3_gridsearch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "1_1EIF_zc_IX"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Basics - Data Handling\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# sklearn Classification and Model Building\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB, BernoulliNB\n",
    "from sklearn.linear_model import SGDClassifier, LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.ensemble import StackingClassifier\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Cross Validation\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Scoring functions\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Plotting\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Score1</th>\n",
       "      <th>Score3</th>\n",
       "      <th>Score4</th>\n",
       "      <th>Score5</th>\n",
       "      <th>Loan type_1.0</th>\n",
       "      <th>Occupation type_1.0</th>\n",
       "      <th>Occupation type_2.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16167.84409</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.272247</td>\n",
       "      <td>7.793966</td>\n",
       "      <td>602.632122</td>\n",
       "      <td>3396.474235</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15772.85155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.203328</td>\n",
       "      <td>12.165225</td>\n",
       "      <td>600.397183</td>\n",
       "      <td>3476.857709</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Income  Age    Score1     Score3      Score4       Score5  \\\n",
       "0  16167.84409  1.0  0.272247   7.793966  602.632122  3396.474235   \n",
       "1  15772.85155  0.0  0.203328  12.165225  600.397183  3476.857709   \n",
       "\n",
       "   Loan type_1.0  Occupation type_1.0  Occupation type_2.0  \n",
       "0              0                    1                    0  \n",
       "1              0                    1                    0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = pd.read_csv(\"datasets/train_smote.csv\", index_col=0)\n",
    "y_train = X_train[\"y\"]\n",
    "X_train.drop([\"y\"], axis=1, inplace=True)\n",
    "X_train.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid Search with 5-fold CV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [MLP](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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_early_stopping</th>\n",
       "      <th>param_hidden_layer_sizes</th>\n",
       "      <th>param_learning_rate</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.093337</td>\n",
       "      <td>5.735616</td>\n",
       "      <td>0.049536</td>\n",
       "      <td>0.004857</td>\n",
       "      <td>True</td>\n",
       "      <td>(50,)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'early_stopping': True, 'hidden_layer_sizes':...</td>\n",
       "      <td>0.832552</td>\n",
       "      <td>0.789568</td>\n",
       "      <td>0.851907</td>\n",
       "      <td>0.825591</td>\n",
       "      <td>0.857009</td>\n",
       "      <td>0.831325</td>\n",
       "      <td>0.023922</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.920570</td>\n",
       "      <td>8.781274</td>\n",
       "      <td>0.051829</td>\n",
       "      <td>0.005084</td>\n",
       "      <td>True</td>\n",
       "      <td>(50,)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'early_stopping': True, 'hidden_layer_sizes':...</td>\n",
       "      <td>0.832552</td>\n",
       "      <td>0.789568</td>\n",
       "      <td>0.851907</td>\n",
       "      <td>0.825591</td>\n",
       "      <td>0.857009</td>\n",
       "      <td>0.831325</td>\n",
       "      <td>0.023922</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43.653032</td>\n",
       "      <td>16.353532</td>\n",
       "      <td>0.069954</td>\n",
       "      <td>0.009473</td>\n",
       "      <td>True</td>\n",
       "      <td>(100,)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'early_stopping': True, 'hidden_layer_sizes':...</td>\n",
       "      <td>0.820088</td>\n",
       "      <td>0.856799</td>\n",
       "      <td>0.858260</td>\n",
       "      <td>0.873014</td>\n",
       "      <td>0.850077</td>\n",
       "      <td>0.851648</td>\n",
       "      <td>0.017466</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.945214</td>\n",
       "      <td>14.203473</td>\n",
       "      <td>0.063179</td>\n",
       "      <td>0.010767</td>\n",
       "      <td>True</td>\n",
       "      <td>(100,)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'early_stopping': True, 'hidden_layer_sizes':...</td>\n",
       "      <td>0.820088</td>\n",
       "      <td>0.856799</td>\n",
       "      <td>0.858260</td>\n",
       "      <td>0.873014</td>\n",
       "      <td>0.850077</td>\n",
       "      <td>0.851648</td>\n",
       "      <td>0.017466</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.005682</td>\n",
       "      <td>9.492491</td>\n",
       "      <td>0.107681</td>\n",
       "      <td>0.002959</td>\n",
       "      <td>True</td>\n",
       "      <td>(200,)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'early_stopping': True, 'hidden_layer_sizes':...</td>\n",
       "      <td>0.831477</td>\n",
       "      <td>0.636774</td>\n",
       "      <td>0.841750</td>\n",
       "      <td>0.859928</td>\n",
       "      <td>0.824734</td>\n",
       "      <td>0.798933</td>\n",
       "      <td>0.081944</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>29.607980</td>\n",
       "      <td>9.527550</td>\n",
       "      <td>0.111407</td>\n",
       "      <td>0.006404</td>\n",
       "      <td>True</td>\n",
       "      <td>(200,)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'early_stopping': True, 'hidden_layer_sizes':...</td>\n",
       "      <td>0.831477</td>\n",
       "      <td>0.636774</td>\n",
       "      <td>0.841750</td>\n",
       "      <td>0.859928</td>\n",
       "      <td>0.824734</td>\n",
       "      <td>0.798933</td>\n",
       "      <td>0.081944</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13.228956</td>\n",
       "      <td>1.824432</td>\n",
       "      <td>0.042398</td>\n",
       "      <td>0.006118</td>\n",
       "      <td>False</td>\n",
       "      <td>(50,)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'early_stopping': False, 'hidden_layer_sizes'...</td>\n",
       "      <td>0.713055</td>\n",
       "      <td>0.766980</td>\n",
       "      <td>0.625554</td>\n",
       "      <td>0.741955</td>\n",
       "      <td>0.733870</td>\n",
       "      <td>0.716283</td>\n",
       "      <td>0.048540</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13.222710</td>\n",
       "      <td>1.778445</td>\n",
       "      <td>0.037929</td>\n",
       "      <td>0.000504</td>\n",
       "      <td>False</td>\n",
       "      <td>(50,)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'early_stopping': False, 'hidden_layer_sizes'...</td>\n",
       "      <td>0.713055</td>\n",
       "      <td>0.766980</td>\n",
       "      <td>0.625554</td>\n",
       "      <td>0.741955</td>\n",
       "      <td>0.733870</td>\n",
       "      <td>0.716283</td>\n",
       "      <td>0.048540</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36.357850</td>\n",
       "      <td>15.153926</td>\n",
       "      <td>0.076251</td>\n",
       "      <td>0.006788</td>\n",
       "      <td>False</td>\n",
       "      <td>(100,)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'early_stopping': False, 'hidden_layer_sizes'...</td>\n",
       "      <td>0.405939</td>\n",
       "      <td>0.805941</td>\n",
       "      <td>0.852789</td>\n",
       "      <td>0.726747</td>\n",
       "      <td>0.203716</td>\n",
       "      <td>0.599026</td>\n",
       "      <td>0.251823</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>36.795651</td>\n",
       "      <td>15.463835</td>\n",
       "      <td>0.079245</td>\n",
       "      <td>0.009053</td>\n",
       "      <td>False</td>\n",
       "      <td>(100,)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'early_stopping': False, 'hidden_layer_sizes'...</td>\n",
       "      <td>0.405939</td>\n",
       "      <td>0.805941</td>\n",
       "      <td>0.852789</td>\n",
       "      <td>0.726747</td>\n",
       "      <td>0.203716</td>\n",
       "      <td>0.599026</td>\n",
       "      <td>0.251823</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>34.368760</td>\n",
       "      <td>7.930855</td>\n",
       "      <td>0.132708</td>\n",
       "      <td>0.011801</td>\n",
       "      <td>False</td>\n",
       "      <td>(200,)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'early_stopping': False, 'hidden_layer_sizes'...</td>\n",
       "      <td>0.421882</td>\n",
       "      <td>0.663352</td>\n",
       "      <td>0.850109</td>\n",
       "      <td>0.555763</td>\n",
       "      <td>0.682499</td>\n",
       "      <td>0.634721</td>\n",
       "      <td>0.142148</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>34.341198</td>\n",
       "      <td>8.014685</td>\n",
       "      <td>0.137053</td>\n",
       "      <td>0.006040</td>\n",
       "      <td>False</td>\n",
       "      <td>(200,)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'early_stopping': False, 'hidden_layer_sizes'...</td>\n",
       "      <td>0.421882</td>\n",
       "      <td>0.663352</td>\n",
       "      <td>0.850109</td>\n",
       "      <td>0.555763</td>\n",
       "      <td>0.682499</td>\n",
       "      <td>0.634721</td>\n",
       "      <td>0.142148</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       18.093337      5.735616         0.049536        0.004857   \n",
       "1       22.920570      8.781274         0.051829        0.005084   \n",
       "2       43.653032     16.353532         0.069954        0.009473   \n",
       "3       37.945214     14.203473         0.063179        0.010767   \n",
       "4       30.005682      9.492491         0.107681        0.002959   \n",
       "5       29.607980      9.527550         0.111407        0.006404   \n",
       "6       13.228956      1.824432         0.042398        0.006118   \n",
       "7       13.222710      1.778445         0.037929        0.000504   \n",
       "8       36.357850     15.153926         0.076251        0.006788   \n",
       "9       36.795651     15.463835         0.079245        0.009053   \n",
       "10      34.368760      7.930855         0.132708        0.011801   \n",
       "11      34.341198      8.014685         0.137053        0.006040   \n",
       "\n",
       "   param_early_stopping param_hidden_layer_sizes param_learning_rate  \\\n",
       "0                  True                    (50,)            constant   \n",
       "1                  True                    (50,)            adaptive   \n",
       "2                  True                   (100,)            constant   \n",
       "3                  True                   (100,)            adaptive   \n",
       "4                  True                   (200,)            constant   \n",
       "5                  True                   (200,)            adaptive   \n",
       "6                 False                    (50,)            constant   \n",
       "7                 False                    (50,)            adaptive   \n",
       "8                 False                   (100,)            constant   \n",
       "9                 False                   (100,)            adaptive   \n",
       "10                False                   (200,)            constant   \n",
       "11                False                   (200,)            adaptive   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0   {'early_stopping': True, 'hidden_layer_sizes':...           0.832552   \n",
       "1   {'early_stopping': True, 'hidden_layer_sizes':...           0.832552   \n",
       "2   {'early_stopping': True, 'hidden_layer_sizes':...           0.820088   \n",
       "3   {'early_stopping': True, 'hidden_layer_sizes':...           0.820088   \n",
       "4   {'early_stopping': True, 'hidden_layer_sizes':...           0.831477   \n",
       "5   {'early_stopping': True, 'hidden_layer_sizes':...           0.831477   \n",
       "6   {'early_stopping': False, 'hidden_layer_sizes'...           0.713055   \n",
       "7   {'early_stopping': False, 'hidden_layer_sizes'...           0.713055   \n",
       "8   {'early_stopping': False, 'hidden_layer_sizes'...           0.405939   \n",
       "9   {'early_stopping': False, 'hidden_layer_sizes'...           0.405939   \n",
