           0.038719                  14                    1.0   \n",
       "\n",
       "    split1_train_accuracy  split2_train_accuracy  split3_train_accuracy  \\\n",
       "44                    1.0                    1.0                    1.0   \n",
       "40                    1.0                    1.0                    1.0   \n",
       "9                     1.0                    1.0                    1.0   \n",
       "45                    1.0                    1.0                    1.0   \n",
       "46                    1.0                    1.0                    1.0   \n",
       "48                    1.0                    1.0                    1.0   \n",
       "33                    1.0                    1.0                    1.0   \n",
       "30                    1.0                    1.0                    1.0   \n",
       "3                     1.0                    1.0                    1.0   \n",
       "36                    1.0                    1.0                    1.0   \n",
       "27                    1.0                    1.0                    1.0   \n",
       "35                    1.0                    1.0                    1.0   \n",
       "34                    1.0                    1.0                    1.0   \n",
       "5                     1.0                    1.0                    1.0   \n",
       "11                    1.0                    1.0                    1.0   \n",
       "15                    1.0                    1.0                    1.0   \n",
       "8                     1.0                    1.0                    1.0   \n",
       "23                    1.0                    1.0                    1.0   \n",
       "32                    1.0                    1.0                    1.0   \n",
       "19                    1.0                    1.0                    1.0   \n",
       "10                    1.0                    1.0                    1.0   \n",
       "29                    1.0                    1.0                    1.0   \n",
       "42                    1.0                    1.0                    1.0   \n",
       "7                     1.0                    1.0                    1.0   \n",
       "28                    1.0                    1.0                    1.0   \n",
       "24                    1.0                    1.0                    1.0   \n",
       "4                     1.0                    1.0                    1.0   \n",
       "47                    1.0                    1.0                    1.0   \n",
       "20                    1.0                    1.0                    1.0   \n",
       "17                    1.0                    1.0                    1.0   \n",
       "6                     1.0                    1.0                    1.0   \n",
       "31                    1.0                    1.0                    1.0   \n",
       "12                    1.0                    1.0                    1.0   \n",
       "22                    1.0                    1.0                    1.0   \n",
       "13                    1.0                    1.0                    1.0   \n",
       "1                     1.0                    1.0                    1.0   \n",
       "2                     1.0                    1.0                    1.0   \n",
       "26                    1.0                    1.0                    1.0   \n",
       "43                    1.0                    1.0                    1.0   \n",
       "49                    1.0                    1.0                    1.0   \n",
       "37                    1.0                    1.0                    1.0   \n",
       "39                    1.0                    1.0                    1.0   \n",
       "25                    1.0                    1.0                    1.0   \n",
       "18                    1.0                    1.0                    1.0   \n",
       "38                    1.0                    1.0                    1.0   \n",
       "16                    1.0                    1.0                    1.0   \n",
       "0                     1.0                    1.0                    1.0   \n",
       "41                    1.0                    1.0                    1.0   \n",
       "14                    1.0                    1.0                    1.0   \n",
       "21                    1.0                    1.0                    1.0   \n",
       "\n",
       "    split4_train_accuracy  mean_train_accuracy  std_train_accuracy  \\\n",
       "44                    1.0                  1.0                 0.0   \n",
       "40                    1.0                  1.0                 0.0   \n",
       "9                     1.0                  1.0                 0.0   \n",
       "45                    1.0                  1.0                 0.0   \n",
       "46                    1.0                  1.0                 0.0   \n",
       "48                    1.0                  1.0                 0.0   \n",
       "33                    1.0                  1.0                 0.0   \n",
       "30                    1.0                  1.0                 0.0   \n",
       "3                     1.0                  1.0                 0.0   \n",
       "36                    1.0                  1.0                 0.0   \n",
       "27                    1.0                  1.0                 0.0   \n",
       "35                    1.0                  1.0                 0.0   \n",
       "34                    1.0                  1.0                 0.0   \n",
       "5                     1.0                  1.0                 0.0   \n",
       "11                    1.0                  1.0                 0.0   \n",
       "15                    1.0                  1.0                 0.0   \n",
       "8                     1.0                  1.0                 0.0   \n",
       "23                    1.0                  1.0                 0.0   \n",
       "32                    1.0                  1.0                 0.0   \n",
       "19                    1.0                  1.0                 0.0   \n",
       "10                    1.0                  1.0                 0.0   \n",
       "29                    1.0                  1.0                 0.0   \n",
       "42                    1.0                  1.0                 0.0   \n",
       "7                     1.0                  1.0                 0.0   \n",
       "28                    1.0                  1.0                 0.0   \n",
       "24                    1.0                  1.0                 0.0   \n",
       "4                     1.0                  1.0                 0.0   \n",
       "47                    1.0                  1.0                 0.0   \n",
       "20                    1.0                  1.0                 0.0   \n",
       "17                    1.0                  1.0                 0.0   \n",
       "6                     1.0                  1.0                 0.0   \n",
       "31                    1.0                  1.0                 0.0   \n",
       "12                    1.0                  1.0                 0.0   \n",
       "22                    1.0                  1.0                 0.0   \n",
       "13                    1.0                  1.0                 0.0   \n",
       "1                     1.0                  1.0                 0.0   \n",
       "2                     1.0                  1.0                 0.0   \n",
       "26                    1.0                  1.0                 0.0   \n",
       "43                    1.0                  1.0                 0.0   \n",
       "49                    1.0                  1.0                 0.0   \n",
       "37                    1.0                  1.0                 0.0   \n",
       "39                    1.0                  1.0                 0.0   \n",
       "25                    1.0                  1.0                 0.0   \n",
       "18                    1.0                  1.0                 0.0   \n",
       "38                    1.0                  1.0                 0.0   \n",
       "16                    1.0                  1.0                 0.0   \n",
       "0                     1.0                  1.0                 0.0   \n",
       "41                    1.0                  1.0                 0.0   \n",
       "14                    1.0                  1.0                 0.0   \n",
       "21                    1.0                  1.0                 0.0   \n",
       "\n",
       "    split0_test_f1  split1_test_f1  split2_test_f1  split3_test_f1  \\\n",
       "44        0.820513        0.604651        0.809524        0.702703   \n",
       "40        0.789474        0.604651        0.809524        0.702703   \n",
       "9         0.756757        0.619048        0.780488        0.628571   \n",
       "45        0.820513        0.604651        0.837209        0.702703   \n",
       "46        0.789474        0.604651        0.809524        0.702703   \n",
       "48        0.820513        0.636364        0.809524        0.702703   \n",
       "33        0.769231        0.590909        0.837209        0.702703   \n",
       "30        0.722222        0.604651        0.780488        0.628571   \n",
       "3         0.769231        0.608696        0.837209        0.702703   \n",
       "36        0.769231        0.608696        0.809524        0.702703   \n",
       "27        0.750000        0.595745        0.809524        0.666667   \n",
       "35        0.722222        0.619048        0.800000        0.628571   \n",
       "34        0.769231        0.595745        0.837209        0.702703   \n",
       "5         0.736842        0.625000        0.780488        0.702703   \n",
       "11        0.756757        0.666667        0.780488        0.628571   \n",
       "15        0.756757        0.666667        0.790698        0.588235   \n",
       "8         0.722222        0.636364        0.780488        0.628571   \n",
       "23        0.722222        0.636364        0.780488        0.628571   \n",
       "32        0.685714        0.652174        0.780488        0.666667   \n",
       "19        0.722222        0.666667        0.780488        0.666667   \n",
       "10        0.666667        0.600000        0.780488        0.588235   \n",
       "29        0.789474        0.695652        0.761905        0.666667   \n",
       "42        0.789474        0.695652        0.761905        0.666667   \n",
       "7         0.722222        0.666667        0.800000        0.545455   \n",
       "28        0.722222        0.666667        0.800000        0.545455   \n",
       "24        0.722222        0.666667        0.800000        0.545455   \n",
       "4         0.736842        0.652174        0.780488        0.702703   \n",
       "47        0.820513        0.695652        0.761905        0.684211   \n",
       "20        0.820513        0.695652        0.761905        0.684211   \n",
       "17        0.750000        0.652174        0.750000        0.666667   \n",
       "6         0.722222        0.666667        0.800000        0.588235   \n",
       "31        0.820513        0.723404        0.761905        0.666667   \n",
       "12        0.717949        0.652174        0.780488        0.702703   \n",
       "22        0.756757        0.652174        0.780488        0.702703   \n",
       "13        0.820513        0.695652        0.761905        0.702703   \n",
       "1         0.820513        0.695652        0.761905        0.684211   \n",
       "2         0.742857        0.755556        0.731707        0.666667   \n",
       "26        0.717949        0.638298        0.780488        0.702703   \n",
       "43        0.820513        0.695652        0.761905        0.666667   \n",
       "49        0.625000        0.650000        0.666667        0.562500   \n",
       "37        0.800000        0.695652        0.761905        0.666667   \n",
       "39        0.625000        0.744186        0.700000        0.588235   \n",
       "25        0.800000        0.695652        0.761905        0.666667   \n",
       "18        0.666667        0.744186        0.717949        0.606061   \n",
       "38        0.625000        0.588235        0.628571        0.516129   \n",
       "16        0.800000        0.666667        0.761905        0.666667   \n",
       "0         0.800000        0.666667        0.761905        0.666667   \n",
       "41        0.533333        0.516129        0.545455        0.466667   \n",
       "14        0.370370        0.370370        0.466667        0.357143   \n",
       "21        0.769231        0.595745        0.837209        0.736842   \n",
       "\n",
       "    split4_test_f1  mean_test_f1  std_test_f1  rank_test_f1  split0_train_f1  \\\n",
       "44        0.904762      0.768430     0.104049             3              1.0   \n",
       "40        0.878049      0.756880     0.094472            13              1.0   \n",
       "9         0.878049      0.732582     0.097722            32              1.0   \n",
       "45        0.878049      0.768625     0.100601             2              1.0   \n",
       "46        0.878049      0.756880     0.094472            13              1.0   \n",
       "48        0.850000      0.763821     0.080883             6              1.0   \n",
       "33        0.878049      0.755620     0.101746            17              1.0   \n",
       "30        0.850000      0.717187     0.091857            40              1.0   \n",
       "3         0.878049      0.759177     0.096078            12              1.0   \n",
       "36        0.878049      0.753640     0.092138            19              1.0   \n",
       "27        0.878049      0.739997     0.100178            27              1.0   \n",
       "35        0.850000      0.723968     0.091408            39              1.0   \n",
