baseline_pipeline.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"
   ]
  },
  {
   "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": 7,
   "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": 8,
   "id": "acc1f00c-8bdf-4350-9835-ad3dd7040815",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "clf = Pipeline(\n",
    "    steps=[\n",
    "        (\"preprocessor\", preprocessor),\n",
    "        (\"dummy\", DummyClassifier())\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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;dummy&#x27;, DummyClassifier())])</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-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" 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;dummy&#x27;, DummyClassifier())])</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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" 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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" 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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" 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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" 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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" 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-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" 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-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyClassifier</label><div class=\"sk-toggleable__content\"><pre>DummyClassifier()</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",
       "                ('dummy', DummyClassifier())])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "438add06-ef45-4949-9d1c-04352edb8c53",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "clf.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "14831c5d-7df0-46f2-b68a-0ae0d223d01c",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'dummy__constant' : [0,1],\n",
    "               'dummy__random_state': [42],\n",
    "               'dummy__strategy': [\n",
    "                   \"most_frequent\",\n",
    "                   \"prior\",\n",
    "                   \"stratified\",\n",
    "                   \"uniform\",\n",
    "                   \"constant\"],\n",
    "              }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b6b89e92-e3dd-475c-8fdf-5b643bd089a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = ['accuracy', 'f1', 'neg_log_loss', 'precision', 'recall', 'roc_auc',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b1e36ef5-8801-469d-a368-6dc5b70608bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(clf,\n",
    "                    param_grid=param_grid,\n",
    "                    scoring=metrics,\n",
    "                    verbose=1,\n",
    "                    refit=\"neg_log_loss\",\n",
    "                    return_train_score=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "dcffc9ac-342f-4e24-ab8a-84ecf61f86a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(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;cat&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;encoder&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                                                         [&#x27;EJ&#x27;])])),\n",
       "                                       (&#x27;dummy&#x27;, DummyClassifier())]),\n",
       "             param_grid={&#x27;dummy__constant&#x27;: [0, 1], &#x27;dummy__random_state&#x27;: [42],\n",
       "                         &#x27;dummy__strategy&#x27;: [&#x27;most_frequent&#x27;, &#x27;prior&#x27;,\n",
       "                                             &#x27;stratified&#x27;, &#x27;uniform&#x27;,\n",
       "                                             &#x27;constant&#x27;]},\n",
       "             refit=&#x27;neg_log_loss&#x27;,\n",
       "             scoring=[&#x27;accuracy&#x27;, &#x27;f1&#x27;, &#x27;neg_log_loss&#x27;, &#x27;precision&#x27;, &#x27;recall&#x27;,\n",
       "                      &#x27;roc_auc&#x27;],\n",
       "             verbose=1)</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-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(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;cat&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;encoder&#x27;,\n",
       "                                                                                          OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                                                         [&#x27;EJ&#x27;])])),\n",
       "                                       (&#x27;dummy&#x27;, DummyClassifier())]),\n",
       "             param_grid={&#x27;dummy__constant&#x27;: [0, 1], &#x27;dummy__random_state&#x27;: [42],\n",
       "                         &#x27;dummy__strategy&#x27;: [&#x27;most_frequent&#x27;, &#x27;prior&#x27;,\n",
       "                                             &#x27;stratified&#x27;, &#x27;uniform&#x27;,\n",
       "                                             &#x27;constant&#x27;]},\n",
       "             refit=&#x27;neg_log_loss&#x27;,\n",
       "             scoring=[&#x27;accuracy&#x27;, &#x27;f1&#x27;, &#x27;neg_log_loss&#x27;, &#x27;precision&#x27;, &#x27;recall&#x27;,\n",