       "10  {'early_stopping': False, 'hidden_layer_sizes'...           0.421882   \n",
       "11  {'early_stopping': False, 'hidden_layer_sizes'...           0.421882   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0            0.789568           0.851907           0.825591   \n",
       "1            0.789568           0.851907           0.825591   \n",
       "2            0.856799           0.858260           0.873014   \n",
       "3            0.856799           0.858260           0.873014   \n",
       "4            0.636774           0.841750           0.859928   \n",
       "5            0.636774           0.841750           0.859928   \n",
       "6            0.766980           0.625554           0.741955   \n",
       "7            0.766980           0.625554           0.741955   \n",
       "8            0.805941           0.852789           0.726747   \n",
       "9            0.805941           0.852789           0.726747   \n",
       "10           0.663352           0.850109           0.555763   \n",
       "11           0.663352           0.850109           0.555763   \n",
       "\n",
       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0            0.857009         0.831325        0.023922                3  \n",
       "1            0.857009         0.831325        0.023922                3  \n",
       "2            0.850077         0.851648        0.017466                1  \n",
       "3            0.850077         0.851648        0.017466                1  \n",
       "4            0.824734         0.798933        0.081944                5  \n",
       "5            0.824734         0.798933        0.081944                5  \n",
       "6            0.733870         0.716283        0.048540                7  \n",
       "7            0.733870         0.716283        0.048540                7  \n",
       "8            0.203716         0.599026        0.251823               11  \n",
       "9            0.203716         0.599026        0.251823               11  \n",
       "10           0.682499         0.634721        0.142148                9  \n",
       "11           0.682499         0.634721        0.142148                9  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GridSearch for MLP\n",
    "parameters = {'hidden_layer_sizes':[(50,),(100,),(200,)], 'learning_rate':['constant', 'adaptive'], 'early_stopping':[True, False]}\n",
    "model = MLPClassifier(random_state=42)\n",
    "\n",
    "clf = GridSearchCV(model, parameters, scoring=\"f1\")\n",
    "clf.fit(X_train, y_train)\n",
    "df = pd.DataFrame(clf.cv_results_)\n",
    "df.to_csv(\"parameter_search/mlp_param_tuning.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLP with Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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_mlp__early_stopping</th>\n",
       "      <th>param_mlp__hidden_layer_sizes</th>\n",
       "      <th>param_mlp__learning_rate</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52.369903</td>\n",
       "      <td>6.624602</td>\n",
       "      <td>0.054490</td>\n",
       "      <td>0.009572</td>\n",
       "      <td>True</td>\n",
       "      <td>(50,)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'mlp__early_stopping': True, 'mlp__hidden_lay...</td>\n",
       "      <td>0.972710</td>\n",
       "      <td>0.983026</td>\n",
       "      <td>0.985501</td>\n",
       "      <td>0.985061</td>\n",
       "      <td>0.980489</td>\n",
       "      <td>0.981358</td>\n",
       "      <td>0.004673</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57.753451</td>\n",
       "      <td>12.624178</td>\n",
       "      <td>0.047605</td>\n",
       "      <td>0.002271</td>\n",
       "      <td>True</td>\n",
       "      <td>(50,)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'mlp__early_stopping': True, 'mlp__hidden_lay...</td>\n",
       "      <td>0.972710</td>\n",
       "      <td>0.983026</td>\n",
       "      <td>0.985501</td>\n",
       "      <td>0.985061</td>\n",
       "      <td>0.980489</td>\n",
       "      <td>0.981358</td>\n",
       "      <td>0.004673</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60.988719</td>\n",
       "      <td>5.417109</td>\n",
       "      <td>0.078875</td>\n",
       "      <td>0.011582</td>\n",
       "      <td>True</td>\n",
       "      <td>(100,)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'mlp__early_stopping': True, 'mlp__hidden_lay...</td>\n",
       "      <td>0.975775</td>\n",
       "      <td>0.985116</td>\n",
       "      <td>0.986898</td>\n",
       "      <td>0.986977</td>\n",
       "      <td>0.985396</td>\n",
       "      <td>0.984032</td>\n",
       "      <td>0.004197</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63.919460</td>\n",
       "      <td>6.478846</td>\n",
       "      <td>0.073860</td>\n",
       "      <td>0.003954</td>\n",
       "      <td>True</td>\n",
       "      <td>(100,)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'mlp__early_stopping': True, 'mlp__hidden_lay...</td>\n",
       "      <td>0.975775</td>\n",
       "      <td>0.985116</td>\n",
       "      <td>0.986898</td>\n",
       "      <td>0.986977</td>\n",
       "      <td>0.985396</td>\n",
       "      <td>0.984032</td>\n",
       "      <td>0.004197</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>86.725071</td>\n",
       "      <td>25.329384</td>\n",
       "      <td>0.145424</td>\n",
       "      <td>0.011471</td>\n",
       "      <td>True</td>\n",
       "      <td>(200,)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'mlp__early_stopping': True, 'mlp__hidden_lay...</td>\n",
       "      <td>0.974061</td>\n",
       "      <td>0.987893</td>\n",
       "      <td>0.987389</td>\n",
       "      <td>0.986763</td>\n",
       "      <td>0.987176</td>\n",
       "      <td>0.984656</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>79.215362</td>\n",
       "      <td>20.555107</td>\n",
       "      <td>0.140862</td>\n",
       "      <td>0.003584</td>\n",
       "      <td>True</td>\n",
       "      <td>(200,)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'mlp__early_stopping': True, 'mlp__hidden_lay...</td>\n",
       "      <td>0.974061</td>\n",
       "      <td>0.987893</td>\n",
       "      <td>0.987389</td>\n",
       "      <td>0.986763</td>\n",
       "      <td>0.987176</td>\n",
       "      <td>0.984656</td>\n",
       "      <td>0.005310</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>137.501623</td>\n",
       "      <td>28.858772</td>\n",
       "      <td>0.048050</td>\n",
       "      <td>0.002899</td>\n",
       "      <td>False</td>\n",
       "      <td>(50,)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'mlp__early_stopping': False, 'mlp__hidden_la...</td>\n",
       "      <td>0.975899</td>\n",
       "      <td>0.988058</td>\n",
       "      <td>0.986808</td>\n",
       "      <td>0.988863</td>\n",
       "      <td>0.987790</td>\n",
       "      <td>0.985484</td>\n",
       "      <td>0.004837</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>169.347074</td>\n",
       "      <td>53.762756</td>\n",
       "      <td>0.049369</td>\n",
       "      <td>0.002669</td>\n",
       "      <td>False</td>\n",
       "      <td>(50,)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'mlp__early_stopping': False, 'mlp__hidden_la...</td>\n",
       "      <td>0.975899</td>\n",
       "      <td>0.988058</td>\n",
       "      <td>0.986808</td>\n",
       "      <td>0.988863</td>\n",
       "      <td>0.987790</td>\n",
       "      <td>0.985484</td>\n",
       "      <td>0.004837</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>266.072091</td>\n",
       "      <td>9.710005</td>\n",
       "      <td>0.108721</td>\n",
       "      <td>0.027207</td>\n",
       "      <td>False</td>\n",
       "      <td>(100,)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'mlp__early_stopping': False, 'mlp__hidden_la...</td>\n",
       "      <td>0.979153</td>\n",
       "      <td>0.987858</td>\n",
       "      <td>0.990039</td>\n",
       "      <td>0.990924</td>\n",
       "      <td>0.988958</td>\n",
       "      <td>0.987386</td>\n",
       "      <td>0.004244</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>175.656541</td>\n",
       "      <td>25.235027</td>\n",
       "      <td>0.085910</td>\n",
       "      <td>0.001518</td>\n",
       "      <td>False</td>\n",
       "      <td>(100,)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'mlp__early_stopping': False, 'mlp__hidden_la...</td>\n",
       "      <td>0.979153</td>\n",
       "      <td>0.987858</td>\n",
       "      <td>0.990039</td>\n",
       "      <td>0.990924</td>\n",
       "      <td>0.988958</td>\n",
       "      <td>0.987386</td>\n",
       "      <td>0.004244</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>233.756847</td>\n",
       "      <td>45.620964</td>\n",
       "      <td>0.140837</td>\n",
       "      <td>0.001985</td>\n",
       "      <td>False</td>\n",
       "      <td>(200,)</td>\n",
       "      <td>constant</td>\n",
       "      <td>{'mlp__early_stopping': False, 'mlp__hidden_la...</td>\n",
       "      <td>0.977639</td>\n",
       "      <td>0.988753</td>\n",
       "      <td>0.990292</td>\n",
       "      <td>0.990605</td>\n",
       "      <td>0.989346</td>\n",
       "      <td>0.987327</td>\n",
       "      <td>0.004889</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>224.315160</td>\n",
       "      <td>29.022758</td>\n",
       "      <td>0.153030</td>\n",
       "      <td>0.023018</td>\n",
       "      <td>False</td>\n",
       "      <td>(200,)</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>{'mlp__early_stopping': False, 'mlp__hidden_la...</td>\n",
       "      <td>0.977639</td>\n",
       "      <td>0.988753</td>\n",
       "      <td>0.990292</td>\n",
       "      <td>0.990605</td>\n",
       "      <td>0.989346</td>\n",
       "      <td>0.987327</td>\n",
       "      <td>0.004889</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       52.369903      6.624602         0.054490        0.009572   \n",
       "1       57.753451     12.624178         0.047605        0.002271   \n",
       "2       60.988719      5.417109         0.078875        0.011582   \n",
       "3       63.919460      6.478846         0.073860        0.003954   \n",
       "4       86.725071     25.329384         0.145424        0.011471   \n",
       "5       79.215362     20.555107         0.140862        0.003584   \n",
       "6      137.501623     28.858772         0.048050        0.002899   \n",
       "7      169.347074     53.762756         0.049369        0.002669   \n",
       "8      266.072091      9.710005         0.108721        0.027207   \n",
       "9      175.656541     25.235027         0.085910        0.001518   \n",
       "10     233.756847     45.620964         0.140837        0.001985   \n",
       "11     224.315160     29.022758         0.153030        0.023018   \n",
       "\n",
       "   param_mlp__early_stopping param_mlp__hidden_layer_sizes  \\\n",
       "0                       True                         (50,)   \n",
       "1                       True                         (50,)   \n",
       "2                       True                        (100,)   \n",
       "3                       True                        (100,)   \n",
       "4                       True                        (200,)   \n",
       "5                       True                        (200,)   \n",
       "6                      False                         (50,)   \n",
       "7                      False                         (50,)   \n",
       "8                      False                        (100,)   \n",
       "9                      False                        (100,)   \n",
       "10                     False                        (200,)   \n",
       "11                     False                        (200,)   \n",
       "\n",
       "   param_mlp__learning_rate  \\\n",
       "0                  constant   \n",
       "1                  adaptive   \n",
       "2                  constant   \n",
       "3                  adaptive   \n",
       "4                  constant   \n",
       "5                  adaptive   \n",
       "6                  constant   \n",
       "7                  adaptive   \n",
       "8                  constant   \n",
       "9                  adaptive   \n",
       "10                 constant   \n",
       "11                 adaptive   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0   {'mlp__early_stopping': True, 'mlp__hidden_lay...           0.972710   \n",