       "34        0.878049      0.756587     0.100186            15              1.0   \n",
       "5         0.878049      0.744616     0.083924            24              1.0   \n",
       "11        0.850000      0.736497     0.079690            30              1.0   \n",
       "15        0.850000      0.730471     0.092636            33              1.0   \n",
       "8         0.878049      0.729139     0.093359            35              1.0   \n",
       "23        0.878049      0.729139     0.093359            35              1.0   \n",
       "32        0.850000      0.727009     0.076070            37              1.0   \n",
       "19        0.850000      0.737209     0.070387            28              1.0   \n",
       "10        0.820513      0.691181     0.094037            46              1.0   \n",
       "29        0.904762      0.763692     0.083201             7              1.0   \n",
       "42        0.904762      0.763692     0.083201             7              1.0   \n",
       "7         0.820513      0.710971     0.099409            42              1.0   \n",
       "28        0.820513      0.710971     0.099409            42              1.0   \n",
       "24        0.820513      0.710971     0.099409            42              1.0   \n",
       "4         0.850000      0.744441     0.067461            25              1.0   \n",
       "47        0.857143      0.763885     0.067693             4              1.0   \n",
       "20        0.857143      0.763885     0.067693             4              1.0   \n",
       "17        0.829268      0.729622     0.064376            34              1.0   \n",
       "6         0.850000      0.725425     0.093128            38              1.0   \n",
       "31        0.829268      0.760351     0.060849            11              1.0   \n",
       "12        0.829268      0.736516     0.061868            29              1.0   \n",
       "22        0.829268      0.744278     0.061485            26              1.0   \n",
       "13        0.883721      0.772899     0.071504             1              1.0   \n",
       "1         0.850000      0.762456     0.065757            10              1.0   \n",
       "2         0.850000      0.749357     0.058922            21              1.0   \n",
       "26        0.829268      0.733741     0.065777            31              1.0   \n",
       "43        0.829268      0.754801     0.065085            18              1.0   \n",
       "49        0.842105      0.669254     0.093391            47              1.0   \n",
       "37        0.857143      0.756273     0.069026            16              1.0   \n",
       "39        0.850000      0.701484     0.092258            45              1.0   \n",
       "25        0.837209      0.752287     0.063434            20              1.0   \n",
       "18        0.820513      0.711075     0.072278            41              1.0   \n",
       "38        0.842105      0.640008     0.108835            48              1.0   \n",
       "16        0.829268      0.744901     0.067356            22              1.0   \n",
       "0         0.829268      0.744901     0.067356            22              1.0   \n",
       "41        0.600000      0.532317     0.043189            49              1.0   \n",
       "14        0.551724      0.423255     0.075304            50              1.0   \n",
       "21        0.878049      0.763415     0.097406             9              1.0   \n",
       "\n",
       "    split1_train_f1  split2_train_f1  split3_train_f1  split4_train_f1  \\\n",
       "44              1.0              1.0              1.0              1.0   \n",
       "40              1.0              1.0              1.0              1.0   \n",
       "9               1.0              1.0              1.0              1.0   \n",
       "45              1.0              1.0              1.0              1.0   \n",
       "46              1.0              1.0              1.0              1.0   \n",
       "48              1.0              1.0              1.0              1.0   \n",
       "33              1.0              1.0              1.0              1.0   \n",
       "30              1.0              1.0              1.0              1.0   \n",
       "3               1.0              1.0              1.0              1.0   \n",
       "36              1.0              1.0              1.0              1.0   \n",
       "27              1.0              1.0              1.0              1.0   \n",
       "35              1.0              1.0              1.0              1.0   \n",
       "34              1.0              1.0              1.0              1.0   \n",
       "5               1.0              1.0              1.0              1.0   \n",
       "11              1.0              1.0              1.0              1.0   \n",
       "15              1.0              1.0              1.0              1.0   \n",
       "8               1.0              1.0              1.0              1.0   \n",
       "23              1.0              1.0              1.0              1.0   \n",
       "32              1.0              1.0              1.0              1.0   \n",
       "19              1.0              1.0              1.0              1.0   \n",
       "10              1.0              1.0              1.0              1.0   \n",
       "29              1.0              1.0              1.0              1.0   \n",
       "42              1.0              1.0              1.0              1.0   \n",
       "7               1.0              1.0              1.0              1.0   \n",
       "28              1.0              1.0              1.0              1.0   \n",
       "24              1.0              1.0              1.0              1.0   \n",
       "4               1.0              1.0              1.0              1.0   \n",
       "47              1.0              1.0              1.0              1.0   \n",
       "20              1.0              1.0              1.0              1.0   \n",
       "17              1.0              1.0              1.0              1.0   \n",
       "6               1.0              1.0              1.0              1.0   \n",
       "31              1.0              1.0              1.0              1.0   \n",
       "12              1.0              1.0              1.0              1.0   \n",
       "22              1.0              1.0              1.0              1.0   \n",
       "13              1.0              1.0              1.0              1.0   \n",
       "1               1.0              1.0              1.0              1.0   \n",
       "2               1.0              1.0              1.0              1.0   \n",
       "26              1.0              1.0              1.0              1.0   \n",
       "43              1.0              1.0              1.0              1.0   \n",
       "49              1.0              1.0              1.0              1.0   \n",
       "37              1.0              1.0              1.0              1.0   \n",
       "39              1.0              1.0              1.0              1.0   \n",
       "25              1.0              1.0              1.0              1.0   \n",
       "18              1.0              1.0              1.0              1.0   \n",
       "38              1.0              1.0              1.0              1.0   \n",
       "16              1.0              1.0              1.0              1.0   \n",
       "0               1.0              1.0              1.0              1.0   \n",
       "41              1.0              1.0              1.0              1.0   \n",
       "14              1.0              1.0              1.0              1.0   \n",
       "21              1.0              1.0              1.0              1.0   \n",
       "\n",
       "    mean_train_f1  std_train_f1  split0_test_neg_log_loss  \\\n",
       "44            1.0           0.0                 -0.188384   \n",
       "40            1.0           0.0                 -0.189127   \n",
       "9             1.0           0.0                 -0.193291   \n",
       "45            1.0           0.0                 -0.184958   \n",
       "46            1.0           0.0                 -0.188583   \n",
       "48            1.0           0.0                 -0.191479   \n",
       "33            1.0           0.0                 -0.189803   \n",
       "30            1.0           0.0                 -0.196897   \n",
       "3             1.0           0.0                 -0.186048   \n",
       "36            1.0           0.0                 -0.188121   \n",
       "27            1.0           0.0                 -0.189183   \n",
       "35            1.0           0.0                 -0.200671   \n",
       "34            1.0           0.0                 -0.186212   \n",
       "5             1.0           0.0                 -0.189725   \n",
       "11            1.0           0.0                 -0.202658   \n",
       "15            1.0           0.0                 -0.202730   \n",
       "8             1.0           0.0                 -0.202977   \n",
       "23            1.0           0.0                 -0.202977   \n",
       "32            1.0           0.0                 -0.202642   \n",
       "19            1.0           0.0                 -0.203958   \n",
       "10            1.0           0.0                 -0.210134   \n",
       "29            1.0           0.0                 -0.205300   \n",
       "42            1.0           0.0                 -0.205300   \n",
       "7             1.0           0.0                 -0.211243   \n",
       "28            1.0           0.0                 -0.211243   \n",
       "24            1.0           0.0                 -0.211243   \n",
       "4             1.0           0.0                 -0.201897   \n",
       "47            1.0           0.0                 -0.201670   \n",
       "20            1.0           0.0                 -0.201670   \n",
       "17            1.0           0.0                 -0.202676   \n",
       "6             1.0           0.0                 -0.210221   \n",
       "31            1.0           0.0                 -0.204520   \n",
       "12            1.0           0.0                 -0.202103   \n",
       "22            1.0           0.0                 -0.202547   \n",
       "13            1.0           0.0                 -0.203506   \n",
       "1             1.0           0.0                 -0.202434   \n",
       "2             1.0           0.0                 -0.214412   \n",
       "26            1.0           0.0                 -0.204319   \n",
       "43            1.0           0.0                 -0.203806   \n",
       "49            1.0           0.0                 -0.221266   \n",
       "37            1.0           0.0                 -0.203267   \n",
       "39            1.0           0.0                 -0.219745   \n",
       "25            1.0           0.0                 -0.204399   \n",
       "18            1.0           0.0                 -0.221578   \n",
       "38            1.0           0.0                 -0.226146   \n",
       "16            1.0           0.0                 -0.206337   \n",
       "0             1.0           0.0                 -0.206337   \n",
       "41            1.0           0.0                 -0.233811   \n",
       "14            1.0           0.0                 -0.248008   \n",
       "21            1.0           0.0                 -0.185723   \n",
       "\n",
       "    split1_test_neg_log_loss  split2_test_neg_log_loss  \\\n",
       "44                 -0.246548                 -0.227353   \n",
       "40                 -0.259877                 -0.222542   \n",
       "9                  -0.244133                 -0.227640   \n",
       "45                 -0.262567                 -0.222470   \n",
       "46                 -0.256138                 -0.225058   \n",
       "48                 -0.250005                 -0.231059   \n",
       "33                 -0.269909                 -0.225682   \n",
       "30                 -0.247174                 -0.228466   \n",
       "3                  -0.275502                 -0.223417   \n",
       "36                 -0.280086                 -0.226318   \n",
       "27                 -0.292531                 -0.221965   \n",
       "35                 -0.245605                 -0.233451   \n",
       "34                 -0.287698                 -0.226869   \n",
       "5                  -0.296592                 -0.225357   \n",
       "11                 -0.256360                 -0.236049   \n",
       "15                 -0.246241                 -0.239447   \n",
       "8                  -0.268550                 -0.237213   \n",
       "23                 -0.268550                 -0.237213   \n",
       "32                 -0.271624                 -0.238415   \n",
       "19                 -0.267809                 -0.236955   \n",
       "10                 -0.249459                 -0.240784   \n",
       "29                 -0.254905                 -0.248784   \n",
       "42                 -0.254905                 -0.248784   \n",
       "7                  -0.251684                 -0.241572   \n",
       "28                 -0.251684                 -0.241572   \n",
       "24                 -0.251684                 -0.241572   \n",
       "4                  -0.281896                 -0.238170   \n",
       "47                 -0.266115                 -0.252411   \n",
       "20                 -0.266115                 -0.252411   \n",
       "17                 -0.291838                 -0.239878   \n",
       "6                  -0.251306                 -0.244210   \n",
       "31                 -0.254541                 -0.249963   \n",
       "12                 -0.288987                 -0.240117   \n",
       "22                 -0.280458                 -0.244536   \n",