       "                      &#x27;roc_auc&#x27;],\n",
       "             verbose=1)</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\">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;dummy&#x27;, DummyClassifier())])</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-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" 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-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" 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-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" 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-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" 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-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" 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-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" 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-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyClassifier</label><div class=\"sk-toggleable__content\"><pre>DummyClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(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",
       "                                                                        ('cat',\n",
       "                                                                         Pipeline(steps=[('encoder',\n",
       "                                                                                          OneHotEncoder(drop='first'))]),\n",
       "                                                                         ['EJ'])])),\n",
       "                                       ('dummy', DummyClassifier())]),\n",
       "             param_grid={'dummy__constant': [0, 1], 'dummy__random_state': [42],\n",
       "                         'dummy__strategy': ['most_frequent', 'prior',\n",
       "                                             'stratified', 'uniform',\n",
       "                                             'constant']},\n",
       "             refit='neg_log_loss',\n",
       "             scoring=['accuracy', 'f1', 'neg_log_loss', 'precision', 'recall',\n",
       "                      'roc_auc'],\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bafae994-5998-4338-987d-35b1b761791c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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_dummy__constant</th>\n",
       "      <th>param_dummy__random_state</th>\n",
       "      <th>param_dummy__strategy</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_test_f1</th>\n",
       "      <th>split1_test_f1</th>\n",
       "      <th>split2_test_f1</th>\n",
       "      <th>split3_test_f1</th>\n",
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       "      <th>mean_test_f1</th>\n",
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       "      <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_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_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_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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.006672</td>\n",
       "      <td>0.000386</td>\n",
       "      <td>0.009393</td>\n",
       "      <td>0.000759</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>prior</td>\n",
       "      <td>{'dummy__constant': 0, 'dummy__random_state': 42, 'dummy__strategy': 'prior'}</td>\n",
       "      <td>0.822581</td>\n",
       "      <td>0.822581</td>\n",
       "      <td>0.821138</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.824967</td>\n",
       "      <td>0.003551</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.467488</td>\n",
       "      <td>-0.467488</td>\n",
       "      <td>-0.469742</td>\n",
       "      <td>-0.457145</td>\n",
       "      <td>-0.457145</td>\n",
       "      <td>-0.463802</td>\n",
       "      <td>0.005497</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.006044</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.008783</td>\n",
       "      <td>0.000946</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>prior</td>\n",
       "      <td>{'dummy__constant': 1, 'dummy__random_state': 42, 'dummy__strategy': 'prior'}</td>\n",
       "      <td>0.822581</td>\n",
       "      <td>0.822581</td>\n",
       "      <td>0.821138</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.824967</td>\n",
       "      <td>0.003551</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.467488</td>\n",
       "      <td>-0.467488</td>\n",
       "      <td>-0.469742</td>\n",
       "      <td>-0.457145</td>\n",
       "      <td>-0.457145</td>\n",
       "      <td>-0.463802</td>\n",
       "      <td>0.005497</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.006067</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>0.008563</td>\n",
       "      <td>0.000571</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'dummy__constant': 0, 'dummy__random_state': 42, 'dummy__strategy': 'uniform'}</td>\n",
       "      <td>0.451613</td>\n",
       "      <td>0.435484</td>\n",
       "      <td>0.463415</td>\n",
       "      <td>0.390244</td>\n",
       "      <td>0.455285</td>\n",
       "      <td>0.439208</td>\n",
       "      <td>0.026114</td>\n",