       "1   {'mlp__early_stopping': True, 'mlp__hidden_lay...           0.972710   \n",
       "2   {'mlp__early_stopping': True, 'mlp__hidden_lay...           0.975775   \n",
       "3   {'mlp__early_stopping': True, 'mlp__hidden_lay...           0.975775   \n",
       "4   {'mlp__early_stopping': True, 'mlp__hidden_lay...           0.974061   \n",
       "5   {'mlp__early_stopping': True, 'mlp__hidden_lay...           0.974061   \n",
       "6   {'mlp__early_stopping': False, 'mlp__hidden_la...           0.975899   \n",
       "7   {'mlp__early_stopping': False, 'mlp__hidden_la...           0.975899   \n",
       "8   {'mlp__early_stopping': False, 'mlp__hidden_la...           0.979153   \n",
       "9   {'mlp__early_stopping': False, 'mlp__hidden_la...           0.979153   \n",
       "10  {'mlp__early_stopping': False, 'mlp__hidden_la...           0.977639   \n",
       "11  {'mlp__early_stopping': False, 'mlp__hidden_la...           0.977639   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0            0.983026           0.985501           0.985061   \n",
       "1            0.983026           0.985501           0.985061   \n",
       "2            0.985116           0.986898           0.986977   \n",
       "3            0.985116           0.986898           0.986977   \n",
       "4            0.987893           0.987389           0.986763   \n",
       "5            0.987893           0.987389           0.986763   \n",
       "6            0.988058           0.986808           0.988863   \n",
       "7            0.988058           0.986808           0.988863   \n",
       "8            0.987858           0.990039           0.990924   \n",
       "9            0.987858           0.990039           0.990924   \n",
       "10           0.988753           0.990292           0.990605   \n",
       "11           0.988753           0.990292           0.990605   \n",
       "\n",
       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0            0.980489         0.981358        0.004673               11  \n",
       "1            0.980489         0.981358        0.004673               11  \n",
       "2            0.985396         0.984032        0.004197                9  \n",
       "3            0.985396         0.984032        0.004197                9  \n",
       "4            0.987176         0.984656        0.005310                7  \n",
       "5            0.987176         0.984656        0.005310                7  \n",
       "6            0.987790         0.985484        0.004837                5  \n",
       "7            0.987790         0.985484        0.004837                5  \n",
       "8            0.988958         0.987386        0.004244                1  \n",
       "9            0.988958         0.987386        0.004244                1  \n",
       "10           0.989346         0.987327        0.004889                3  \n",
       "11           0.989346         0.987327        0.004889                3  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GridSearch for MLP, Standard Scaling\n",
    "parameters = {'mlp__hidden_layer_sizes':[(50,),(100,),(200,)], 'mlp__learning_rate':['constant', 'adaptive'], 'mlp__early_stopping':[True, False]}\n",
    "model = Pipeline([('scaler', StandardScaler()), ('mlp', MLPClassifier(max_iter=1000, random_state=42))])\n",
    "\n",
    "clf = GridSearchCV(model, parameters, scoring=\"f1\")\n",
    "clf.fit(X_train, y_train)\n",
    "df = pd.DataFrame(clf.cv_results_)\n",
    "df.to_csv(\"parameter_search/mlp_stdscaler_param_tuning.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Random Forest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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_max_depth</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.770911</td>\n",
       "      <td>0.357813</td>\n",
       "      <td>0.311308</td>\n",
       "      <td>0.003226</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>{'max_depth': None, 'min_samples_split': 2}</td>\n",
       "      <td>0.975983</td>\n",
       "      <td>0.988676</td>\n",
       "      <td>0.988892</td>\n",
       "      <td>0.989162</td>\n",
       "      <td>0.988609</td>\n",
       "      <td>0.986264</td>\n",
       "      <td>0.005145</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.852494</td>\n",
       "      <td>0.261460</td>\n",
       "      <td>0.308165</td>\n",
       "      <td>0.003242</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>{'max_depth': None, 'min_samples_split': 3}</td>\n",
       "      <td>0.975347</td>\n",
       "      <td>0.988809</td>\n",
       "      <td>0.988802</td>\n",
       "      <td>0.988760</td>\n",
       "      <td>0.988251</td>\n",
       "      <td>0.985994</td>\n",
       "      <td>0.005327</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.493523</td>\n",
       "      <td>2.277524</td>\n",
       "      <td>0.409775</td>\n",
       "      <td>0.034156</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "      <td>{'max_depth': None, 'min_samples_split': 4}</td>\n",
       "      <td>0.976111</td>\n",
       "      <td>0.988894</td>\n",
       "      <td>0.988667</td>\n",
       "      <td>0.988625</td>\n",
       "      <td>0.987850</td>\n",
       "      <td>0.986029</td>\n",
       "      <td>0.004972</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.987833</td>\n",
       "      <td>2.349542</td>\n",
       "      <td>0.229841</td>\n",
       "      <td>0.039098</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>{'max_depth': 10, 'min_samples_split': 2}</td>\n",
       "      <td>0.959295</td>\n",
       "      <td>0.974543</td>\n",
       "      <td>0.975272</td>\n",
       "      <td>0.974315</td>\n",
       "      <td>0.973908</td>\n",
       "      <td>0.971467</td>\n",
       "      <td>0.006102</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.517749</td>\n",
       "      <td>0.077742</td>\n",
       "      <td>0.210858</td>\n",
       "      <td>0.003359</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>{'max_depth': 10, 'min_samples_split': 3}</td>\n",
       "      <td>0.959496</td>\n",
       "      <td>0.974679</td>\n",
       "      <td>0.975415</td>\n",
       "      <td>0.974550</td>\n",
       "      <td>0.974643</td>\n",
       "      <td>0.971757</td>\n",
       "      <td>0.006138</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.141402</td>\n",
       "      <td>0.891349</td>\n",
       "      <td>0.216969</td>\n",
       "      <td>0.011283</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>{'max_depth': 10, 'min_samples_split': 4}</td>\n",
       "      <td>0.959832</td>\n",
       "      <td>0.974861</td>\n",
       "      <td>0.975094</td>\n",
       "      <td>0.974763</td>\n",
       "      <td>0.975098</td>\n",
       "      <td>0.971930</td>\n",
       "      <td>0.006050</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.274458</td>\n",
       "      <td>0.365551</td>\n",
       "      <td>0.163696</td>\n",
       "      <td>0.002048</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>{'max_depth': 7, 'min_samples_split': 2}</td>\n",
       "      <td>0.940035</td>\n",
       "      <td>0.955387</td>\n",
       "      <td>0.956463</td>\n",
       "      <td>0.954504</td>\n",
       "      <td>0.954238</td>\n",
       "      <td>0.952125</td>\n",
       "      <td>0.006095</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.782028</td>\n",
       "      <td>0.427322</td>\n",
       "      <td>0.167660</td>\n",
       "      <td>0.005288</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>{'max_depth': 7, 'min_samples_split': 3}</td>\n",
       "      <td>0.940701</td>\n",
       "      <td>0.955787</td>\n",
       "      <td>0.956486</td>\n",
       "      <td>0.954350</td>\n",
       "      <td>0.954385</td>\n",
       "      <td>0.952342</td>\n",
       "      <td>0.005878</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.192439</td>\n",
       "      <td>0.106303</td>\n",
       "      <td>0.189130</td>\n",
       "      <td>0.051371</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>{'max_depth': 7, 'min_samples_split': 4}</td>\n",
       "      <td>0.941020</td>\n",
       "      <td>0.956231</td>\n",
       "      <td>0.956020</td>\n",
       "      <td>0.954360</td>\n",
       "      <td>0.954372</td>\n",
       "      <td>0.952401</td>\n",
       "      <td>0.005745</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.133165</td>\n",
       "      <td>1.776960</td>\n",
       "      <td>0.182980</td>\n",
       "      <td>0.053684</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>{'max_depth': 5, 'min_samples_split': 2}</td>\n",
       "      <td>0.912865</td>\n",
       "      <td>0.934616</td>\n",
       "      <td>0.939679</td>\n",
       "      <td>0.933382</td>\n",
       "      <td>0.935230</td>\n",
       "      <td>0.931154</td>\n",
       "      <td>0.009389</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.336718</td>\n",
       "      <td>0.463929</td>\n",
       "      <td>0.152668</td>\n",
       "      <td>0.010114</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>{'max_depth': 5, 'min_samples_split': 3}</td>\n",
       "      <td>0.912428</td>\n",
       "      <td>0.934616</td>\n",
       "      <td>0.939679</td>\n",
       "      <td>0.933382</td>\n",
       "      <td>0.935230</td>\n",
       "      <td>0.931067</td>\n",
       "      <td>0.009559</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9.004543</td>\n",
       "      <td>0.883870</td>\n",
       "      <td>0.147495</td>\n",
       "      <td>0.014524</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>{'max_depth': 5, 'min_samples_split': 4}</td>\n",
       "      <td>0.912428</td>\n",
       "      <td>0.934677</td>\n",
       "      <td>0.939679</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.935230</td>\n",
       "      <td>0.931070</td>\n",
       "      <td>0.009561</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       15.770911      0.357813         0.311308        0.003226   \n",
       "1       15.852494      0.261460         0.308165        0.003242   \n",
       "2       20.493523      2.277524         0.409775        0.034156   \n",
       "3       12.987833      2.349542         0.229841        0.039098   \n",
       "4       11.517749      0.077742         0.210858        0.003359   \n",
       "5       12.141402      0.891349         0.216969        0.011283   \n",
       "6        9.274458      0.365551         0.163696        0.002048   \n",
       "7        9.782028      0.427322         0.167660        0.005288   \n",
       "8        9.192439      0.106303         0.189130        0.051371   \n",
       "9        9.133165      1.776960         0.182980        0.053684   \n",
       "10       8.336718      0.463929         0.152668        0.010114   \n",
       "11       9.004543      0.883870         0.147495        0.014524   \n",
       "\n",
       "   param_max_depth param_min_samples_split  \\\n",
       "0             None                       2   \n",
       "1             None                       3   \n",
       "2             None                       4   \n",
       "3               10                       2   \n",
       "4               10                       3   \n",
       "5               10                       4   \n",
       "6                7                       2   \n",
       "7                7                       3   \n",
       "8                7                       4   \n",
       "9                5                       2   \n",
       "10               5                       3   \n",
       "11               5                       4   \n",
       "\n",
       "                                         params  split0_test_score  \\\n",
       "0   {'max_depth': None, 'min_samples_split': 2}           0.975983   \n",
       "1   {'max_depth': None, 'min_samples_split': 3}           0.975347   \n",
       "2   {'max_depth': None, 'min_samples_split': 4}           0.976111   \n",
       "3     {'max_depth': 10, 'min_samples_split': 2}           0.959295   \n",
       "4     {'max_depth': 10, 'min_samples_split': 3}           0.959496   \n",
       "5     {'max_depth': 10, 'min_samples_split': 4}           0.959832   \n",
       "6      {'max_depth': 7, 'min_samples_split': 2}           0.940035   \n",
       "7      {'max_depth': 7, 'min_samples_split': 3}           0.940701   \n",
       "8      {'max_depth': 7, 'min_samples_split': 4}           0.941020   \n",