       "13                 -0.261182                 -0.259176   \n",
       "1                  -0.268272                 -0.260161   \n",
       "2                  -0.247631                 -0.251159   \n",
       "26                 -0.294625                 -0.242603   \n",
       "43                 -0.278336                 -0.260761   \n",
       "49                 -0.252215                 -0.252510   \n",
       "37                 -0.280565                 -0.262882   \n",
       "39                 -0.249577                 -0.254977   \n",
       "25                 -0.280863                 -0.265144   \n",
       "18                 -0.250834                 -0.259787   \n",
       "38                 -0.256195                 -0.253226   \n",
       "16                 -0.300663                 -0.275821   \n",
       "0                  -0.300663                 -0.275821   \n",
       "41                 -0.262821                 -0.261573   \n",
       "14                 -0.271295                 -0.271016   \n",
       "21                 -0.275228                 -0.226293   \n",
       "\n",
       "    split3_test_neg_log_loss  split4_test_neg_log_loss  \\\n",
       "44                 -0.230374                 -0.194891   \n",
       "40                 -0.225217                 -0.197657   \n",
       "9                  -0.239728                 -0.196789   \n",
       "45                 -0.221762                 -0.213737   \n",
       "46                 -0.226624                 -0.209373   \n",
       "48                 -0.235000                 -0.199185   \n",
       "33                 -0.220571                 -0.209907   \n",
       "30                 -0.243350                 -0.201142   \n",
       "3                  -0.219385                 -0.214369   \n",
       "36                 -0.221191                 -0.203775   \n",
       "27                 -0.220392                 -0.204445   \n",
       "35                 -0.244237                 -0.204951   \n",
       "34                 -0.216048                 -0.214592   \n",
       "5                  -0.218292                 -0.207291   \n",
       "11                 -0.242741                 -0.209118   \n",
       "15                 -0.252377                 -0.207504   \n",
       "8                  -0.239945                 -0.204363   \n",
       "23                 -0.239945                 -0.204363   \n",
       "32                 -0.237890                 -0.204268   \n",
       "19                 -0.243657                 -0.205636   \n",
       "10                 -0.254386                 -0.210878   \n",
       "29                 -0.249789                 -0.209133   \n",
       "42                 -0.249789                 -0.209133   \n",
       "7                  -0.254599                 -0.212186   \n",
       "28                 -0.254599                 -0.212186   \n",
       "24                 -0.254599                 -0.212186   \n",
       "4                  -0.235774                 -0.215754   \n",
       "47                 -0.246154                 -0.208780   \n",
       "20                 -0.246154                 -0.208780   \n",
       "17                 -0.234025                 -0.207240   \n",
       "6                  -0.253968                 -0.217123   \n",
       "31                 -0.250668                 -0.218199   \n",
       "12                 -0.237177                 -0.209642   \n",
       "22                 -0.239006                 -0.214901   \n",
       "13                 -0.245316                 -0.212683   \n",
       "1                  -0.245065                 -0.206455   \n",
       "2                  -0.260514                 -0.214479   \n",
       "26                 -0.235843                 -0.215385   \n",
       "43                 -0.245538                 -0.215860   \n",
       "49                 -0.261969                 -0.216773   \n",
       "37                 -0.244516                 -0.214796   \n",
       "39                 -0.264054                 -0.221026   \n",
       "25                 -0.246280                 -0.214252   \n",
       "18                 -0.269190                 -0.223585   \n",
       "38                 -0.267870                 -0.226986   \n",
       "16                 -0.247293                 -0.220008   \n",
       "0                  -0.247293                 -0.220008   \n",
       "41                 -0.278747                 -0.235324   \n",
       "14                 -0.290419                 -0.250062   \n",
       "21                 -0.217613                 -0.450749   \n",
       "\n",
       "    mean_test_neg_log_loss  std_test_neg_log_loss  rank_test_neg_log_loss  \\\n",
       "44               -0.217510               0.022206                       1   \n",
       "40               -0.218884               0.024779                       2   \n",
       "9                -0.220316               0.021362                       3   \n",
       "45               -0.221099               0.024829                       4   \n",
       "46               -0.221155               0.022220                       5   \n",
       "48               -0.221346               0.022295                       6   \n",
       "33               -0.223174               0.026408                       7   \n",
       "30               -0.223406               0.020912                       8   \n",
       "3                -0.223744               0.029007                       9   \n",
       "36               -0.223898               0.031161                      10   \n",
       "27               -0.225703               0.035479                      11   \n",
       "35               -0.225783               0.019271                      12   \n",
       "34               -0.226284               0.033524                      13   \n",
       "5                -0.227452               0.036602                      14   \n",
       "11               -0.229385               0.020374                      15   \n",
       "15               -0.229660               0.020508                      16   \n",
       "8                -0.230610               0.024587                      17   \n",
       "23               -0.230610               0.024587                      17   \n",
       "32               -0.230968               0.025579                      19   \n",
       "19               -0.231603               0.024180                      20   \n",
       "10               -0.233128               0.018979                      21   \n",
       "29               -0.233582               0.021661                      22   \n",
       "42               -0.233582               0.021661                      22   \n",
       "7                -0.234257               0.018909                      24   \n",
       "28               -0.234257               0.018909                      24   \n",
       "24               -0.234257               0.018909                      24   \n",
       "4                -0.234698               0.027113                      27   \n",
       "47               -0.235026               0.025275                      28   \n",
       "20               -0.235026               0.025275                      28   \n",
       "17               -0.235131               0.031845                      30   \n",
       "6                -0.235366               0.018130                      31   \n",
       "31               -0.235578               0.020302                      32   \n",
       "12               -0.235605               0.030558                      33   \n",
       "22               -0.236290               0.026916                      34   \n",
       "13               -0.236373               0.023904                      35   \n",
       "1                -0.236477               0.027225                      36   \n",
       "2                -0.237639               0.019400                      37   \n",
       "26               -0.238555               0.031230                      38   \n",
       "43               -0.240860               0.027642                      39   \n",
       "49               -0.240947               0.018299                      40   \n",
       "37               -0.241205               0.028868                      41   \n",
       "39               -0.241876               0.018151                      42   \n",
       "25               -0.242188               0.029148                      43   \n",
       "18               -0.244995               0.019209                      44   \n",
       "38               -0.246085               0.016674                      45   \n",
       "16               -0.250024               0.034776                      46   \n",
       "0                -0.250024               0.034776                      46   \n",
       "41               -0.254455               0.017337                      48   \n",
       "14               -0.266160               0.015666                      49   \n",
       "21               -0.271121               0.094285                      50   \n",
       "\n",
       "    split0_train_neg_log_loss  split1_train_neg_log_loss  \\\n",
       "44                  -0.063184                  -0.057840   \n",
       "40                  -0.061855                  -0.056088   \n",
       "9                   -0.064018                  -0.060149   \n",
       "45                  -0.061075                  -0.054832   \n",
       "46                  -0.062107                  -0.055962   \n",
       "48                  -0.062562                  -0.058090   \n",
       "33                  -0.060615                  -0.054166   \n",
       "30                  -0.064201                  -0.060140   \n",
       "3                   -0.060751                  -0.053647   \n",
       "36                  -0.060538                  -0.054007   \n",
       "27                  -0.059372                  -0.053354   \n",
       "35                  -0.066376                  -0.062905   \n",
       "34                  -0.060005                  -0.052982   \n",
       "5                   -0.059401                  -0.053169   \n",
       "11                  -0.065303                  -0.061007   \n",
       "15                  -0.067223                  -0.063484   \n",
       "8                   -0.064810                  -0.058469   \n",
       "23                  -0.064810                  -0.058469   \n",
       "32                  -0.064252                  -0.058087   \n",
       "19                  -0.065144                  -0.059214   \n",
       "10                  -0.068921                  -0.066549   \n",
       "29                  -0.065871                  -0.062073   \n",
       "42                  -0.065871                  -0.062073   \n",
       "7                   -0.068032                  -0.064064   \n",
       "28                  -0.068032                  -0.064064   \n",
       "24                  -0.068032                  -0.064064   \n",
       "4                   -0.063075                  -0.056922   \n",
       "47                  -0.065417                  -0.059958   \n",
       "20                  -0.065417                  -0.059958   \n",
       "17                  -0.063580                  -0.057088   \n",
       "6                   -0.066945                  -0.064214   \n",
       "31                  -0.065771                  -0.061289   \n",
       "12                  -0.063030                  -0.057085   \n",
       "22                  -0.064061                  -0.057431   \n",
       "13                  -0.065109                  -0.060309   \n",
       "1                   -0.064756                  -0.058710   \n",
       "2                   -0.068995                  -0.065272   \n",
       "26                  -0.062908                  -0.056822   \n",
       "43                  -0.064744                  -0.058412   \n",
       "49                  -0.071303                  -0.067726   \n",
       "37                  -0.064630                  -0.057446   \n",
       "39                  -0.070242                  -0.067618   \n",
       "25                  -0.064554                  -0.057638   \n",
       "18                  -0.071666                  -0.069258   \n",
       "38                  -0.072823                  -0.070059   \n",
       "16                  -0.064265                  -0.056333   \n",
       "0                   -0.064265                  -0.056333   \n",
       "41                  -0.075108                  -0.072985   \n",
       "14                  -0.078929                  -0.076782   \n",
       "21                  -0.060398                  -0.053580   \n",
       "\n",
       "    split2_train_neg_log_loss  split3_train_neg_log_loss  \\\n",
       "44                  -0.060752                  -0.061706   \n",
       "40                  -0.060006                  -0.061709   \n",
       "9                   -0.063239                  -0.062927   \n",
       "45                  -0.059435                  -0.062055   \n",
       "46                  -0.059975                  -0.061801   \n",
       "48                  -0.061433                  -0.061881   \n",
       "33                  -0.059160                  -0.061120   \n",
       "30                  -0.063600                  -0.062928   \n",
       "3                   -0.058928                  -0.061150   \n",
       "36                  -0.058743                  -0.061201   \n",
       "27                  -0.058384                  -0.061280   \n",
       "35                  -0.065090                  -0.064503   \n",
       "34                  -0.058267                  -0.061633   \n",
       "5                   -0.058142                  -0.061601   \n",
       "11                  -0.064309                  -0.063497   \n",
       "15                  -0.066446                  -0.064816   \n",