       "      <td>8</td>\n",
       "      <td>0.276596</td>\n",
       "      <td>0.255319</td>\n",
       "      <td>0.297872</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>0.279570</td>\n",
       "      <td>0.260581</td>\n",
       "      <td>0.036135</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.693147</td>\n",
       "      <td>-0.693147</td>\n",
       "      <td>-0.693147</td>\n",
       "      <td>-0.693147</td>\n",
       "      <td>-0.693147</td>\n",
       "      <td>-0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.180556</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.194444</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.180556</td>\n",
       "      <td>0.169444</td>\n",
       "      <td>0.023895</td>\n",
       "      <td>4</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.564069</td>\n",
       "      <td>0.074398</td>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.006777</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>0.008434</td>\n",
       "      <td>0.000560</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'dummy__constant': 1, 'dummy__random_state': 42, 'dummy__strategy': 'uniform'}</td>\n",
       "      <td>0.451613</td>\n",
       "      <td>0.435484</td>\n",
       "      <td>0.463415</td>\n",
       "      <td>0.390244</td>\n",
       "      <td>0.455285</td>\n",
       "      <td>0.439208</td>\n",
       "      <td>0.026114</td>\n",
       "      <td>8</td>\n",
       "      <td>0.276596</td>\n",
       "      <td>0.255319</td>\n",
       "      <td>0.297872</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>0.279570</td>\n",
       "      <td>0.260581</td>\n",
       "      <td>0.036135</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.693147</td>\n",
       "      <td>-0.693147</td>\n",
       "      <td>-0.693147</td>\n",
       "      <td>-0.693147</td>\n",
       "      <td>-0.693147</td>\n",
       "      <td>-0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.180556</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.194444</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.180556</td>\n",
       "      <td>0.169444</td>\n",
       "      <td>0.023895</td>\n",
       "      <td>4</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.564069</td>\n",
       "      <td>0.074398</td>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.007053</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.009986</td>\n",
       "      <td>0.000587</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>most_frequent</td>\n",
       "      <td>{'dummy__constant': 0, 'dummy__random_state': 42, 'dummy__strategy': 'most_frequent'}</td>\n",
       "      <td>0.822581</td>\n",
       "      <td>0.822581</td>\n",
       "      <td>0.821138</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.824967</td>\n",
       "      <td>0.003551</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>-6.394842</td>\n",
       "      <td>-6.394842</td>\n",
       "      <td>-6.446832</td>\n",
       "      <td>-6.153794</td>\n",
       "      <td>-6.153794</td>\n",
       "      <td>-6.308821</td>\n",
       "      <td>0.127994</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.006264</td>\n",
       "      <td>0.000777</td>\n",
       "      <td>0.008563</td>\n",
       "      <td>0.000591</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'dummy__constant': 0, 'dummy__random_state': 42, 'dummy__strategy': 'constant'}</td>\n",
       "      <td>0.822581</td>\n",
       "      <td>0.822581</td>\n",
       "      <td>0.821138</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.824967</td>\n",
       "      <td>0.003551</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>-6.394842</td>\n",
       "      <td>-6.394842</td>\n",
       "      <td>-6.446832</td>\n",
       "      <td>-6.153794</td>\n",
       "      <td>-6.153794</td>\n",
       "      <td>-6.308821</td>\n",
       "      <td>0.127994</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.006139</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>0.008647</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>most_frequent</td>\n",
       "      <td>{'dummy__constant': 1, 'dummy__random_state': 42, 'dummy__strategy': 'most_frequent'}</td>\n",
       "      <td>0.822581</td>\n",
       "      <td>0.822581</td>\n",
       "      <td>0.821138</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.824967</td>\n",
       "      <td>0.003551</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>-6.394842</td>\n",
       "      <td>-6.394842</td>\n",
       "      <td>-6.446832</td>\n",
       "      <td>-6.153794</td>\n",
       "      <td>-6.153794</td>\n",
       "      <td>-6.308821</td>\n",
       "      <td>0.127994</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.006652</td>\n",
       "      <td>0.000941</td>\n",
       "      <td>0.008936</td>\n",
       "      <td>0.000646</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>stratified</td>\n",
       "      <td>{'dummy__constant': 0, 'dummy__random_state': 42, 'dummy__strategy': 'stratified'}</td>\n",
       "      <td>0.709677</td>\n",
       "      <td>0.725806</td>\n",
       "      <td>0.723577</td>\n",
       "      <td>0.731707</td>\n",
       "      <td>0.699187</td>\n",