       "9      {'max_depth': 5, 'min_samples_split': 2}           0.912865   \n",
       "10     {'max_depth': 5, 'min_samples_split': 3}           0.912428   \n",
       "11     {'max_depth': 5, 'min_samples_split': 4}           0.912428   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0            0.988676           0.988892           0.989162   \n",
       "1            0.988809           0.988802           0.988760   \n",
       "2            0.988894           0.988667           0.988625   \n",
       "3            0.974543           0.975272           0.974315   \n",
       "4            0.974679           0.975415           0.974550   \n",
       "5            0.974861           0.975094           0.974763   \n",
       "6            0.955387           0.956463           0.954504   \n",
       "7            0.955787           0.956486           0.954350   \n",
       "8            0.956231           0.956020           0.954360   \n",
       "9            0.934616           0.939679           0.933382   \n",
       "10           0.934616           0.939679           0.933382   \n",
       "11           0.934677           0.939679           0.933333   \n",
       "\n",
       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0            0.988609         0.986264        0.005145                1  \n",
       "1            0.988251         0.985994        0.005327                3  \n",
       "2            0.987850         0.986029        0.004972                2  \n",
       "3            0.973908         0.971467        0.006102                6  \n",
       "4            0.974643         0.971757        0.006138                5  \n",
       "5            0.975098         0.971930        0.006050                4  \n",
       "6            0.954238         0.952125        0.006095                9  \n",
       "7            0.954385         0.952342        0.005878                8  \n",
       "8            0.954372         0.952401        0.005745                7  \n",
       "9            0.935230         0.931154        0.009389               10  \n",
       "10           0.935230         0.931067        0.009559               12  \n",
       "11           0.935230         0.931070        0.009561               11  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GridSearch for Random Forest\n",
    "parameters = {'max_depth':[None, 10, 7, 5], 'min_samples_split':[2, 3, 4]}\n",
    "model = RandomForestClassifier(random_state=42)\n",
    "\n",
    "clf = GridSearchCV(model, parameters, scoring=\"f1\")\n",
    "clf.fit(X_train, y_train)\n",
    "df = pd.DataFrame(clf.cv_results_)\n",
    "df.to_csv(\"parameter_search/rf_param_tuning.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [AdaBoost](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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_algorithm</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.827998</td>\n",
       "      <td>0.704317</td>\n",
       "      <td>0.052048</td>\n",
       "      <td>0.010975</td>\n",
       "      <td>SAMME</td>\n",
       "      <td>{'algorithm': 'SAMME'}</td>\n",
       "      <td>0.882570</td>\n",
       "      <td>0.913960</td>\n",
       "      <td>0.918008</td>\n",
       "      <td>0.917557</td>\n",
       "      <td>0.922554</td>\n",
       "      <td>0.910930</td>\n",
       "      <td>0.01444</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.123478</td>\n",
       "      <td>0.016647</td>\n",
       "      <td>0.088691</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>SAMME.R</td>\n",
       "      <td>{'algorithm': 'SAMME.R'}</td>\n",
       "      <td>0.905616</td>\n",
       "      <td>0.931657</td>\n",
       "      <td>0.932688</td>\n",
       "      <td>0.931048</td>\n",
       "      <td>0.932133</td>\n",
       "      <td>0.926629</td>\n",
       "      <td>0.01052</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",
       "0       4.827998      0.704317         0.052048        0.010975   \n",
       "1       4.123478      0.016647         0.088691        0.000982   \n",
       "\n",
       "  param_algorithm                    params  split0_test_score  \\\n",
       "0           SAMME    {'algorithm': 'SAMME'}           0.882570   \n",
       "1         SAMME.R  {'algorithm': 'SAMME.R'}           0.905616   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0           0.913960           0.918008           0.917557           0.922554   \n",
       "1           0.931657           0.932688           0.931048           0.932133   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  \n",
       "0         0.910930         0.01444                2  \n",
       "1         0.926629         0.01052                1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GridSearch for AdaBoost\n",
    "parameters = {'algorithm':['SAMME', 'SAMME.R']}\n",
    "model = AdaBoostClassifier(random_state=42)\n",
    "\n",
    "clf = GridSearchCV(model, parameters, scoring=\"f1\")\n",
    "clf.fit(X_train, y_train)\n",
    "df = pd.DataFrame(clf.cv_results_)\n",
    "df.to_csv(\"parameter_search/adaboost_param_tuning.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BmeF4nFfip6H"
   },
   "source": [
    "## [KNN](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 264
    },
    "id": "pLQt-lU6c_Iv",
    "outputId": "c6d97b1c-2532-4651-ba14-b934fc607ec1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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_n_neighbors</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.175926</td>\n",
       "      <td>0.022018</td>\n",
       "      <td>0.637395</td>\n",
       "      <td>0.046223</td>\n",
       "      <td>3</td>\n",
       "      <td>{'n_neighbors': 3}</td>\n",
       "      <td>0.947545</td>\n",
       "      <td>0.945629</td>\n",
       "      <td>0.945792</td>\n",
       "      <td>0.943784</td>\n",
       "      <td>0.946859</td>\n",
       "      <td>0.945922</td>\n",
       "      <td>0.001280</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.171477</td>\n",
       "      <td>0.013540</td>\n",
       "      <td>0.667038</td>\n",
       "      <td>0.051468</td>\n",
       "      <td>5</td>\n",
       "      <td>{'n_neighbors': 5}</td>\n",
       "      <td>0.939427</td>\n",
       "      <td>0.936598</td>\n",
       "      <td>0.938453</td>\n",
       "      <td>0.936732</td>\n",
       "      <td>0.938970</td>\n",
       "      <td>0.938036</td>\n",
       "      <td>0.001162</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.172241</td>\n",
       "      <td>0.008957</td>\n",
       "      <td>0.717805</td>\n",
       "      <td>0.016657</td>\n",
       "      <td>10</td>\n",
       "      <td>{'n_neighbors': 10}</td>\n",
       "      <td>0.927730</td>\n",
       "      <td>0.926166</td>\n",
       "      <td>0.927975</td>\n",
       "      <td>0.925185</td>\n",
       "      <td>0.927740</td>\n",
       "      <td>0.926959</td>\n",
       "      <td>0.001096</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.178850</td>\n",
       "      <td>0.009006</td>\n",
       "      <td>0.786475</td>\n",
       "      <td>0.043938</td>\n",
       "      <td>15</td>\n",
       "      <td>{'n_neighbors': 15}</td>\n",
       "      <td>0.912225</td>\n",
       "      <td>0.910997</td>\n",
       "      <td>0.913341</td>\n",
       "      <td>0.910596</td>\n",
       "      <td>0.912829</td>\n",
       "      <td>0.911998</td>\n",
       "      <td>0.001050</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.175926      0.022018         0.637395        0.046223   \n",
       "1       0.171477      0.013540         0.667038        0.051468   \n",
       "2       0.172241      0.008957         0.717805        0.016657   \n",
       "3       0.178850      0.009006         0.786475        0.043938   \n",
       "\n",
       "  param_n_neighbors               params  split0_test_score  \\\n",
       "0                 3   {'n_neighbors': 3}           0.947545   \n",
       "1                 5   {'n_neighbors': 5}           0.939427   \n",
       "2                10  {'n_neighbors': 10}           0.927730   \n",
       "3                15  {'n_neighbors': 15}           0.912225   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0           0.945629           0.945792           0.943784           0.946859   \n",
       "1           0.936598           0.938453           0.936732           0.938970   \n",
       "2           0.926166           0.927975           0.925185           0.927740   \n",
       "3           0.910997           0.913341           0.910596           0.912829   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  \n",
       "0         0.945922        0.001280                1  \n",
       "1         0.938036        0.001162                2  \n",
       "2         0.926959        0.001096                3  \n",
       "3         0.911998        0.001050                4  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GridSearch for KNN\n",
    "parameters = {'n_neighbors':[3, 5, 10, 15]}\n",
    "model = KNeighborsClassifier()\n",
    "\n",
    "clf = GridSearchCV(model, parameters, scoring=\"f1\")\n",
    "clf.fit(X_train, y_train)\n",
    "df = pd.DataFrame(clf.cv_results_)\n",
    "df.to_csv(\"parameter_search/knn_param_tuning.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [KNN with Pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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_knn__n_neighbors</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.171467</td>\n",
       "      <td>0.016049</td>\n",
       "      <td>1.257305</td>\n",
       "      <td>0.077728</td>\n",
       "      <td>3</td>\n",
       "      <td>{'knn__n_neighbors': 3}</td>\n",
       "      <td>0.977014</td>\n",
       "      <td>0.983173</td>\n",
       "      <td>0.985323</td>\n",
       "      <td>0.985242</td>\n",
       "      <td>0.985117</td>\n",
       "      <td>0.983174</td>\n",
       "      <td>0.003182</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.154278</td>\n",
       "      <td>0.003218</td>\n",
       "      <td>1.319354</td>\n",
       "      <td>0.035646</td>\n",
       "      <td>5</td>\n",
       "      <td>{'knn__n_neighbors': 5}</td>\n",
       "      <td>0.974768</td>\n",
       "      <td>0.982987</td>\n",
       "      <td>0.983191</td>\n",
       "      <td>0.983519</td>\n",
       "      <td>0.982980</td>\n",
       "      <td>0.981489</td>\n",
       "      <td>0.003366</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.151419</td>\n",
       "      <td>0.000930</td>\n",
       "      <td>1.594745</td>\n",
       "      <td>0.008517</td>\n",
       "      <td>10</td>\n",
       "      <td>{'knn__n_neighbors': 10}</td>\n",
       "      <td>0.967971</td>\n",
       "      <td>0.980022</td>\n",
       "      <td>0.979872</td>\n",
       "      <td>0.980198</td>\n",
       "      <td>0.976977</td>\n",
       "      <td>0.977008</td>\n",
       "      <td>0.004672</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.151147</td>\n",
       "      <td>0.001258</td>\n",
       "      <td>1.808690</td>\n",
       "      <td>0.027968</td>\n",
       "      <td>15</td>\n",
       "      <td>{'knn__n_neighbors': 15}</td>\n",
       "      <td>0.966383</td>\n",
       "      <td>0.978157</td>\n",
       "      <td>0.977808</td>\n",
       "      <td>0.979008</td>\n",
       "      <td>0.975581</td>\n",
       "      <td>0.975387</td>\n",
       "      <td>0.004642</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.171467      0.016049         1.257305        0.077728   \n",
       "1       0.154278      0.003218         1.319354        0.035646   \n",
       "2       0.151419      0.000930         1.594745        0.008517   \n",
       "3       0.151147      0.001258         1.808690        0.027968   \n",
       "\n",
       "  param_knn__n_neighbors                    params  split0_test_score  \\\n",
       "0                      3   {'knn__n_neighbors': 3}           0.977014   \n",
       "1                      5   {'knn__n_neighbors': 5}           0.974768   \n",
       "2                     10  {'knn__n_neighbors': 10}           0.967971   \n",
       "3                     15  {'knn__n_neighbors': 15}           0.966383   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0           0.983173           0.985323           0.985242           0.985117   \n",
       "1           0.982987           0.983191           0.983519           0.982980   \n",
       "2           0.980022           0.979872           0.980198           0.976977   \n",