       "8                   -0.063103                  -0.063257   \n",
       "23                  -0.063103                  -0.063257   \n",
       "32                  -0.063196                  -0.062840   \n",
       "19                  -0.064127                  -0.062981   \n",
       "10                  -0.067740                  -0.066006   \n",
       "29                  -0.064291                  -0.063781   \n",
       "42                  -0.064291                  -0.063781   \n",
       "7                   -0.067504                  -0.065871   \n",
       "28                  -0.067504                  -0.065871   \n",
       "24                  -0.067504                  -0.065871   \n",
       "4                   -0.062008                  -0.062966   \n",
       "47                  -0.063536                  -0.063017   \n",
       "20                  -0.063536                  -0.063017   \n",
       "17                  -0.061790                  -0.062884   \n",
       "6                   -0.067089                  -0.064779   \n",
       "31                  -0.064755                  -0.063722   \n",
       "12                  -0.061839                  -0.063005   \n",
       "22                  -0.062101                  -0.063203   \n",
       "13                  -0.063672                  -0.062594   \n",
       "1                   -0.063179                  -0.062716   \n",
       "2                   -0.067779                  -0.066980   \n",
       "26                  -0.061390                  -0.062548   \n",
       "43                  -0.063292                  -0.062198   \n",
       "49                  -0.069953                  -0.067978   \n",
       "37                  -0.063084                  -0.062069   \n",
       "39                  -0.068804                  -0.067863   \n",
       "25                  -0.061908                  -0.061841   \n",
       "18                  -0.069895                  -0.068604   \n",
       "38                  -0.070878                  -0.069382   \n",
       "16                  -0.061530                  -0.062128   \n",
       "0                   -0.061530                  -0.062128   \n",
       "41                  -0.074612                  -0.072683   \n",
       "14                  -0.076937                  -0.076150   \n",
       "21                  -0.058312                  -0.061488   \n",
       "\n",
       "    split4_train_neg_log_loss  mean_train_neg_log_loss  \\\n",
       "44                  -0.062456                -0.061188   \n",
       "40                  -0.061415                -0.060215   \n",
       "9                   -0.064846                -0.063036   \n",
       "45                  -0.060835                -0.059646   \n",
       "46                  -0.061772                -0.060323   \n",
       "48                  -0.063491                -0.061492   \n",
       "33                  -0.060546                -0.059122   \n",
       "30                  -0.065730                -0.063320   \n",
       "3                   -0.060107                -0.058916   \n",
       "36                  -0.060214                -0.058941   \n",
       "27                  -0.059293                -0.058337   \n",
       "35                  -0.067612                -0.065297   \n",
       "34                  -0.059447                -0.058467   \n",
       "5                   -0.059602                -0.058383   \n",
       "11                  -0.066154                -0.064054   \n",
       "15                  -0.068418                -0.066077   \n",
       "8                   -0.064818                -0.062891   \n",
       "23                  -0.064818                -0.062891   \n",
       "32                  -0.065199                -0.062715   \n",
       "19                  -0.066075                -0.063508   \n",
       "10                  -0.069591                -0.067761   \n",
       "29                  -0.066545                -0.064512   \n",
       "42                  -0.066545                -0.064512   \n",
       "7                   -0.068626                -0.066819   \n",
       "28                  -0.068626                -0.066819   \n",
       "24                  -0.068626                -0.066819   \n",
       "4                   -0.064503                -0.061895   \n",
       "47                  -0.065593                -0.063504   \n",
       "20                  -0.065593                -0.063504   \n",
       "17                  -0.064526                -0.061974   \n",
       "6                   -0.068601                -0.066326   \n",
       "31                  -0.066422                -0.064392   \n",
       "12                  -0.064560                -0.061904   \n",
       "22                  -0.064597                -0.062279   \n",
       "13                  -0.065144                -0.063366   \n",
       "1                   -0.065390                -0.062950   \n",
       "2                   -0.069831                -0.067771   \n",
       "26                  -0.064268                -0.061587   \n",
       "43                  -0.065232                -0.062775   \n",
       "49                  -0.071737                -0.069739   \n",
       "37                  -0.064820                -0.062410   \n",
       "39                  -0.071076                -0.069121   \n",
       "25                  -0.065085                -0.062205   \n",
       "18                  -0.073306                -0.070546   \n",
       "38                  -0.072836                -0.071196   \n",
       "16                  -0.064316                -0.061714   \n",
       "0                   -0.064316                -0.061714   \n",
       "41                  -0.075574                -0.074192   \n",
       "14                  -0.078959                -0.077551   \n",
       "21                  -0.059363                -0.058628   \n",
       "\n",
       "    std_train_neg_log_loss  split0_test_precision  split1_test_precision  \\\n",
       "44                0.001858               0.941176               0.619048   \n",
       "40                0.002165               0.937500               0.619048   \n",
       "9                 0.001589               0.933333               0.650000   \n",
       "45                0.002548               0.941176               0.619048   \n",
       "46                0.002307               0.937500               0.619048   \n",
       "48                0.001837               0.941176               0.636364   \n",
       "33                0.002562               0.882353               0.590909   \n",
       "30                0.001840               0.928571               0.619048   \n",
       "3                 0.002740               0.882353               0.583333   \n",
       "36                0.002595               0.882353               0.583333   \n",
       "27                0.002664               0.833333               0.560000   \n",
       "35                0.001607               0.928571               0.650000   \n",
       "34                0.002949               0.882353               0.560000   \n",
       "5                 0.002833               0.875000               0.576923   \n",
       "11                0.001768               0.933333               0.652174   \n",
       "15                0.001746               0.933333               0.700000   \n",
       "8                 0.002329               0.928571               0.636364   \n",
       "23                0.002329               0.928571               0.636364   \n",
       "32                0.002457               0.923077               0.625000   \n",
       "19                0.002382               0.928571               0.652174   \n",
       "10                0.001360               1.000000               0.666667   \n",
       "29                0.001582               0.937500               0.666667   \n",
       "42                0.001582               0.937500               0.666667   \n",
       "7                 0.001655               0.928571               0.700000   \n",
       "28                0.001655               0.928571               0.700000   \n",
       "24                0.001655               0.928571               0.700000   \n",
       "4                 0.002611               0.875000               0.625000   \n",
       "47                0.002041               0.941176               0.666667   \n",
       "20                0.002041               0.941176               0.666667   \n",
       "17                0.002601               0.833333               0.625000   \n",
       "6                 0.001612               0.928571               0.700000   \n",
       "31                0.001801               0.941176               0.680000   \n",
       "12                0.002560               0.823529               0.625000   \n",
       "22                0.002567               0.933333               0.625000   \n",
       "13                0.001802               0.941176               0.666667   \n",
       "1                 0.002337               0.941176               0.666667   \n",
       "2                 0.001588               1.000000               0.739130   \n",
       "26                0.002553               0.823529               0.600000   \n",
       "43                0.002431               0.941176               0.666667   \n",
       "49                0.001652               1.000000               0.722222   \n",
       "37                0.002681               0.888889               0.666667   \n",
       "39                0.001343               1.000000               0.761905   \n",
       "25                0.002642               0.888889               0.666667   \n",
       "18                0.001716               1.000000               0.761905   \n",
       "38                0.001416               1.000000               0.833333   \n",
       "16                0.002913               0.888889               0.615385   \n",
       "0                 0.002913               0.888889               0.615385   \n",
       "41                0.001154               1.000000               0.888889   \n",
       "14                0.001167               1.000000               1.000000   \n",
       "21                0.002736               0.882353               0.560000   \n",
       "\n",
       "    split2_test_precision  split3_test_precision  split4_test_precision  \\\n",
       "44               0.850000               0.812500               0.904762   \n",
       "40               0.850000               0.812500               0.900000   \n",
       "9                0.842105               0.785714               0.900000   \n",
       "45               0.857143               0.812500               0.900000   \n",
       "46               0.850000               0.812500               0.900000   \n",
       "48               0.850000               0.812500               0.894737   \n",
       "33               0.857143               0.812500               0.900000   \n",
       "30               0.842105               0.785714               0.894737   \n",
       "3                0.857143               0.812500               0.900000   \n",
       "36               0.850000               0.812500               0.900000   \n",
       "27               0.850000               0.800000               0.900000   \n",
       "35               0.888889               0.785714               0.894737   \n",
       "34               0.857143               0.812500               0.900000   \n",
       "5                0.842105               0.812500               0.900000   \n",
       "11               0.842105               0.785714               0.894737   \n",
       "15               0.809524               0.769231               0.894737   \n",
       "8                0.842105               0.785714               0.900000   \n",
       "23               0.842105               0.785714               0.900000   \n",
       "32               0.842105               0.800000               0.894737   \n",
       "19               0.842105               0.800000               0.894737   \n",
       "10               0.842105               0.769231               0.888889   \n",
       "29               0.800000               0.800000               0.904762   \n",
       "42               0.800000               0.800000               0.904762   \n",
       "7                0.888889               0.750000               0.888889   \n",
       "28               0.888889               0.750000               0.888889   \n",
       "24               0.888889               0.750000               0.888889   \n",
       "4                0.842105               0.812500               0.894737   \n",
       "47               0.800000               0.764706               0.857143   \n",
       "20               0.800000               0.764706               0.857143   \n",
       "17               0.833333               0.800000               0.850000   \n",
       "6                0.888889               0.769231               0.894737   \n",
       "31               0.800000               0.800000               0.850000   \n",
       "12               0.842105               0.812500               0.850000   \n",
       "22               0.842105               0.812500               0.850000   \n",
       "13               0.800000               0.812500               0.863636   \n",
       "1                0.800000               0.764706               0.894737   \n",
       "2                0.789474               0.800000               0.894737   \n",
       "26               0.842105               0.812500               0.850000   \n",
       "43               0.800000               0.722222               0.850000   \n",
       "49               0.857143               0.818182               0.941176   \n",