       "      <td>0.717991</td>\n",
       "      <td>0.011859</td>\n",
       "      <td>6</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.195122</td>\n",
       "      <td>0.097561</td>\n",
       "      <td>0.163298</td>\n",
       "      <td>0.038024</td>\n",
       "      <td>4</td>\n",
       "      <td>-10.464286</td>\n",
       "      <td>-9.882937</td>\n",
       "      <td>-9.963286</td>\n",
       "      <td>-9.670248</td>\n",
       "      <td>-10.842400</td>\n",
       "      <td>-10.164632</td>\n",
       "      <td>0.427427</td>\n",
       "      <td>8</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>0.036312</td>\n",
       "      <td>4</td>\n",
       "      <td>0.484848</td>\n",
       "      <td>0.512478</td>\n",
       "      <td>0.511701</td>\n",
       "      <td>0.516807</td>\n",
       "      <td>0.459384</td>\n",
       "      <td>0.497044</td>\n",
       "      <td>0.021957</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.006643</td>\n",
       "      <td>0.000562</td>\n",
       "      <td>0.008473</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>stratified</td>\n",
       "      <td>{'dummy__constant': 1, 'dummy__random_state': 42, 'dummy__strategy': 'stratified'}</td>\n",
       "      <td>0.709677</td>\n",
       "      <td>0.725806</td>\n",
       "      <td>0.723577</td>\n",
       "      <td>0.731707</td>\n",
       "      <td>0.699187</td>\n",
       "      <td>0.717991</td>\n",
       "      <td>0.011859</td>\n",
       "      <td>6</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.195122</td>\n",
       "      <td>0.097561</td>\n",
       "      <td>0.163298</td>\n",
       "      <td>0.038024</td>\n",
       "      <td>4</td>\n",
       "      <td>-10.464286</td>\n",
       "      <td>-9.882937</td>\n",
       "      <td>-9.963286</td>\n",
       "      <td>-9.670248</td>\n",
       "      <td>-10.842400</td>\n",
       "      <td>-10.164632</td>\n",
       "      <td>0.427427</td>\n",
       "      <td>8</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>0.036312</td>\n",
       "      <td>4</td>\n",
       "      <td>0.484848</td>\n",
       "      <td>0.512478</td>\n",
       "      <td>0.511701</td>\n",
       "      <td>0.516807</td>\n",
       "      <td>0.459384</td>\n",
       "      <td>0.497044</td>\n",
       "      <td>0.021957</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.006338</td>\n",
       "      <td>0.000382</td>\n",
       "      <td>0.009041</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'dummy__constant': 1, 'dummy__random_state': 42, 'dummy__strategy': 'constant'}</td>\n",
       "      <td>0.177419</td>\n",
       "      <td>0.177419</td>\n",
       "      <td>0.178862</td>\n",
       "      <td>0.170732</td>\n",
       "      <td>0.170732</td>\n",
       "      <td>0.175033</td>\n",
       "      <td>0.003551</td>\n",
       "      <td>10</td>\n",
       "      <td>0.301370</td>\n",
       "      <td>0.301370</td>\n",
       "      <td>0.303448</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.297904</td>\n",
       "      <td>0.005149</td>\n",
       "      <td>1</td>\n",
       "      <td>-29.648812</td>\n",
       "      <td>-29.648812</td>\n",
       "      <td>-29.596821</td>\n",
       "      <td>-29.889859</td>\n",
       "      <td>-29.889859</td>\n",
       "      <td>-29.734832</td>\n",
       "      <td>0.127994</td>\n",
       "      <td>10</td>\n",
       "      <td>0.177419</td>\n",
       "      <td>0.177419</td>\n",
       "      <td>0.178862</td>\n",
       "      <td>0.170732</td>\n",
       "      <td>0.170732</td>\n",
       "      <td>0.175033</td>\n",
       "      <td>0.003551</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "1       0.006672      0.000386         0.009393        0.000759   \n",
       "6       0.006044      0.000048         0.008783        0.000946   \n",
       "3       0.006067      0.000241         0.008563        0.000571   \n",
       "8       0.006777      0.000421         0.008434        0.000560   \n",
       "0       0.007053      0.000471         0.009986        0.000587   \n",
       "4       0.006264      0.000777         0.008563        0.000591   \n",
       "5       0.006139      0.000384         0.008647        0.000501   \n",
       "2       0.006652      0.000941         0.008936        0.000646   \n",
       "7       0.006643      0.000562         0.008473        0.000465   \n",
       "9       0.006338      0.000382         0.009041        0.000544   \n",
       "\n",
       "  param_dummy__constant param_dummy__random_state param_dummy__strategy  \\\n",
       "1                     0                        42                 prior   \n",
       "6                     1                        42                 prior   \n",
       "3                     0                        42               uniform   \n",
       "8                     1                        42               uniform   \n",
       "0                     0                        42         most_frequent   \n",
       "4                     0                        42              constant   \n",
       "5                     1                        42         most_frequent   \n",
       "2                     0                        42            stratified   \n",