       "3           0.978157           0.977808           0.979008           0.975581   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  \n",
       "0         0.983174        0.003182                1  \n",
       "1         0.981489        0.003366                2  \n",
       "2         0.977008        0.004672                3  \n",
       "3         0.975387        0.004642                4  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GridSearch for KNN with StandardScaler\n",
    "parameters = {'knn__n_neighbors':[3, 5, 10, 15]}\n",
    "model = Pipeline([('scaler', StandardScaler()), ('knn', KNeighborsClassifier())])\n",
    "\n",
    "clf = GridSearchCV(model, parameters, scoring=\"f1\")\n",
    "clf.fit(X_train, y_train)\n",
    "df = pd.DataFrame(clf.cv_results_)\n",
    "df.to_csv(\"parameter_search/knn_stdscaler_param_tuning.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-SzwHCy8izks"
   },
   "source": [
    "## [DecisionTreeClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "LLKBdxh1fUNv",
    "outputId": "09aaab55-a9ff-42ce-f436-4c39691488af"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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_criterion</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_splitter</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.674387</td>\n",
       "      <td>0.017642</td>\n",
       "      <td>0.012455</td>\n",
       "      <td>0.000733</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': None, 'min_...</td>\n",
       "      <td>0.965157</td>\n",
       "      <td>0.975895</td>\n",
       "      <td>0.979059</td>\n",
       "      <td>0.978786</td>\n",
       "      <td>0.976370</td>\n",
       "      <td>0.975053</td>\n",
       "      <td>0.005106</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.069653</td>\n",
       "      <td>0.001871</td>\n",
       "      <td>0.012714</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': None, 'min_...</td>\n",
       "      <td>0.963093</td>\n",
       "      <td>0.976477</td>\n",
       "      <td>0.977161</td>\n",
       "      <td>0.978156</td>\n",
       "      <td>0.975789</td>\n",
       "      <td>0.974135</td>\n",
       "      <td>0.005576</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.659199</td>\n",
       "      <td>0.028124</td>\n",
       "      <td>0.012090</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': None, 'min_...</td>\n",
       "      <td>0.963649</td>\n",
       "      <td>0.975707</td>\n",
       "      <td>0.979181</td>\n",
       "      <td>0.979228</td>\n",
       "      <td>0.977090</td>\n",
       "      <td>0.974971</td>\n",
       "      <td>0.005815</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.067967</td>\n",
       "      <td>0.002614</td>\n",
       "      <td>0.012613</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': None, 'min_...</td>\n",
       "      <td>0.960673</td>\n",
       "      <td>0.976117</td>\n",
       "      <td>0.975082</td>\n",
       "      <td>0.976862</td>\n",
       "      <td>0.976452</td>\n",
       "      <td>0.973037</td>\n",
       "      <td>0.006210</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.660544</td>\n",
       "      <td>0.026469</td>\n",
       "      <td>0.012071</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': None, 'min_...</td>\n",
       "      <td>0.963686</td>\n",
       "      <td>0.975484</td>\n",
       "      <td>0.979182</td>\n",
       "      <td>0.978387</td>\n",
       "      <td>0.976920</td>\n",
       "      <td>0.974732</td>\n",
       "      <td>0.005666</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.067559</td>\n",
       "      <td>0.000988</td>\n",
       "      <td>0.012529</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': None, 'min_...</td>\n",
       "      <td>0.960471</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>0.977560</td>\n",
       "      <td>0.977194</td>\n",
       "      <td>0.975894</td>\n",
       "      <td>0.973744</td>\n",
       "      <td>0.006665</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.531295</td>\n",
       "      <td>0.010049</td>\n",
       "      <td>0.011379</td>\n",
       "      <td>0.000266</td>\n",
       "      <td>gini</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 10, 'min_sa...</td>\n",
       "      <td>0.950992</td>\n",
       "      <td>0.967870</td>\n",
       "      <td>0.969446</td>\n",
       "      <td>0.967008</td>\n",
       "      <td>0.968340</td>\n",
       "      <td>0.964731</td>\n",
       "      <td>0.006914</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.048389</td>\n",
       "      <td>0.000655</td>\n",
       "      <td>0.011712</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>gini</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 10, 'min_sa...</td>\n",
       "      <td>0.908957</td>\n",
       "      <td>0.924562</td>\n",
       "      <td>0.913287</td>\n",
       "      <td>0.932733</td>\n",
       "      <td>0.939999</td>\n",
       "      <td>0.923908</td>\n",
       "      <td>0.011607</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.533087</td>\n",
       "      <td>0.010285</td>\n",
       "      <td>0.011381</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>gini</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 10, 'min_sa...</td>\n",
       "      <td>0.951184</td>\n",
       "      <td>0.967951</td>\n",
       "      <td>0.969443</td>\n",
       "      <td>0.966956</td>\n",
       "      <td>0.968645</td>\n",
       "      <td>0.964836</td>\n",
       "      <td>0.006875</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.049336</td>\n",
       "      <td>0.001273</td>\n",
       "      <td>0.011708</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>gini</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 10, 'min_sa...</td>\n",
       "      <td>0.904183</td>\n",
       "      <td>0.905610</td>\n",
       "      <td>0.934201</td>\n",
       "      <td>0.926759</td>\n",
       "      <td>0.933489</td>\n",
       "      <td>0.920848</td>\n",
       "      <td>0.013289</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.534207</td>\n",
       "      <td>0.008250</td>\n",
       "      <td>0.011458</td>\n",
       "      <td>0.000388</td>\n",
       "      <td>gini</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 10, 'min_sa...</td>\n",
       "      <td>0.951188</td>\n",
       "      <td>0.968218</td>\n",
       "      <td>0.969397</td>\n",
       "      <td>0.967049</td>\n",
       "      <td>0.968462</td>\n",
       "      <td>0.964863</td>\n",
       "      <td>0.006878</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.049710</td>\n",
       "      <td>0.001464</td>\n",
       "      <td>0.011746</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>gini</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 10, 'min_sa...</td>\n",
       "      <td>0.914138</td>\n",
       "      <td>0.939293</td>\n",
       "      <td>0.933291</td>\n",
       "      <td>0.926194</td>\n",
       "      <td>0.937588</td>\n",
       "      <td>0.930101</td>\n",
       "      <td>0.009174</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.410795</td>\n",
       "      <td>0.001569</td>\n",
       "      <td>0.011086</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>gini</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 7, 'min_sam...</td>\n",
       "      <td>0.923521</td>\n",
       "      <td>0.938604</td>\n",
       "      <td>0.940278</td>\n",
       "      <td>0.935209</td>\n",
       "      <td>0.944904</td>\n",
       "      <td>0.936503</td>\n",
       "      <td>0.007203</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.040341</td>\n",
       "      <td>0.001309</td>\n",
       "      <td>0.011481</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>gini</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 7, 'min_sam...</td>\n",
       "      <td>0.867752</td>\n",
       "      <td>0.887499</td>\n",
       "      <td>0.845341</td>\n",
       "      <td>0.848169</td>\n",
       "      <td>0.874380</td>\n",
       "      <td>0.864628</td>\n",
       "      <td>0.015943</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.409271</td>\n",
       "      <td>0.002517</td>\n",
       "      <td>0.011128</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>gini</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 7, 'min_sam...</td>\n",
       "      <td>0.923521</td>\n",
       "      <td>0.938604</td>\n",
       "      <td>0.940278</td>\n",
       "      <td>0.935209</td>\n",
       "      <td>0.944904</td>\n",
       "      <td>0.936503</td>\n",
       "      <td>0.007203</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.040522</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>0.011501</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>gini</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 7, 'min_sam...</td>\n",
       "      <td>0.867752</td>\n",
       "      <td>0.887499</td>\n",
       "      <td>0.845341</td>\n",
       "      <td>0.848169</td>\n",
       "      <td>0.874380</td>\n",
       "      <td>0.864628</td>\n",
       "      <td>0.015943</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.410455</td>\n",
       "      <td>0.001852</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>gini</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 7, 'min_sam...</td>\n",
       "      <td>0.923521</td>\n",
       "      <td>0.938604</td>\n",
       "      <td>0.940278</td>\n",
       "      <td>0.935209</td>\n",
       "      <td>0.944904</td>\n",
       "      <td>0.936503</td>\n",
       "      <td>0.007203</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.040013</td>\n",
       "      <td>0.001142</td>\n",
       "      <td>0.011510</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>gini</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 7, 'min_sam...</td>\n",
       "      <td>0.857308</td>\n",
       "      <td>0.864922</td>\n",
       "      <td>0.845341</td>\n",
       "      <td>0.848169</td>\n",
       "      <td>0.874380</td>\n",
       "      <td>0.858024</td>\n",
       "      <td>0.010711</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.310467</td>\n",
       "      <td>0.001552</td>\n",
       "      <td>0.010947</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>gini</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 5, 'min_sam...</td>\n",
       "      <td>0.897510</td>\n",
       "      <td>0.898031</td>\n",
       "      <td>0.910589</td>\n",
       "      <td>0.903538</td>\n",
       "      <td>0.908900</td>\n",
       "      <td>0.903713</td>\n",
       "      <td>0.005385</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.034040</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.011327</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>gini</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 5, 'min_sam...</td>\n",
       "      <td>0.777205</td>\n",
       "      <td>0.814027</td>\n",
       "      <td>0.798007</td>\n",
       "      <td>0.824077</td>\n",
       "      <td>0.805502</td>\n",
       "      <td>0.803764</td>\n",
       "      <td>0.015871</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.310040</td>\n",
       "      <td>0.001502</td>\n",
       "      <td>0.010979</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>gini</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 5, 'min_sam...</td>\n",
       "      <td>0.897510</td>\n",
       "      <td>0.898031</td>\n",
       "      <td>0.910589</td>\n",
       "      <td>0.903538</td>\n",
       "      <td>0.908900</td>\n",
       "      <td>0.903713</td>\n",
       "      <td>0.005385</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.033889</td>\n",
       "      <td>0.000450</td>\n",
       "      <td>0.011292</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>gini</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 5, 'min_sam...</td>\n",
       "      <td>0.777205</td>\n",
       "      <td>0.814027</td>\n",
       "      <td>0.798007</td>\n",
       "      <td>0.824077</td>\n",
       "      <td>0.805502</td>\n",
       "      <td>0.803764</td>\n",
       "      <td>0.015871</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.309409</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>0.010932</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>gini</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 5, 'min_sam...</td>\n",
       "      <td>0.897510</td>\n",
       "      <td>0.898031</td>\n",
       "      <td>0.910589</td>\n",
       "      <td>0.903538</td>\n",
       "      <td>0.908900</td>\n",