       "37               0.800000               0.722222               0.857143   \n",
       "39               0.777778               0.769231               0.894737   \n",
       "25               0.800000               0.722222               0.818182   \n",
       "18               0.823529               0.833333               0.888889   \n",
       "38               0.846154               0.800000               0.941176   \n",
       "16               0.800000               0.722222               0.850000   \n",
       "0                0.800000               0.722222               0.850000   \n",
       "41               0.818182               0.777778               1.000000   \n",
       "14               0.875000               0.714286               1.000000   \n",
       "21               0.857143               0.823529               0.900000   \n",
       "\n",
       "    mean_test_precision  std_test_precision  rank_test_precision  \\\n",
       "44             0.825497            0.112299                   16   \n",
       "40             0.823810            0.110880                   17   \n",
       "9              0.822231            0.099783                   20   \n",
       "45             0.825973            0.112003                   15   \n",
       "46             0.823810            0.110880                   17   \n",
       "48             0.826955            0.104600                   14   \n",
       "33             0.808581            0.112743                   34   \n",
       "30             0.814035            0.108850                   30   \n",
       "3              0.807066            0.115671                   35   \n",
       "36             0.805637            0.115086                   38   \n",
       "27             0.788667            0.118801                   44   \n",
       "35             0.829582            0.101721                   13   \n",
       "34             0.802399            0.124720                   40   \n",
       "5              0.801306            0.116025                   41   \n",
       "11             0.821613            0.098231                   23   \n",
       "15             0.821365            0.084271                   24   \n",
       "8              0.818551            0.103502                   25   \n",
       "23             0.818551            0.103502                   25   \n",
       "32             0.816984            0.104956                   27   \n",
       "19             0.823517            0.096297                   19   \n",
       "10             0.833378            0.112096                    9   \n",
       "29             0.821786            0.095169                   21   \n",
       "42             0.821786            0.095169                   21   \n",
       "7              0.831270            0.089380                   10   \n",
       "28             0.831270            0.089380                   10   \n",
       "24             0.831270            0.089380                   10   \n",
       "4              0.809868            0.096610                   33   \n",
       "47             0.805938            0.091699                   36   \n",
       "20             0.805938            0.091699                   36   \n",
       "17             0.788333            0.083267                   45   \n",
       "6              0.836286            0.086914                    8   \n",
       "31             0.814235            0.084638                   29   \n",
       "12             0.790627            0.083867                   43   \n",
       "22             0.812588            0.102019                   32   \n",
       "13             0.816796            0.090004                   28   \n",
       "1              0.813457            0.096978                   31   \n",
       "2              0.844668            0.092536                    6   \n",
       "26             0.785627            0.093755                   47   \n",
       "43             0.796013            0.096103                   42   \n",
       "49             0.867745            0.096571                    4   \n",
       "37             0.786984            0.082640                   46   \n",
       "39             0.840730            0.093351                    7   \n",
       "25             0.779192            0.077323                   48   \n",
       "18             0.861531            0.080108                    5   \n",
       "38             0.884133            0.074539                    3   \n",
       "16             0.775299            0.097450                   49   \n",
       "0              0.775299            0.097450                   49   \n",
       "41             0.896970            0.091335                    2   \n",
       "14             0.917857            0.112712                    1   \n",
       "21             0.804605            0.124976                   39   \n",
       "\n",
       "    split0_train_precision  split1_train_precision  split2_train_precision  \\\n",
       "44                     1.0                     1.0                     1.0   \n",
       "40                     1.0                     1.0                     1.0   \n",
       "9                      1.0                     1.0                     1.0   \n",
       "45                     1.0                     1.0                     1.0   \n",
       "46                     1.0                     1.0                     1.0   \n",
       "48                     1.0                     1.0                     1.0   \n",
       "33                     1.0                     1.0                     1.0   \n",
       "30                     1.0                     1.0                     1.0   \n",
       "3                      1.0                     1.0                     1.0   \n",
       "36                     1.0                     1.0                     1.0   \n",
       "27                     1.0                     1.0                     1.0   \n",
       "35                     1.0                     1.0                     1.0   \n",
       "34                     1.0                     1.0                     1.0   \n",
       "5                      1.0                     1.0                     1.0   \n",
       "11                     1.0                     1.0                     1.0   \n",
       "15                     1.0                     1.0                     1.0   \n",
       "8                      1.0                     1.0                     1.0   \n",
       "23                     1.0                     1.0                     1.0   \n",
       "32                     1.0                     1.0                     1.0   \n",
       "19                     1.0                     1.0                     1.0   \n",
       "10                     1.0                     1.0                     1.0   \n",
       "29                     1.0                     1.0                     1.0   \n",
       "42                     1.0                     1.0                     1.0   \n",
       "7                      1.0                     1.0                     1.0   \n",
       "28                     1.0                     1.0                     1.0   \n",
       "24                     1.0                     1.0                     1.0   \n",
       "4                      1.0                     1.0                     1.0   \n",
       "47                     1.0                     1.0                     1.0   \n",
       "20                     1.0                     1.0                     1.0   \n",
       "17                     1.0                     1.0                     1.0   \n",
       "6                      1.0                     1.0                     1.0   \n",
       "31                     1.0                     1.0                     1.0   \n",
       "12                     1.0                     1.0                     1.0   \n",
       "22                     1.0                     1.0                     1.0   \n",
       "13                     1.0                     1.0                     1.0   \n",
       "1                      1.0                     1.0                     1.0   \n",
       "2                      1.0                     1.0                     1.0   \n",
       "26                     1.0                     1.0                     1.0   \n",
       "43                     1.0                     1.0                     1.0   \n",
       "49                     1.0                     1.0                     1.0   \n",
       "37                     1.0                     1.0                     1.0   \n",
       "39                     1.0                     1.0                     1.0   \n",
       "25                     1.0                     1.0                     1.0   \n",
       "18                     1.0                     1.0                     1.0   \n",
       "38                     1.0                     1.0                     1.0   \n",
       "16                     1.0                     1.0                     1.0   \n",
       "0                      1.0                     1.0                     1.0   \n",
       "41                     1.0                     1.0                     1.0   \n",
       "14                     1.0                     1.0                     1.0   \n",
       "21                     1.0                     1.0                     1.0   \n",
       "\n",
       "    split3_train_precision  split4_train_precision  mean_train_precision  \\\n",
       "44                     1.0                     1.0                   1.0   \n",
       "40                     1.0                     1.0                   1.0   \n",
       "9                      1.0                     1.0                   1.0   \n",
       "45                     1.0                     1.0                   1.0   \n",
       "46                     1.0                     1.0                   1.0   \n",
       "48                     1.0                     1.0                   1.0   \n",
       "33                     1.0                     1.0                   1.0   \n",
       "30                     1.0                     1.0                   1.0   \n",
       "3                      1.0                     1.0                   1.0   \n",
       "36                     1.0                     1.0                   1.0   \n",
       "27                     1.0                     1.0                   1.0   \n",
       "35                     1.0                     1.0                   1.0   \n",
       "34                     1.0                     1.0                   1.0   \n",
       "5                      1.0                     1.0                   1.0   \n",
       "11                     1.0                     1.0                   1.0   \n",
       "15                     1.0                     1.0                   1.0   \n",
       "8                      1.0                     1.0                   1.0   \n",
       "23                     1.0                     1.0                   1.0   \n",
       "32                     1.0                     1.0                   1.0   \n",
       "19                     1.0                     1.0                   1.0   \n",
       "10                     1.0                     1.0                   1.0   \n",
       "29                     1.0                     1.0                   1.0   \n",
       "42                     1.0                     1.0                   1.0   \n",
       "7                      1.0                     1.0                   1.0   \n",
       "28                     1.0                     1.0                   1.0   \n",
       "24                     1.0                     1.0                   1.0   \n",
       "4                      1.0                     1.0                   1.0   \n",
       "47                     1.0                     1.0                   1.0   \n",
       "20                     1.0                     1.0                   1.0   \n",
       "17                     1.0                     1.0                   1.0   \n",
       "6                      1.0                     1.0                   1.0   \n",
       "31                     1.0                     1.0                   1.0   \n",
       "12                     1.0                     1.0                   1.0   \n",
       "22                     1.0                     1.0                   1.0   \n",
       "13                     1.0                     1.0                   1.0   \n",
       "1                      1.0                     1.0                   1.0   \n",
       "2                      1.0                     1.0                   1.0   \n",
       "26                     1.0                     1.0                   1.0   \n",
       "43                     1.0                     1.0                   1.0   \n",
       "49                     1.0                     1.0                   1.0   \n",
       "37                     1.0                     1.0                   1.0   \n",
       "39                     1.0                     1.0                   1.0   \n",
       "25                     1.0                     1.0                   1.0   \n",
       "18                     1.0                     1.0                   1.0   \n",
       "38                     1.0                     1.0                   1.0   \n",
       "16                     1.0                     1.0                   1.0   \n",
       "0                      1.0                     1.0                   1.0   \n",
       "41                     1.0                     1.0                   1.0   \n",
       "14                     1.0                     1.0                   1.0   \n",
       "21                     1.0                     1.0                   1.0   \n",
       "\n",