       "7                     1                        42            stratified   \n",
       "9                     1                        42              constant   \n",
       "\n",
       "                                                                                  params  \\\n",
       "1          {'dummy__constant': 0, 'dummy__random_state': 42, 'dummy__strategy': 'prior'}   \n",
       "6          {'dummy__constant': 1, 'dummy__random_state': 42, 'dummy__strategy': 'prior'}   \n",
       "3        {'dummy__constant': 0, 'dummy__random_state': 42, 'dummy__strategy': 'uniform'}   \n",
       "8        {'dummy__constant': 1, 'dummy__random_state': 42, 'dummy__strategy': 'uniform'}   \n",
       "0  {'dummy__constant': 0, 'dummy__random_state': 42, 'dummy__strategy': 'most_frequent'}   \n",
       "4       {'dummy__constant': 0, 'dummy__random_state': 42, 'dummy__strategy': 'constant'}   \n",
       "5  {'dummy__constant': 1, 'dummy__random_state': 42, 'dummy__strategy': 'most_frequent'}   \n",
       "2     {'dummy__constant': 0, 'dummy__random_state': 42, 'dummy__strategy': 'stratified'}   \n",
       "7     {'dummy__constant': 1, 'dummy__random_state': 42, 'dummy__strategy': 'stratified'}   \n",
       "9       {'dummy__constant': 1, 'dummy__random_state': 42, 'dummy__strategy': 'constant'}   \n",
       "\n",
       "   split0_test_accuracy  split1_test_accuracy  split2_test_accuracy  \\\n",
       "1              0.822581              0.822581              0.821138   \n",
       "6              0.822581              0.822581              0.821138   \n",
       "3              0.451613              0.435484              0.463415   \n",
       "8              0.451613              0.435484              0.463415   \n",
       "0              0.822581              0.822581              0.821138   \n",
       "4              0.822581              0.822581              0.821138   \n",
       "5              0.822581              0.822581              0.821138   \n",
       "2              0.709677              0.725806              0.723577   \n",
       "7              0.709677              0.725806              0.723577   \n",
       "9              0.177419              0.177419              0.178862   \n",
       "\n",
       "   split3_test_accuracy  split4_test_accuracy  mean_test_accuracy  \\\n",
       "1              0.829268              0.829268            0.824967   \n",
       "6              0.829268              0.829268            0.824967   \n",
       "3              0.390244              0.455285            0.439208   \n",
       "8              0.390244              0.455285            0.439208   \n",
       "0              0.829268              0.829268            0.824967   \n",
       "4              0.829268              0.829268            0.824967   \n",
       "5              0.829268              0.829268            0.824967   \n",
       "2              0.731707              0.699187            0.717991   \n",
       "7              0.731707              0.699187            0.717991   \n",
       "9              0.170732              0.170732            0.175033   \n",
       "\n",
       "   std_test_accuracy  rank_test_accuracy  split0_test_f1  split1_test_f1  \\\n",
       "1           0.003551                   1        0.000000        0.000000   \n",
       "6           0.003551                   1        0.000000        0.000000   \n",
       "3           0.026114                   8        0.276596        0.255319   \n",
       "8           0.026114                   8        0.276596        0.255319   \n",
       "0           0.003551                   1        0.000000        0.000000   \n",
       "4           0.003551                   1        0.000000        0.000000   \n",
       "5           0.003551                   1        0.000000        0.000000   \n",
       "2           0.011859                   6        0.142857        0.190476   \n",
       "7           0.011859                   6        0.142857        0.190476   \n",
       "9           0.003551                  10        0.301370        0.301370   \n",
       "\n",
       "   split2_test_f1  split3_test_f1  split4_test_f1  mean_test_f1  std_test_f1  \\\n",
       "1        0.000000        0.000000        0.000000      0.000000     0.000000   \n",
       "6        0.000000        0.000000        0.000000      0.000000     0.000000   \n",
       "3        0.297872        0.193548        0.279570      0.260581     0.036135   \n",
       "8        0.297872        0.193548        0.279570      0.260581     0.036135   \n",
       "0        0.000000        0.000000        0.000000      0.000000     0.000000   \n",
       "4        0.000000        0.000000        0.000000      0.000000     0.000000   \n",
       "5        0.000000        0.000000        0.000000      0.000000     0.000000   \n",
       "2        0.190476        0.195122        0.097561      0.163298     0.038024   \n",
       "7        0.190476        0.195122        0.097561      0.163298     0.038024   \n",
       "9        0.303448        0.291667        0.291667      0.297904     0.005149   \n",
       "\n",
       "   rank_test_f1  split0_test_neg_log_loss  split1_test_neg_log_loss  \\\n",
       "1             6                 -0.467488                 -0.467488   \n",