       "      <td>0.903713</td>\n",
       "      <td>0.005385</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.034560</td>\n",
       "      <td>0.001083</td>\n",
       "      <td>0.011307</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>gini</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 5, 'min_sam...</td>\n",
       "      <td>0.777205</td>\n",
       "      <td>0.814027</td>\n",
       "      <td>0.798007</td>\n",
       "      <td>0.824077</td>\n",
       "      <td>0.805502</td>\n",
       "      <td>0.803764</td>\n",
       "      <td>0.015871</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.755167</td>\n",
       "      <td>0.016930</td>\n",
       "      <td>0.011897</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>entropy</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': None, 'm...</td>\n",
       "      <td>0.967320</td>\n",
       "      <td>0.978848</td>\n",
       "      <td>0.980888</td>\n",
       "      <td>0.980081</td>\n",
       "      <td>0.980089</td>\n",
       "      <td>0.977445</td>\n",
       "      <td>0.005105</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.068267</td>\n",
       "      <td>0.001940</td>\n",
       "      <td>0.012605</td>\n",
       "      <td>0.000298</td>\n",
       "      <td>entropy</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': None, 'm...</td>\n",
       "      <td>0.961415</td>\n",
       "      <td>0.977979</td>\n",
       "      <td>0.976969</td>\n",
       "      <td>0.977004</td>\n",
       "      <td>0.977898</td>\n",
       "      <td>0.974253</td>\n",
       "      <td>0.006433</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.755559</td>\n",
       "      <td>0.016511</td>\n",
       "      <td>0.011842</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>entropy</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': None, 'm...</td>\n",
       "      <td>0.966106</td>\n",
       "      <td>0.979203</td>\n",
       "      <td>0.980752</td>\n",
       "      <td>0.978922</td>\n",
       "      <td>0.979467</td>\n",
       "      <td>0.976890</td>\n",
       "      <td>0.005428</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.068026</td>\n",
       "      <td>0.001807</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>0.000259</td>\n",
       "      <td>entropy</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': None, 'm...</td>\n",
       "      <td>0.962742</td>\n",
       "      <td>0.977183</td>\n",
       "      <td>0.978497</td>\n",
       "      <td>0.978742</td>\n",
       "      <td>0.977934</td>\n",
       "      <td>0.975020</td>\n",
       "      <td>0.006162</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.779302</td>\n",
       "      <td>0.034346</td>\n",
       "      <td>0.011989</td>\n",
       "      <td>0.000311</td>\n",
       "      <td>entropy</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': None, 'm...</td>\n",
       "      <td>0.966156</td>\n",
       "      <td>0.978840</td>\n",
       "      <td>0.980356</td>\n",
       "      <td>0.980009</td>\n",
       "      <td>0.979733</td>\n",
       "      <td>0.977019</td>\n",
       "      <td>0.005455</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.067732</td>\n",
       "      <td>0.002713</td>\n",
       "      <td>0.012501</td>\n",
       "      <td>0.000324</td>\n",
       "      <td>entropy</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': None, 'm...</td>\n",
       "      <td>0.962256</td>\n",
       "      <td>0.977199</td>\n",
       "      <td>0.976180</td>\n",
       "      <td>0.976423</td>\n",
       "      <td>0.976328</td>\n",
       "      <td>0.973677</td>\n",
       "      <td>0.005722</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.660591</td>\n",
       "      <td>0.010802</td>\n",
       "      <td>0.011471</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>entropy</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 10, 'min...</td>\n",
       "      <td>0.949663</td>\n",
       "      <td>0.967123</td>\n",
       "      <td>0.966542</td>\n",
       "      <td>0.965997</td>\n",
       "      <td>0.964909</td>\n",
       "      <td>0.962847</td>\n",
       "      <td>0.006632</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.048409</td>\n",
       "      <td>0.001258</td>\n",
       "      <td>0.011696</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>entropy</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 10, 'min...</td>\n",
       "      <td>0.904245</td>\n",
       "      <td>0.926470</td>\n",
       "      <td>0.929950</td>\n",
       "      <td>0.928196</td>\n",
       "      <td>0.933737</td>\n",
       "      <td>0.924520</td>\n",
       "      <td>0.010420</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.670656</td>\n",
       "      <td>0.016134</td>\n",
       "      <td>0.011426</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>entropy</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 10, 'min...</td>\n",
       "      <td>0.949524</td>\n",
       "      <td>0.967250</td>\n",
       "      <td>0.966591</td>\n",
       "      <td>0.965771</td>\n",
       "      <td>0.964866</td>\n",
       "      <td>0.962800</td>\n",
       "      <td>0.006686</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.048200</td>\n",
       "      <td>0.001127</td>\n",
       "      <td>0.011671</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>entropy</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 10, 'min...</td>\n",
       "      <td>0.904245</td>\n",
       "      <td>0.926470</td>\n",
       "      <td>0.930005</td>\n",
       "      <td>0.929878</td>\n",
       "      <td>0.939005</td>\n",
       "      <td>0.925920</td>\n",
       "      <td>0.011608</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.662174</td>\n",
       "      <td>0.010838</td>\n",
       "      <td>0.011545</td>\n",
       "      <td>0.000366</td>\n",
       "      <td>entropy</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 10, 'min...</td>\n",
       "      <td>0.949476</td>\n",
       "      <td>0.967206</td>\n",
       "      <td>0.966679</td>\n",
       "      <td>0.965771</td>\n",
       "      <td>0.964869</td>\n",
       "      <td>0.962800</td>\n",
       "      <td>0.006710</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.051657</td>\n",
       "      <td>0.003543</td>\n",
       "      <td>0.011816</td>\n",
       "      <td>0.000193</td>\n",
       "      <td>entropy</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 10, 'min...</td>\n",
       "      <td>0.917632</td>\n",
       "      <td>0.928349</td>\n",
       "      <td>0.930005</td>\n",
       "      <td>0.924551</td>\n",
       "      <td>0.915752</td>\n",
       "      <td>0.923258</td>\n",
       "      <td>0.005676</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.521385</td>\n",
       "      <td>0.002568</td>\n",
       "      <td>0.011145</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>entropy</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 7, 'min_...</td>\n",
       "      <td>0.922999</td>\n",
       "      <td>0.942128</td>\n",
       "      <td>0.946092</td>\n",
       "      <td>0.941989</td>\n",
       "      <td>0.942425</td>\n",
       "      <td>0.939127</td>\n",
       "      <td>0.008206</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.040566</td>\n",
       "      <td>0.000710</td>\n",
       "      <td>0.011411</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>entropy</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 7, 'min_...</td>\n",
       "      <td>0.845905</td>\n",
       "      <td>0.864778</td>\n",
       "      <td>0.895556</td>\n",
       "      <td>0.894095</td>\n",
       "      <td>0.864842</td>\n",
       "      <td>0.873035</td>\n",
       "      <td>0.019090</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.520388</td>\n",
       "      <td>0.003224</td>\n",
       "      <td>0.011164</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>entropy</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 7, 'min_...</td>\n",
       "      <td>0.922999</td>\n",
       "      <td>0.942128</td>\n",
       "      <td>0.946092</td>\n",
       "      <td>0.941989</td>\n",
       "      <td>0.942425</td>\n",
       "      <td>0.939127</td>\n",
       "      <td>0.008206</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.041186</td>\n",
       "      <td>0.001409</td>\n",
       "      <td>0.011510</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>entropy</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 7, 'min_...</td>\n",
       "      <td>0.845905</td>\n",
       "      <td>0.864778</td>\n",
       "      <td>0.895556</td>\n",
       "      <td>0.894095</td>\n",
       "      <td>0.864842</td>\n",
       "      <td>0.873035</td>\n",
       "      <td>0.019090</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.524917</td>\n",
       "      <td>0.007270</td>\n",
       "      <td>0.011461</td>\n",
       "      <td>0.000921</td>\n",
       "      <td>entropy</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 7, 'min_...</td>\n",
       "      <td>0.922999</td>\n",
       "      <td>0.942128</td>\n",
       "      <td>0.946092</td>\n",
       "      <td>0.941989</td>\n",
       "      <td>0.942425</td>\n",
       "      <td>0.939127</td>\n",
       "      <td>0.008206</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.040743</td>\n",
       "      <td>0.000551</td>\n",
       "      <td>0.011526</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>entropy</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 7, 'min_...</td>\n",
       "      <td>0.845905</td>\n",
       "      <td>0.864778</td>\n",
       "      <td>0.895556</td>\n",
       "      <td>0.876315</td>\n",
       "      <td>0.864842</td>\n",
       "      <td>0.869479</td>\n",
       "      <td>0.016285</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.393272</td>\n",
       "      <td>0.004017</td>\n",
       "      <td>0.010890</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>entropy</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 5, 'min_...</td>\n",
       "      <td>0.902639</td>\n",
       "      <td>0.911163</td>\n",
       "      <td>0.915370</td>\n",
       "      <td>0.910369</td>\n",
       "      <td>0.912167</td>\n",
       "      <td>0.910341</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.034749</td>\n",
       "      <td>0.000629</td>\n",
       "      <td>0.011332</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>entropy</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 5, 'min_...</td>\n",
       "      <td>0.783990</td>\n",
       "      <td>0.801293</td>\n",
       "      <td>0.817636</td>\n",
       "      <td>0.824077</td>\n",
       "      <td>0.798531</td>\n",
       "      <td>0.805105</td>\n",
       "      <td>0.014287</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.392663</td>\n",
       "      <td>0.001350</td>\n",
       "      <td>0.010861</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>entropy</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 5, 'min_...</td>\n",
       "      <td>0.902639</td>\n",
       "      <td>0.911163</td>\n",
       "      <td>0.915370</td>\n",
       "      <td>0.910369</td>\n",
       "      <td>0.912167</td>\n",
       "      <td>0.910341</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.034437</td>\n",
       "      <td>0.000831</td>\n",
       "      <td>0.011325</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>entropy</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 5, 'min_...</td>\n",
       "      <td>0.783990</td>\n",
       "      <td>0.801293</td>\n",
       "      <td>0.817636</td>\n",
       "      <td>0.824077</td>\n",
       "      <td>0.798531</td>\n",
       "      <td>0.805105</td>\n",
       "      <td>0.014287</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.393191</td>\n",
       "      <td>0.001665</td>\n",
       "      <td>0.010871</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>entropy</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 5, 'min_...</td>\n",
       "      <td>0.902639</td>\n",
       "      <td>0.911163</td>\n",
       "      <td>0.915370</td>\n",
       "      <td>0.910369</td>\n",
       "      <td>0.912167</td>\n",
       "      <td>0.910341</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.034510</td>\n",
       "      <td>0.000778</td>\n",
       "      <td>0.011423</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>entropy</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 5, 'min_...</td>\n",