       "    std_train_precision  split0_test_recall  split1_test_recall  \\\n",
       "44                  0.0            0.727273            0.590909   \n",
       "40                  0.0            0.681818            0.590909   \n",
       "9                   0.0            0.636364            0.590909   \n",
       "45                  0.0            0.727273            0.590909   \n",
       "46                  0.0            0.681818            0.590909   \n",
       "48                  0.0            0.727273            0.636364   \n",
       "33                  0.0            0.681818            0.590909   \n",
       "30                  0.0            0.590909            0.590909   \n",
       "3                   0.0            0.681818            0.636364   \n",
       "36                  0.0            0.681818            0.636364   \n",
       "27                  0.0            0.681818            0.636364   \n",
       "35                  0.0            0.590909            0.590909   \n",
       "34                  0.0            0.681818            0.636364   \n",
       "5                   0.0            0.636364            0.681818   \n",
       "11                  0.0            0.636364            0.681818   \n",
       "15                  0.0            0.636364            0.636364   \n",
       "8                   0.0            0.590909            0.636364   \n",
       "23                  0.0            0.590909            0.636364   \n",
       "32                  0.0            0.545455            0.681818   \n",
       "19                  0.0            0.590909            0.681818   \n",
       "10                  0.0            0.500000            0.545455   \n",
       "29                  0.0            0.681818            0.727273   \n",
       "42                  0.0            0.681818            0.727273   \n",
       "7                   0.0            0.590909            0.636364   \n",
       "28                  0.0            0.590909            0.636364   \n",
       "24                  0.0            0.590909            0.636364   \n",
       "4                   0.0            0.636364            0.681818   \n",
       "47                  0.0            0.727273            0.727273   \n",
       "20                  0.0            0.727273            0.727273   \n",
       "17                  0.0            0.681818            0.681818   \n",
       "6                   0.0            0.590909            0.636364   \n",
       "31                  0.0            0.727273            0.772727   \n",
       "12                  0.0            0.636364            0.681818   \n",
       "22                  0.0            0.636364            0.681818   \n",
       "13                  0.0            0.727273            0.727273   \n",
       "1                   0.0            0.727273            0.727273   \n",
       "2                   0.0            0.590909            0.772727   \n",
       "26                  0.0            0.636364            0.681818   \n",
       "43                  0.0            0.727273            0.727273   \n",
       "49                  0.0            0.454545            0.590909   \n",
       "37                  0.0            0.727273            0.727273   \n",
       "39                  0.0            0.454545            0.727273   \n",
       "25                  0.0            0.727273            0.727273   \n",
       "18                  0.0            0.500000            0.727273   \n",
       "38                  0.0            0.454545            0.454545   \n",
       "16                  0.0            0.727273            0.727273   \n",
       "0                   0.0            0.727273            0.727273   \n",
       "41                  0.0            0.363636            0.363636   \n",
       "14                  0.0            0.227273            0.227273   \n",
       "21                  0.0            0.681818            0.636364   \n",
       "\n",
       "    split2_test_recall  split3_test_recall  split4_test_recall  \\\n",
       "44            0.772727            0.619048            0.904762   \n",
       "40            0.772727            0.619048            0.857143   \n",
       "9             0.727273            0.523810            0.857143   \n",
       "45            0.818182            0.619048            0.857143   \n",
       "46            0.772727            0.619048            0.857143   \n",
       "48            0.772727            0.619048            0.809524   \n",
       "33            0.818182            0.619048            0.857143   \n",
       "30            0.727273            0.523810            0.809524   \n",
       "3             0.818182            0.619048            0.857143   \n",
       "36            0.772727            0.619048            0.857143   \n",
       "27            0.772727            0.571429            0.857143   \n",
       "35            0.727273            0.523810            0.809524   \n",
       "34            0.818182            0.619048            0.857143   \n",
       "5             0.727273            0.619048            0.857143   \n",
       "11            0.727273            0.523810            0.809524   \n",
       "15            0.772727            0.476190            0.809524   \n",
       "8             0.727273            0.523810            0.857143   \n",
       "23            0.727273            0.523810            0.857143   \n",
       "32            0.727273            0.571429            0.809524   \n",
       "19            0.727273            0.571429            0.809524   \n",
       "10            0.727273            0.476190            0.761905   \n",
       "29            0.727273            0.571429            0.904762   \n",
       "42            0.727273            0.571429            0.904762   \n",
       "7             0.727273            0.428571            0.761905   \n",
       "28            0.727273            0.428571            0.761905   \n",
       "24            0.727273            0.428571            0.761905   \n",
       "4             0.727273            0.619048            0.809524   \n",
       "47            0.727273            0.619048            0.857143   \n",
       "20            0.727273            0.619048            0.857143   \n",
       "17            0.681818            0.571429            0.809524   \n",
       "6             0.727273            0.476190            0.809524   \n",
       "31            0.727273            0.571429            0.809524   \n",
       "12            0.727273            0.619048            0.809524   \n",
       "22            0.727273            0.619048            0.809524   \n",
       "13            0.727273            0.619048            0.904762   \n",
       "1             0.727273            0.619048            0.809524   \n",
       "2             0.681818            0.571429            0.809524   \n",
       "26            0.727273            0.619048            0.809524   \n",
       "43            0.727273            0.619048            0.809524   \n",
       "49            0.545455            0.428571            0.761905   \n",
       "37            0.727273            0.619048            0.857143   \n",
       "39            0.636364            0.476190            0.809524   \n",
       "25            0.727273            0.619048            0.857143   \n",
       "18            0.636364            0.476190            0.761905   \n",
       "38            0.500000            0.380952            0.761905   \n",
       "16            0.727273            0.619048            0.809524   \n",
       "0             0.727273            0.619048            0.809524   \n",
       "41            0.409091            0.333333            0.428571   \n",
       "14            0.318182            0.238095            0.380952   \n",
       "21            0.818182            0.666667            0.857143   \n",
       "\n",
       "    mean_test_recall  std_test_recall  rank_test_recall  split0_train_recall  \\\n",
       "44          0.722944         0.112945                 7                  1.0   \n",
       "40          0.704329         0.098665                21                  1.0   \n",
       "9           0.667100         0.115775                33                  1.0   \n",
       "45          0.722511         0.105190                 8                  1.0   \n",
       "46          0.704329         0.098665                21                  1.0   \n",
       "48          0.712987         0.074549                20                  1.0   \n",
       "33          0.713420         0.106344                18                  1.0   \n",
       "30          0.648485         0.104226                38                  1.0   \n",
       "3           0.722511         0.097016                 8                  1.0   \n",
       "36          0.713420         0.089461                18                  1.0   \n",
       "27          0.703896         0.100815                24                  1.0   \n",
       "35          0.648485         0.104226                38                  1.0   \n",
       "34          0.722511         0.097016                 8                  1.0   \n",
       "5           0.704329         0.085179                21                  1.0   \n",
       "11          0.675758         0.095104                32                  1.0   \n",
       "15          0.666234         0.118130                37                  1.0   \n",
       "8           0.667100         0.115775                33                  1.0   \n",
       "23          0.667100         0.115775                33                  1.0   \n",
       "32          0.667100         0.098054                33                  1.0   \n",
       "19          0.676190         0.087940                31                  1.0   \n",
       "10          0.602165         0.118905                46                  1.0   \n",
       "29          0.722511         0.107464                 8                  1.0   \n",
       "42          0.722511         0.107464                 8                  1.0   \n",
       "7           0.629004         0.117472                41                  1.0   \n",
       "28          0.629004         0.117472                41                  1.0   \n",
       "24          0.629004         0.117472                41                  1.0   \n",
       "4           0.694805         0.068612                25                  1.0   \n",
       "47          0.731602         0.075479                 3                  1.0   \n",
       "20          0.731602         0.075479                 3                  1.0   \n",
       "17          0.685281         0.075412                29                  1.0   \n",
       "6           0.648052         0.114269                40                  1.0   \n",
       "31          0.721645         0.081192                17                  1.0   \n",
       "12          0.694805         0.068612                25                  1.0   \n",
       "22          0.694805         0.068612                25                  1.0   \n",
       "13          0.741126         0.091930                 1                  1.0   \n",
       "1           0.722078         0.060569                13                  1.0   \n",
       "2           0.685281         0.094830                29                  1.0   \n",
       "26          0.694805         0.068612                25                  1.0   \n",
       "43          0.722078         0.060569                13                  1.0   \n",
       "49          0.556277         0.118539                47                  1.0   \n",
       "37          0.731602         0.075479                 3                  1.0   \n",
       "39          0.620779         0.138382                44                  1.0   \n",
       "25          0.731602         0.075479                 3                  1.0   \n",
       "18          0.620346         0.115752                45                  1.0   \n",
       "38          0.510390         0.131422                48                  1.0   \n",
       "16          0.722078         0.060569                13                  1.0   \n",
       "0           0.722078         0.060569                13                  1.0   \n",
       "41          0.379654         0.034404                49                  1.0   \n",
       "14          0.278355         0.061566                50                  1.0   \n",
       "21          0.732035         0.088341                 2                  1.0   \n",
       "\n",
       "    split1_train_recall  split2_train_recall  split3_train_recall  \\\n",
       "44                  1.0                  1.0                  1.0   \n",
       "40                  1.0                  1.0                  1.0   \n",
       "9                   1.0                  1.0                  1.0   \n",
       "45                  1.0                  1.0                  1.0   \n",
       "46                  1.0                  1.0                  1.0   \n",
       "48                  1.0                  1.0                  1.0   \n",
       "33                  1.0                  1.0                  1.0   \n",
       "30                  1.0                  1.0                  1.0   \n",
       "3                   1.0                  1.0                  1.0   \n",
       "36                  1.0                  1.0                  1.0   \n",
       "27                  1.0                  1.0                  1.0   \n",
       "35                  1.0                  1.0                  1.0   \n",
       "34                  1.0                  1.0                  1.0   \n",
       "5                   1.0                  1.0                  1.0   \n",
       "11                  1.0                  1.0                  1.0   \n",
       "15                  1.0                  1.0                  1.0   \n",