       "6             6                 -0.467488                 -0.467488   \n",
       "3             2                 -0.693147                 -0.693147   \n",
       "8             2                 -0.693147                 -0.693147   \n",
       "0             6                 -6.394842                 -6.394842   \n",
       "4             6                 -6.394842                 -6.394842   \n",
       "5             6                 -6.394842                 -6.394842   \n",
       "2             4                -10.464286                 -9.882937   \n",
       "7             4                -10.464286                 -9.882937   \n",
       "9             1                -29.648812                -29.648812   \n",
       "\n",
       "   split2_test_neg_log_loss  split3_test_neg_log_loss  \\\n",
       "1                 -0.469742                 -0.457145   \n",
       "6                 -0.469742                 -0.457145   \n",
       "3                 -0.693147                 -0.693147   \n",
       "8                 -0.693147                 -0.693147   \n",
       "0                 -6.446832                 -6.153794   \n",
       "4                 -6.446832                 -6.153794   \n",
       "5                 -6.446832                 -6.153794   \n",
       "2                 -9.963286                 -9.670248   \n",
       "7                 -9.963286                 -9.670248   \n",
       "9                -29.596821                -29.889859   \n",
       "\n",
       "   split4_test_neg_log_loss  mean_test_neg_log_loss  std_test_neg_log_loss  \\\n",
       "1                 -0.457145               -0.463802               0.005497   \n",
       "6                 -0.457145               -0.463802               0.005497   \n",
       "3                 -0.693147               -0.693147               0.000000   \n",
       "8                 -0.693147               -0.693147               0.000000   \n",
       "0                 -6.153794               -6.308821               0.127994   \n",
       "4                 -6.153794               -6.308821               0.127994   \n",
       "5                 -6.153794               -6.308821               0.127994   \n",
       "2                -10.842400              -10.164632               0.427427   \n",
       "7                -10.842400              -10.164632               0.427427   \n",
       "9                -29.889859              -29.734832               0.127994   \n",
       "\n",
       "   rank_test_neg_log_loss  split0_test_precision  split1_test_precision  \\\n",
       "1                       1               0.000000               0.000000   \n",
       "6                       1               0.000000               0.000000   \n",
       "3                       3               0.180556               0.166667   \n",
       "8                       3               0.180556               0.166667   \n",
       "0                       5               0.000000               0.000000   \n",
       "4                       5               0.000000               0.000000   \n",
       "5                       5               0.000000               0.000000   \n",
       "2                       8               0.150000               0.200000   \n",
       "7                       8               0.150000               0.200000   \n",
       "9                      10               0.177419               0.177419   \n",
       "\n",
       "   split2_test_precision  split3_test_precision  split4_test_precision  \\\n",
       "1               0.000000               0.000000               0.000000   \n",
       "6               0.000000               0.000000               0.000000   \n",
       "3               0.194444               0.125000               0.180556   \n",
       "8               0.194444               0.125000               0.180556   \n",
       "0               0.000000               0.000000               0.000000   \n",
       "4               0.000000               0.000000               0.000000   \n",
       "5               0.000000               0.000000               0.000000   \n",
       "2               0.200000               0.200000               0.100000   \n",
       "7               0.200000               0.200000               0.100000   \n",
       "9               0.178862               0.170732               0.170732   \n",
       "\n",
       "   mean_test_precision  std_test_precision  rank_test_precision  \\\n",
       "1             0.000000            0.000000                    6   \n",
       "6             0.000000            0.000000                    6   \n",
       "3             0.169444            0.023895                    4   \n",
       "8             0.169444            0.023895                    4   \n",
       "0             0.000000            0.000000                    6   \n",
       "4             0.000000            0.000000                    6   \n",
       "5             0.000000            0.000000                    6   \n",
       "2             0.170000            0.040000                    2   \n",
       "7             0.170000            0.040000                    2   \n",
       "9             0.175033            0.003551                    1   \n",