       "      <td>0.783990</td>\n",
       "      <td>0.801293</td>\n",
       "      <td>0.817636</td>\n",
       "      <td>0.824077</td>\n",
       "      <td>0.798531</td>\n",
       "      <td>0.805105</td>\n",
       "      <td>0.014287</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.674387      0.017642         0.012455        0.000733   \n",
       "1        0.069653      0.001871         0.012714        0.000269   \n",
       "2        0.659199      0.028124         0.012090        0.000257   \n",
       "3        0.067967      0.002614         0.012613        0.000235   \n",
       "4        0.660544      0.026469         0.012071        0.000217   \n",
       "5        0.067559      0.000988         0.012529        0.000268   \n",
       "6        0.531295      0.010049         0.011379        0.000266   \n",
       "7        0.048389      0.000655         0.011712        0.000267   \n",
       "8        0.533087      0.010285         0.011381        0.000267   \n",
       "9        0.049336      0.001273         0.011708        0.000237   \n",
       "10       0.534207      0.008250         0.011458        0.000388   \n",
       "11       0.049710      0.001464         0.011746        0.000233   \n",
       "12       0.410795      0.001569         0.011086        0.000217   \n",
       "13       0.040341      0.001309         0.011481        0.000132   \n",
       "14       0.409271      0.002517         0.011128        0.000224   \n",
       "15       0.040522      0.001267         0.011501        0.000150   \n",
       "16       0.410455      0.001852         0.011214        0.000267   \n",
       "17       0.040013      0.001142         0.011510        0.000161   \n",
       "18       0.310467      0.001552         0.010947        0.000205   \n",
       "19       0.034040      0.000413         0.011327        0.000207   \n",
       "20       0.310040      0.001502         0.010979        0.000195   \n",
       "21       0.033889      0.000450         0.011292        0.000213   \n",
       "22       0.309409      0.001335         0.010932        0.000217   \n",
       "23       0.034560      0.001083         0.011307        0.000192   \n",
       "24       0.755167      0.016930         0.011897        0.000247   \n",
       "25       0.068267      0.001940         0.012605        0.000298   \n",
       "26       0.755559      0.016511         0.011842        0.000329   \n",
       "27       0.068026      0.001807         0.012463        0.000259   \n",
       "28       0.779302      0.034346         0.011989        0.000311   \n",
       "29       0.067732      0.002713         0.012501        0.000324   \n",
       "30       0.660591      0.010802         0.011471        0.000264   \n",
       "31       0.048409      0.001258         0.011696        0.000216   \n",
       "32       0.670656      0.016134         0.011426        0.000284   \n",
       "33       0.048200      0.001127         0.011671        0.000216   \n",
       "34       0.662174      0.010838         0.011545        0.000366   \n",
       "35       0.051657      0.003543         0.011816        0.000193   \n",
       "36       0.521385      0.002568         0.011145        0.000261   \n",
       "37       0.040566      0.000710         0.011411        0.000288   \n",
       "38       0.520388      0.003224         0.011164        0.000260   \n",
       "39       0.041186      0.001409         0.011510        0.000383   \n",
       "40       0.524917      0.007270         0.011461        0.000921   \n",
       "41       0.040743      0.000551         0.011526        0.000300   \n",
       "42       0.393272      0.004017         0.010890        0.000211   \n",
       "43       0.034749      0.000629         0.011332        0.000177   \n",
       "44       0.392663      0.001350         0.010861        0.000244   \n",
       "45       0.034437      0.000831         0.011325        0.000234   \n",
       "46       0.393191      0.001665         0.010871        0.000239   \n",
       "47       0.034510      0.000778         0.011423        0.000317   \n",
       "\n",
       "   param_criterion param_max_depth param_min_samples_split param_splitter  \\\n",
       "0             gini            None                       2           best   \n",
       "1             gini            None                       2         random   \n",
       "2             gini            None                       3           best   \n",
       "3             gini            None                       3         random   \n",
       "4             gini            None                       4           best   \n",
       "5             gini            None                       4         random   \n",
       "6             gini              10                       2           best   \n",
       "7             gini              10                       2         random   \n",
       "8             gini              10                       3           best   \n",
       "9             gini              10                       3         random   \n",
       "10            gini              10                       4           best   \n",
       "11            gini              10                       4         random   \n",
       "12            gini               7                       2           best   \n",
       "13            gini               7                       2         random   \n",
       "14            gini               7                       3           best   \n",
       "15            gini               7                       3         random   \n",
       "16            gini               7                       4           best   \n",
       "17            gini               7                       4         random   \n",
       "18            gini               5                       2           best   \n",
       "19            gini               5                       2         random   \n",
       "20            gini               5                       3           best   \n",
       "21            gini               5                       3         random   \n",
       "22            gini               5                       4           best   \n",
       "23            gini               5                       4         random   \n",
       "24         entropy            None                       2           best   \n",
       "25         entropy            None                       2         random   \n",
       "26         entropy            None                       3           best   \n",
       "27         entropy            None                       3         random   \n",
       "28         entropy            None                       4           best   \n",
       "29         entropy            None                       4         random   \n",
       "30         entropy              10                       2           best   \n",
       "31         entropy              10                       2         random   \n",
       "32         entropy              10                       3           best   \n",
       "33         entropy              10                       3         random   \n",
       "34         entropy              10                       4           best   \n",
       "35         entropy              10                       4         random   \n",
       "36         entropy               7                       2           best   \n",
       "37         entropy               7                       2         random   \n",
       "38         entropy               7                       3           best   \n",
       "39         entropy               7                       3         random   \n",
       "40         entropy               7                       4           best   \n",
       "41         entropy               7                       4         random   \n",
       "42         entropy               5                       2           best   \n",
       "43         entropy               5                       2         random   \n",
       "44         entropy               5                       3           best   \n",
       "45         entropy               5                       3         random   \n",
       "46         entropy               5                       4           best   \n",
       "47         entropy               5                       4         random   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0   {'criterion': 'gini', 'max_depth': None, 'min_...           0.965157   \n",
       "1   {'criterion': 'gini', 'max_depth': None, 'min_...           0.963093   \n",
       "2   {'criterion': 'gini', 'max_depth': None, 'min_...           0.963649   \n",
       "3   {'criterion': 'gini', 'max_depth': None, 'min_...           0.960673   \n",
       "4   {'criterion': 'gini', 'max_depth': None, 'min_...           0.963686   \n",
       "5   {'criterion': 'gini', 'max_depth': None, 'min_...           0.960471   \n",
       "6   {'criterion': 'gini', 'max_depth': 10, 'min_sa...           0.950992   \n",
       "7   {'criterion': 'gini', 'max_depth': 10, 'min_sa...           0.908957   \n",
       "8   {'criterion': 'gini', 'max_depth': 10, 'min_sa...           0.951184   \n",
       "9   {'criterion': 'gini', 'max_depth': 10, 'min_sa...           0.904183   \n",
       "10  {'criterion': 'gini', 'max_depth': 10, 'min_sa...           0.951188   \n",
       "11  {'criterion': 'gini', 'max_depth': 10, 'min_sa...           0.914138   \n",
       "12  {'criterion': 'gini', 'max_depth': 7, 'min_sam...           0.923521   \n",
       "13  {'criterion': 'gini', 'max_depth': 7, 'min_sam...           0.867752   \n",
       "14  {'criterion': 'gini', 'max_depth': 7, 'min_sam...           0.923521   \n",
       "15  {'criterion': 'gini', 'max_depth': 7, 'min_sam...           0.867752   \n",
       "16  {'criterion': 'gini', 'max_depth': 7, 'min_sam...           0.923521   \n",
       "17  {'criterion': 'gini', 'max_depth': 7, 'min_sam...           0.857308   \n",
       "18  {'criterion': 'gini', 'max_depth': 5, 'min_sam...           0.897510   \n",
       "19  {'criterion': 'gini', 'max_depth': 5, 'min_sam...           0.777205   \n",
       "20  {'criterion': 'gini', 'max_depth': 5, 'min_sam...           0.897510   \n",
       "21  {'criterion': 'gini', 'max_depth': 5, 'min_sam...           0.777205   \n",
       "22  {'criterion': 'gini', 'max_depth': 5, 'min_sam...           0.897510   \n",
       "23  {'criterion': 'gini', 'max_depth': 5, 'min_sam...           0.777205   \n",
       "24  {'criterion': 'entropy', 'max_depth': None, 'm...           0.967320   \n",
       "25  {'criterion': 'entropy', 'max_depth': None, 'm...           0.961415   \n",
       "26  {'criterion': 'entropy', 'max_depth': None, 'm...           0.966106   \n",
       "27  {'criterion': 'entropy', 'max_depth': None, 'm...           0.962742   \n",
       "28  {'criterion': 'entropy', 'max_depth': None, 'm...           0.966156   \n",
       "29  {'criterion': 'entropy', 'max_depth': None, 'm...           0.962256   \n",
       "30  {'criterion': 'entropy', 'max_depth': 10, 'min...           0.949663   \n",
       "31  {'criterion': 'entropy', 'max_depth': 10, 'min...           0.904245   \n",
       "32  {'criterion': 'entropy', 'max_depth': 10, 'min...           0.949524   \n",
       "33  {'criterion': 'entropy', 'max_depth': 10, 'min...           0.904245   \n",
       "34  {'criterion': 'entropy', 'max_depth': 10, 'min...           0.949476   \n",
       "35  {'criterion': 'entropy', 'max_depth': 10, 'min...           0.917632   \n",
       "36  {'criterion': 'entropy', 'max_depth': 7, 'min_...           0.922999   \n",
       "37  {'criterion': 'entropy', 'max_depth': 7, 'min_...           0.845905   \n",
       "38  {'criterion': 'entropy', 'max_depth': 7, 'min_...           0.922999   \n",
       "39  {'criterion': 'entropy', 'max_depth': 7, 'min_...           0.845905   \n",
       "40  {'criterion': 'entropy', 'max_depth': 7, 'min_...           0.922999   \n",
       "41  {'criterion': 'entropy', 'max_depth': 7, 'min_...           0.845905   \n",
       "42  {'criterion': 'entropy', 'max_depth': 5, 'min_...           0.902639   \n",
       "43  {'criterion': 'entropy', 'max_depth': 5, 'min_...           0.783990   \n",
       "44  {'criterion': 'entropy', 'max_depth': 5, 'min_...           0.902639   \n",
       "45  {'criterion': 'entropy', 'max_depth': 5, 'min_...           0.783990   \n",
       "46  {'criterion': 'entropy', 'max_depth': 5, 'min_...           0.902639   \n",