       "8                   1.0                  1.0                  1.0   \n",
       "23                  1.0                  1.0                  1.0   \n",
       "32                  1.0                  1.0                  1.0   \n",
       "19                  1.0                  1.0                  1.0   \n",
       "10                  1.0                  1.0                  1.0   \n",
       "29                  1.0                  1.0                  1.0   \n",
       "42                  1.0                  1.0                  1.0   \n",
       "7                   1.0                  1.0                  1.0   \n",
       "28                  1.0                  1.0                  1.0   \n",
       "24                  1.0                  1.0                  1.0   \n",
       "4                   1.0                  1.0                  1.0   \n",
       "47                  1.0                  1.0                  1.0   \n",
       "20                  1.0                  1.0                  1.0   \n",
       "17                  1.0                  1.0                  1.0   \n",
       "6                   1.0                  1.0                  1.0   \n",
       "31                  1.0                  1.0                  1.0   \n",
       "12                  1.0                  1.0                  1.0   \n",
       "22                  1.0                  1.0                  1.0   \n",
       "13                  1.0                  1.0                  1.0   \n",
       "1                   1.0                  1.0                  1.0   \n",
       "2                   1.0                  1.0                  1.0   \n",
       "26                  1.0                  1.0                  1.0   \n",
       "43                  1.0                  1.0                  1.0   \n",
       "49                  1.0                  1.0                  1.0   \n",
       "37                  1.0                  1.0                  1.0   \n",
       "39                  1.0                  1.0                  1.0   \n",
       "25                  1.0                  1.0                  1.0   \n",
       "18                  1.0                  1.0                  1.0   \n",
       "38                  1.0                  1.0                  1.0   \n",
       "16                  1.0                  1.0                  1.0   \n",
       "0                   1.0                  1.0                  1.0   \n",
       "41                  1.0                  1.0                  1.0   \n",
       "14                  1.0                  1.0                  1.0   \n",
       "21                  1.0                  1.0                  1.0   \n",
       "\n",
       "    split4_train_recall  mean_train_recall  std_train_recall  \\\n",
       "44                  1.0                1.0               0.0   \n",
       "40                  1.0                1.0               0.0   \n",
       "9                   1.0                1.0               0.0   \n",
       "45                  1.0                1.0               0.0   \n",
       "46                  1.0                1.0               0.0   \n",
       "48                  1.0                1.0               0.0   \n",
       "33                  1.0                1.0               0.0   \n",
       "30                  1.0                1.0               0.0   \n",
       "3                   1.0                1.0               0.0   \n",
       "36                  1.0                1.0               0.0   \n",
       "27                  1.0                1.0               0.0   \n",
       "35                  1.0                1.0               0.0   \n",
       "34                  1.0                1.0               0.0   \n",
       "5                   1.0                1.0               0.0   \n",
       "11                  1.0                1.0               0.0   \n",
       "15                  1.0                1.0               0.0   \n",
       "8                   1.0                1.0               0.0   \n",
       "23                  1.0                1.0               0.0   \n",
       "32                  1.0                1.0               0.0   \n",
       "19                  1.0                1.0               0.0   \n",
       "10                  1.0                1.0               0.0   \n",
       "29                  1.0                1.0               0.0   \n",
       "42                  1.0                1.0               0.0   \n",
       "7                   1.0                1.0               0.0   \n",
       "28                  1.0                1.0               0.0   \n",
       "24                  1.0                1.0               0.0   \n",
       "4                   1.0                1.0               0.0   \n",
       "47                  1.0                1.0               0.0   \n",
       "20                  1.0                1.0               0.0   \n",
       "17                  1.0                1.0               0.0   \n",
       "6                   1.0                1.0               0.0   \n",
       "31                  1.0                1.0               0.0   \n",
       "12                  1.0                1.0               0.0   \n",
       "22                  1.0                1.0               0.0   \n",
       "13                  1.0                1.0               0.0   \n",
       "1                   1.0                1.0               0.0   \n",
       "2                   1.0                1.0               0.0   \n",
       "26                  1.0                1.0               0.0   \n",
       "43                  1.0                1.0               0.0   \n",
       "49                  1.0                1.0               0.0   \n",
       "37                  1.0                1.0               0.0   \n",
       "39                  1.0                1.0               0.0   \n",
       "25                  1.0                1.0               0.0   \n",
       "18                  1.0                1.0               0.0   \n",
       "38                  1.0                1.0               0.0   \n",
       "16                  1.0                1.0               0.0   \n",
       "0                   1.0                1.0               0.0   \n",
       "41                  1.0                1.0               0.0   \n",
       "14                  1.0                1.0               0.0   \n",
       "21                  1.0                1.0               0.0   \n",
       "\n",
       "    split0_test_roc_auc  split1_test_roc_auc  split2_test_roc_auc  \\\n",
       "44             0.983957             0.940508             0.943069   \n",
       "40             0.978610             0.928030             0.946895   \n",
       "9              0.988191             0.944519             0.946670   \n",
       "45             0.980838             0.920677             0.944869   \n",
       "46             0.982175             0.929590             0.944869   \n",
       "48             0.981283             0.933155             0.937669   \n",
       "33             0.975045             0.915330             0.941944   \n",
       "30             0.987299             0.941622             0.949820   \n",
       "3              0.976381             0.910873             0.943519   \n",
       "36             0.978610             0.911542             0.939919   \n",
       "27             0.974599             0.903743             0.944419   \n",
       "35             0.990196             0.944296             0.951845   \n",
       "34             0.975045             0.907086             0.939019   \n",
       "5              0.973708             0.900401             0.939019   \n",
       "11             0.978387             0.924242             0.936094   \n",
       "15             0.991533             0.944296             0.943744   \n",
       "8              0.974153             0.915107             0.936094   \n",
       "23             0.974153             0.915107             0.936094   \n",
       "32             0.973930             0.914661             0.934293   \n",
       "19             0.978610             0.915998             0.937669   \n",
       "10             0.990642             0.946078             0.940819   \n",
       "29             0.974599             0.927807             0.925518   \n",
       "42             0.974599             0.927807             0.925518   \n",
       "7              0.986854             0.941622             0.941719   \n",
       "28             0.986854             0.941622             0.941719   \n",
       "24             0.986854             0.941622             0.941719   \n",
       "4              0.968806             0.907531             0.932943   \n",
       "47             0.973708             0.918895             0.920342   \n",
       "20             0.973708             0.918895             0.920342   \n",
       "17             0.966132             0.901961             0.932268   \n",
       "6              0.987299             0.938280             0.941044   \n",
       "31             0.974822             0.927807             0.926418   \n",
       "12             0.965686             0.903966             0.929793   \n",
       "22             0.969697             0.910205             0.923492   \n",
       "13             0.969029             0.924688             0.911791   \n",
       "1              0.967914             0.919786             0.911566   \n",
       "2              0.981283             0.944519             0.928443   \n",
       "26             0.962344             0.900624             0.928218   \n",
       "43             0.965463             0.912433             0.912016   \n",
       "49             0.989528             0.945633             0.933843   \n",
       "37             0.959225             0.910428             0.913816   \n",
       "39             0.983066             0.949421             0.923267   \n",
       "25             0.962567             0.912879             0.908641   \n",
       "18             0.983957             0.952986             0.921242   \n",
       "38             0.990642             0.953877             0.942169   \n",
       "16             0.957219             0.903298             0.904815   \n",
       "0              0.957219             0.903298             0.904815   \n",
       "41             0.993316             0.953877             0.937669   \n",
       "14             0.991979             0.962567             0.945545   \n",
       "21             0.977273             0.914216             0.939244   \n",
       "\n",
       "    split3_test_roc_auc  split4_test_roc_auc  mean_test_roc_auc  \\\n",
       "44             0.956349             0.953081           0.955393   \n",
       "40             0.959384             0.951447           0.952873   \n",
       "9              0.956116             0.956816           0.958462   \n",
       "45             0.957983             0.946312           0.950136   \n",
       "46             0.955182             0.945845           0.951532   \n",
       "48             0.952614             0.949113           0.950767   \n",
       "33             0.957983             0.945612           0.947183   \n",
       "30             0.947012             0.952148           0.955580   \n",
       "3              0.961251             0.946312           0.947667   \n",
       "36             0.960784             0.950980           0.948367   \n",
       "27             0.961718             0.955182           0.947932   \n",
       "35             0.953315             0.952381           0.958407   \n",
       "34             0.961018             0.946779           0.945789   \n",
       "5              0.961718             0.951914           0.945352   \n",
       "11             0.952848             0.945845           0.947483   \n",
       "15             0.945845             0.952148           0.955513   \n",
       "8              0.951447             0.948179           0.944996   \n",
       "23             0.951447             0.948179           0.944996   \n",
       "32             0.952381             0.947012           0.944456   \n",
       "19             0.949346             0.948880           0.946101   \n",
       "10             0.949580             0.952848           0.955993   \n",
       "29             0.939776             0.947246           0.942989   \n",
       "42             0.939776             0.947246           0.942989   \n",
       "7              0.944678             0.949346           0.952844   \n",
       "28             0.944678             0.949346           0.952844   \n",
       "24             0.944678             0.949346           0.952844   \n",
       "4              0.951214             0.940243           0.940147   \n",
       "47             0.938609             0.946779           0.939666   \n",
       "20             0.938609             0.946779           0.939666   \n",
       "17             0.952381             0.945378           0.939624   \n",
       "6              0.942810             0.945845           0.951056   \n",
       "31             0.937675             0.942810           0.941906   \n",
       "12             0.949346             0.942577           0.938274   \n",
       "22             0.948646             0.942810           0.938970   \n",
       "13             0.938375             0.946312           0.938039   \n",
       "1              0.935808             0.948880           0.936791   \n",
       "2              0.940476             0.948413           0.948627   \n",
       "26             0.950280             0.941410           0.936575   \n",
       "43             0.937208             0.943744           0.934173   \n",
       "49             0.944911             0.953081           0.953399   \n",
       "37             0.935574             0.944678           0.932744   \n",