       "\n",
       "   split0_test_recall  split1_test_recall  split2_test_recall  \\\n",
       "1            0.000000            0.000000            0.000000   \n",
       "6            0.000000            0.000000            0.000000   \n",
       "3            0.590909            0.545455            0.636364   \n",
       "8            0.590909            0.545455            0.636364   \n",
       "0            0.000000            0.000000            0.000000   \n",
       "4            0.000000            0.000000            0.000000   \n",
       "5            0.000000            0.000000            0.000000   \n",
       "2            0.136364            0.181818            0.181818   \n",
       "7            0.136364            0.181818            0.181818   \n",
       "9            1.000000            1.000000            1.000000   \n",
       "\n",
       "   split3_test_recall  split4_test_recall  mean_test_recall  std_test_recall  \\\n",
       "1            0.000000            0.000000          0.000000         0.000000   \n",
       "6            0.000000            0.000000          0.000000         0.000000   \n",
       "3            0.428571            0.619048          0.564069         0.074398   \n",
       "8            0.428571            0.619048          0.564069         0.074398   \n",
       "0            0.000000            0.000000          0.000000         0.000000   \n",
       "4            0.000000            0.000000          0.000000         0.000000   \n",
       "5            0.000000            0.000000          0.000000         0.000000   \n",
       "2            0.190476            0.095238          0.157143         0.036312   \n",
       "7            0.190476            0.095238          0.157143         0.036312   \n",
       "9            1.000000            1.000000          1.000000         0.000000   \n",
       "\n",
       "   rank_test_recall  split0_test_roc_auc  split1_test_roc_auc  \\\n",
       "1                 6             0.500000             0.500000   \n",
       "6                 6             0.500000             0.500000   \n",
       "3                 2             0.500000             0.500000   \n",
       "8                 2             0.500000             0.500000   \n",
       "0                 6             0.500000             0.500000   \n",
       "4                 6             0.500000             0.500000   \n",
       "5                 6             0.500000             0.500000   \n",
       "2                 4             0.484848             0.512478   \n",
       "7                 4             0.484848             0.512478   \n",
       "9                 1             0.500000             0.500000   \n",
       "\n",
       "   split2_test_roc_auc  split3_test_roc_auc  split4_test_roc_auc  \\\n",
       "1             0.500000             0.500000             0.500000   \n",
       "6             0.500000             0.500000             0.500000   \n",
       "3             0.500000             0.500000             0.500000   \n",
       "8             0.500000             0.500000             0.500000   \n",
       "0             0.500000             0.500000             0.500000   \n",
       "4             0.500000             0.500000             0.500000   \n",
       "5             0.500000             0.500000             0.500000   \n",
       "2             0.511701             0.516807             0.459384   \n",
       "7             0.511701             0.516807             0.459384   \n",
       "9             0.500000             0.500000             0.500000   \n",
       "\n",
       "   mean_test_roc_auc  std_test_roc_auc  rank_test_roc_auc  \n",
       "1           0.500000          0.000000                  1  \n",
       "6           0.500000          0.000000                  1  \n",
       "3           0.500000          0.000000                  1  \n",
       "8           0.500000          0.000000                  1  \n",
       "0           0.500000          0.000000                  1  \n",
       "4           0.500000          0.000000                  1  \n",
       "5           0.500000          0.000000                  1  \n",
       "2           0.497044          0.021957                  9  \n",
       "7           0.497044          0.021957                  9  \n",
       "9           0.500000          0.000000                  1  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(grid.cv_results_).sort_values(by='rank_test_neg_log_loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c2f8e37e-ee59-434f-99f6-dae2026fbf97",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('test.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7c29661c-280e-43b5-b63d-aa51d508e588",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 56)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ceb10744-137c-4874-aeba-6b922fcdac86",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = grid.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b9b0c55c-3b53-4a43-993c-2dcd44efd375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55920336-6f45-4c55-adc3-f01b05122efd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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