       "39             0.936741             0.948413           0.948182   \n",
       "25             0.934174             0.944911           0.932634   \n",
       "18             0.935808             0.949346           0.948668   \n",
       "38             0.944911             0.951681           0.956656   \n",
       "16             0.931373             0.941410           0.927623   \n",
       "0              0.931373             0.941410           0.927623   \n",
       "41             0.941877             0.951914           0.955730   \n",
       "14             0.936975             0.950047           0.957422   \n",
       "21             0.959384             0.944678           0.946959   \n",
       "\n",
       "    std_test_roc_auc  rank_test_roc_auc  split0_train_roc_auc  \\\n",
       "44          0.015464                  9                   1.0   \n",
       "40          0.016491                 11                   1.0   \n",
       "9           0.015656                  1                   1.0   \n",
       "45          0.019567                 18                   1.0   \n",
       "46          0.017381                 15                   1.0   \n",
       "48          0.016847                 17                   1.0   \n",
       "33          0.019685                 26                   1.0   \n",
       "30          0.016244                  7                   1.0   \n",
       "3           0.021826                 24                   1.0   \n",
       "36          0.022375                 21                   1.0   \n",
       "27          0.024161                 23                   1.0   \n",
       "35          0.016217                  2                   1.0   \n",
       "34          0.022946                 29                   1.0   \n",
       "5           0.025202                 30                   1.0   \n",
       "11          0.018201                 25                   1.0   \n",
       "15          0.018257                  8                   1.0   \n",
       "8           0.019362                 31                   1.0   \n",
       "23          0.019362                 31                   1.0   \n",
       "32          0.019642                 33                   1.0   \n",
       "19          0.020260                 28                   1.0   \n",
       "10          0.017777                  5                   1.0   \n",
       "29          0.017683                 34                   1.0   \n",
       "42          0.017683                 34                   1.0   \n",
       "7           0.017235                 12                   1.0   \n",
       "28          0.017235                 12                   1.0   \n",
       "24          0.017235                 12                   1.0   \n",
       "4           0.020295                 37                   1.0   \n",
       "47          0.020077                 38                   1.0   \n",
       "20          0.020077                 38                   1.0   \n",
       "17          0.021777                 40                   1.0   \n",
       "6           0.018288                 16                   1.0   \n",
       "31          0.017555                 36                   1.0   \n",
       "12          0.020695                 42                   1.0   \n",
       "22          0.020589                 41                   1.0   \n",
       "13          0.019474                 43                   1.0   \n",
       "1           0.020205                 44                   1.0   \n",
       "2           0.017648                 20                   1.0   \n",
       "26          0.021155                 45                   1.0   \n",
       "43          0.020216                 46                   1.0   \n",
       "49          0.019080                 10                   1.0   \n",
       "37          0.018482                 47                   1.0   \n",
       "39          0.019850                 22                   1.0   \n",
       "25          0.020075                 48                   1.0   \n",
       "18          0.020890                 19                   1.0   \n",
       "38          0.017523                  4                   1.0   \n",
       "16          0.020938                 49                   1.0   \n",
       "0           0.020938                 49                   1.0   \n",
       "41          0.019742                  6                   1.0   \n",
       "14          0.019153                  3                   1.0   \n",
       "21          0.021024                 27                   1.0   \n",
       "\n",
       "    split1_train_roc_auc  split2_train_roc_auc  split3_train_roc_auc  \\\n",
       "44                   1.0                   1.0                   1.0   \n",
       "40                   1.0                   1.0                   1.0   \n",
       "9                    1.0                   1.0                   1.0   \n",
       "45                   1.0                   1.0                   1.0   \n",
       "46                   1.0                   1.0                   1.0   \n",
       "48                   1.0                   1.0                   1.0   \n",
       "33                   1.0                   1.0                   1.0   \n",
       "30                   1.0                   1.0                   1.0   \n",
       "3                    1.0                   1.0                   1.0   \n",
       "36                   1.0                   1.0                   1.0   \n",
       "27                   1.0                   1.0                   1.0   \n",
       "35                   1.0                   1.0                   1.0   \n",
       "34                   1.0                   1.0                   1.0   \n",
       "5                    1.0                   1.0                   1.0   \n",
       "11                   1.0                   1.0                   1.0   \n",
       "15                   1.0                   1.0                   1.0   \n",
       "8                    1.0                   1.0                   1.0   \n",
       "23                   1.0                   1.0                   1.0   \n",
       "32                   1.0                   1.0                   1.0   \n",
       "19                   1.0                   1.0                   1.0   \n",
       "10                   1.0                   1.0                   1.0   \n",
       "29                   1.0                   1.0                   1.0   \n",
       "42                   1.0                   1.0                   1.0   \n",
       "7                    1.0                   1.0                   1.0   \n",
       "28                   1.0                   1.0                   1.0   \n",
       "24                   1.0                   1.0                   1.0   \n",
       "4                    1.0                   1.0                   1.0   \n",
       "47                   1.0                   1.0                   1.0   \n",
       "20                   1.0                   1.0                   1.0   \n",
       "17                   1.0                   1.0                   1.0   \n",
       "6                    1.0                   1.0                   1.0   \n",
       "31                   1.0                   1.0                   1.0   \n",
       "12                   1.0                   1.0                   1.0   \n",
       "22                   1.0                   1.0                   1.0   \n",
       "13                   1.0                   1.0                   1.0   \n",
       "1                    1.0                   1.0                   1.0   \n",
       "2                    1.0                   1.0                   1.0   \n",
       "26                   1.0                   1.0                   1.0   \n",
       "43                   1.0                   1.0                   1.0   \n",
       "49                   1.0                   1.0                   1.0   \n",
       "37                   1.0                   1.0                   1.0   \n",
       "39                   1.0                   1.0                   1.0   \n",
       "25                   1.0                   1.0                   1.0   \n",
       "18                   1.0                   1.0                   1.0   \n",
       "38                   1.0                   1.0                   1.0   \n",
       "16                   1.0                   1.0                   1.0   \n",
       "0                    1.0                   1.0                   1.0   \n",
       "41                   1.0                   1.0                   1.0   \n",
       "14                   1.0                   1.0                   1.0   \n",
       "21                   1.0                   1.0                   1.0   \n",
       "\n",
       "    split4_train_roc_auc  mean_train_roc_auc  std_train_roc_auc  \n",
       "44                   1.0                 1.0       4.965068e-17  \n",
       "40                   1.0                 1.0       4.965068e-17  \n",
       "9                    1.0                 1.0       7.021667e-17  \n",
       "45                   1.0                 1.0       4.965068e-17  \n",
       "46                   1.0                 1.0       0.000000e+00  \n",
       "48                   1.0                 1.0       7.021667e-17  \n",
       "33                   1.0                 1.0       0.000000e+00  \n",
       "30                   1.0                 1.0       0.000000e+00  \n",
       "3                    1.0                 1.0       0.000000e+00  \n",
       "36                   1.0                 1.0       0.000000e+00  \n",
       "27                   1.0                 1.0       0.000000e+00  \n",
       "35                   1.0                 1.0       4.965068e-17  \n",
       "34                   1.0                 1.0       7.021667e-17  \n",
       "5                    1.0                 1.0       0.000000e+00  \n",
       "11                   1.0                 1.0       0.000000e+00  \n",
       "15                   1.0                 1.0       0.000000e+00  \n",
       "8                    1.0                 1.0       4.965068e-17  \n",
       "23                   1.0                 1.0       4.965068e-17  \n",
       "32                   1.0                 1.0       0.000000e+00  \n",
       "19                   1.0                 1.0       4.965068e-17  \n",
       "10                   1.0                 1.0       4.965068e-17  \n",
       "29                   1.0                 1.0       0.000000e+00  \n",
       "42                   1.0                 1.0       0.000000e+00  \n",
       "7                    1.0                 1.0       0.000000e+00  \n",
       "28                   1.0                 1.0       0.000000e+00  \n",
       "24                   1.0                 1.0       0.000000e+00  \n",
       "4                    1.0                 1.0       0.000000e+00  \n",
       "47                   1.0                 1.0       4.965068e-17  \n",
       "20                   1.0                 1.0       4.965068e-17  \n",
       "17                   1.0                 1.0       0.000000e+00  \n",
       "6                    1.0                 1.0       0.000000e+00  \n",
       "31                   1.0                 1.0       0.000000e+00  \n",
       "12                   1.0                 1.0       0.000000e+00  \n",
       "22                   1.0                 1.0       0.000000e+00  \n",
       "13                   1.0                 1.0       0.000000e+00  \n",
       "1                    1.0                 1.0       4.965068e-17  \n",
       "2                    1.0                 1.0       0.000000e+00  \n",
       "26                   1.0                 1.0       7.021667e-17  \n",
       "43                   1.0                 1.0       0.000000e+00  \n",
       "49                   1.0                 1.0       7.021667e-17  \n",
       "37                   1.0                 1.0       0.000000e+00  \n",
       "39                   1.0                 1.0       0.000000e+00  \n",
       "25                   1.0                 1.0       4.965068e-17  \n",
       "18                   1.0                 1.0       0.000000e+00  \n",
       "38                   1.0                 1.0       0.000000e+00  \n",
       "16                   1.0                 1.0       0.000000e+00  \n",
       "0                    1.0                 1.0       0.000000e+00  \n",
       "41                   1.0                 1.0       0.000000e+00  \n",
       "14                   1.0                 1.0       4.965068e-17  \n",
       "21                   1.0                 1.0       0.000000e+00  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(grid.cv_results_).sort_values(by='rank_test_neg_log_loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "55920336-6f45-4c55-adc3-f01b05122efd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'forest__bootstrap': True,\n",
       " 'forest__ccp_alpha': 0.0,\n",
       " 'forest__class_weight': {0: 1, 1: 1},\n",
       " 'forest__criterion': 'log_loss',\n",
       " 'forest__max_depth': None,\n",
       " 'forest__max_features': 0.4722149251619493,\n",
       " 'forest__max_leaf_nodes': None,\n",
       " 'forest__max_samples': None,\n",
       " 'forest__min_impurity_decrease': 0.0,\n",
       " 'forest__min_samples_leaf': 1,\n",
       " 'forest__min_samples_split': 2,\n",
       " 'forest__min_weight_fraction_leaf': 0.0,\n",
       " 'forest__n_estimators': 1000,\n",
       " 'forest__n_jobs': -1,\n",
       " 'forest__oob_score': False,\n",
       " 'forest__random_state': 42,\n",
       " 'forest__verbose': 10,\n",
       " 'forest__warm_start': True}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "892f7292-8c40-4bf3-b4f0-ec9e5707f070",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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