       "47  {'criterion': 'entropy', 'max_depth': 5, 'min_...           0.783990   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0            0.975895           0.979059           0.978786   \n",
       "1            0.976477           0.977161           0.978156   \n",
       "2            0.975707           0.979181           0.979228   \n",
       "3            0.976117           0.975082           0.976862   \n",
       "4            0.975484           0.979182           0.978387   \n",
       "5            0.977602           0.977560           0.977194   \n",
       "6            0.967870           0.969446           0.967008   \n",
       "7            0.924562           0.913287           0.932733   \n",
       "8            0.967951           0.969443           0.966956   \n",
       "9            0.905610           0.934201           0.926759   \n",
       "10           0.968218           0.969397           0.967049   \n",
       "11           0.939293           0.933291           0.926194   \n",
       "12           0.938604           0.940278           0.935209   \n",
       "13           0.887499           0.845341           0.848169   \n",
       "14           0.938604           0.940278           0.935209   \n",
       "15           0.887499           0.845341           0.848169   \n",
       "16           0.938604           0.940278           0.935209   \n",
       "17           0.864922           0.845341           0.848169   \n",
       "18           0.898031           0.910589           0.903538   \n",
       "19           0.814027           0.798007           0.824077   \n",
       "20           0.898031           0.910589           0.903538   \n",
       "21           0.814027           0.798007           0.824077   \n",
       "22           0.898031           0.910589           0.903538   \n",
       "23           0.814027           0.798007           0.824077   \n",
       "24           0.978848           0.980888           0.980081   \n",
       "25           0.977979           0.976969           0.977004   \n",
       "26           0.979203           0.980752           0.978922   \n",
       "27           0.977183           0.978497           0.978742   \n",
       "28           0.978840           0.980356           0.980009   \n",
       "29           0.977199           0.976180           0.976423   \n",
       "30           0.967123           0.966542           0.965997   \n",
       "31           0.926470           0.929950           0.928196   \n",
       "32           0.967250           0.966591           0.965771   \n",
       "33           0.926470           0.930005           0.929878   \n",
       "34           0.967206           0.966679           0.965771   \n",
       "35           0.928349           0.930005           0.924551   \n",
       "36           0.942128           0.946092           0.941989   \n",
       "37           0.864778           0.895556           0.894095   \n",
       "38           0.942128           0.946092           0.941989   \n",
       "39           0.864778           0.895556           0.894095   \n",
       "40           0.942128           0.946092           0.941989   \n",
       "41           0.864778           0.895556           0.876315   \n",
       "42           0.911163           0.915370           0.910369   \n",
       "43           0.801293           0.817636           0.824077   \n",
       "44           0.911163           0.915370           0.910369   \n",
       "45           0.801293           0.817636           0.824077   \n",
       "46           0.911163           0.915370           0.910369   \n",
       "47           0.801293           0.817636           0.824077   \n",
       "\n",
       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0            0.976370         0.975053        0.005106                4  \n",
       "1            0.975789         0.974135        0.005576                9  \n",
       "2            0.977090         0.974971        0.005815                6  \n",
       "3            0.976452         0.973037        0.006210               12  \n",
       "4            0.976920         0.974732        0.005666                7  \n",
       "5            0.975894         0.973744        0.006665               10  \n",
       "6            0.968340         0.964731        0.006914               15  \n",
       "7            0.939999         0.923908        0.011607               28  \n",
       "8            0.968645         0.964836        0.006875               14  \n",
       "9            0.933489         0.920848        0.013289               30  \n",
       "10           0.968462         0.964863        0.006878               13  \n",
       "11           0.937588         0.930101        0.009174               25  \n",
       "12           0.944904         0.936503        0.007203               22  \n",
       "13           0.874380         0.864628        0.015943               40  \n",
       "14           0.944904         0.936503        0.007203               22  \n",
       "15           0.874380         0.864628        0.015943               40  \n",
       "16           0.944904         0.936503        0.007203               22  \n",
       "17           0.874380         0.858024        0.010711               42  \n",
       "18           0.908900         0.903713        0.005385               34  \n",
       "19           0.805502         0.803764        0.015871               46  \n",
       "20           0.908900         0.903713        0.005385               34  \n",
       "21           0.805502         0.803764        0.015871               46  \n",
       "22           0.908900         0.903713        0.005385               34  \n",
       "23           0.805502         0.803764        0.015871               46  \n",
       "24           0.980089         0.977445        0.005105                1  \n",
       "25           0.977898         0.974253        0.006433                8  \n",
       "26           0.979467         0.976890        0.005428                3  \n",
       "27           0.977934         0.975020        0.006162                5  \n",
       "28           0.979733         0.977019        0.005455                2  \n",
       "29           0.976328         0.973677        0.005722               11  \n",
       "30           0.964909         0.962847        0.006632               16  \n",
       "31           0.933737         0.924520        0.010420               27  \n",
       "32           0.964866         0.962800        0.006686               17  \n",
       "33           0.939005         0.925920        0.011608               26  \n",
       "34           0.964869         0.962800        0.006710               18  \n",
       "35           0.915752         0.923258        0.005676               29  \n",
       "36           0.942425         0.939127        0.008206               19  \n",
       "37           0.864842         0.873035        0.019090               37  \n",
       "38           0.942425         0.939127        0.008206               19  \n",
       "39           0.864842         0.873035        0.019090               37  \n",
       "40           0.942425         0.939127        0.008206               19  \n",
       "41           0.864842         0.869479        0.016285               39  \n",
       "42           0.912167         0.910341        0.004210               31  \n",
       "43           0.798531         0.805105        0.014287               43  \n",
       "44           0.912167         0.910341        0.004210               31  \n",
       "45           0.798531         0.805105        0.014287               43  \n",
       "46           0.912167         0.910341        0.004210               31  \n",
       "47           0.798531         0.805105        0.014287               43  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GridSearch for DecisionTreeClassifier\n",
    "parameters = {'criterion':['gini', 'entropy'], 'splitter':['best', 'random'], 'max_depth':[None, 10, 7, 5], 'min_samples_split':[2, 3, 4]}\n",
    "model = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "clf = GridSearchCV(model, parameters, scoring=\"f1\")\n",
    "clf.fit(X_train, y_train)\n",
    "df = pd.DataFrame(clf.cv_results_)\n",
    "df.to_csv(\"parameter_search/decisiontree_param_tuning.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e_m7XHHKjpTh"
   },
   "source": [
    "## [SGDClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 878
    },
    "id": "5Z474hdHjQFg",
    "outputId": "360b295a-3b6c-4589-8c67-e94b4f63813f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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_eta0</th>\n",
       "      <th>param_learning_rate</th>\n",
       "      <th>param_penalty</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.774193</td>\n",
       "      <td>0.158204</td>\n",
       "      <td>0.027890</td>\n",
       "      <td>0.011560</td>\n",
       "      <td>0.001</td>\n",
       "      <td>constant</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'eta0': 0.001, 'learning_rate': 'constant', '...</td>\n",
       "      <td>0.330607</td>\n",
       "      <td>0.666885</td>\n",
       "      <td>0.774907</td>\n",
       "      <td>0.594648</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.473516</td>\n",
       "      <td>0.278221</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.104324</td>\n",
       "      <td>0.181571</td>\n",
       "      <td>0.022587</td>\n",
       "      <td>0.002260</td>\n",
       "      <td>0.001</td>\n",
       "      <td>constant</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'eta0': 0.001, 'learning_rate': 'constant', '...</td>\n",
       "      <td>0.765391</td>\n",
       "      <td>0.782056</td>\n",
       "      <td>0.674010</td>\n",
       "      <td>0.526796</td>\n",
       "      <td>0.734298</td>\n",
       "      <td>0.696510</td>\n",
       "      <td>0.092520</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.528636</td>\n",
       "      <td>0.345031</td>\n",
       "      <td>0.025352</td>\n",
       "      <td>0.002660</td>\n",
       "      <td>0.001</td>\n",
       "      <td>constant</td>\n",
       "      <td>elasticnet</td>\n",
       "      <td>{'eta0': 0.001, 'learning_rate': 'constant', '...</td>\n",
       "      <td>0.731000</td>\n",
       "      <td>0.667005</td>\n",
       "      <td>0.671432</td>\n",
       "      <td>0.591399</td>\n",
       "      <td>0.670910</td>\n",
       "      <td>0.666349</td>\n",
       "      <td>0.044372</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.948836</td>\n",
       "      <td>0.466000</td>\n",
       "      <td>0.023896</td>\n",
       "      <td>0.001042</td>\n",
       "      <td>0.001</td>\n",
       "      <td>optimal</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'eta0': 0.001, 'learning_rate': 'optimal', 'p...</td>\n",
       "      <td>0.800019</td>\n",
       "      <td>0.789422</td>\n",
       "      <td>0.771414</td>\n",
       "      <td>0.651949</td>\n",
       "      <td>0.801834</td>\n",
       "      <td>0.762928</td>\n",
       "      <td>0.056533</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.350017</td>\n",
       "      <td>0.810974</td>\n",
       "      <td>0.021732</td>\n",
       "      <td>0.000322</td>\n",
       "      <td>0.001</td>\n",
       "      <td>optimal</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'eta0': 0.001, 'learning_rate': 'optimal', 'p...</td>\n",
       "      <td>0.786147</td>\n",
       "      <td>0.786332</td>\n",
       "      <td>0.796811</td>\n",
       "      <td>0.795890</td>\n",
       "      <td>0.796502</td>\n",
       "      <td>0.792336</td>\n",
       "      <td>0.004987</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>4.560351</td>\n",
       "      <td>0.468787</td>\n",
       "      <td>0.022504</td>\n",
       "      <td>0.000703</td>\n",
       "      <td>1</td>\n",
       "      <td>invscaling</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'eta0': 1, 'learning_rate': 'invscaling', 'pe...</td>\n",
       "      <td>0.705641</td>\n",
       "      <td>0.797634</td>\n",
       "      <td>0.694284</td>\n",
       "      <td>0.793780</td>\n",
       "      <td>0.803204</td>\n",
       "      <td>0.758909</td>\n",
       "      <td>0.048356</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>4.757399</td>\n",
       "      <td>0.633029</td>\n",
       "      <td>0.021300</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>1</td>\n",
       "      <td>invscaling</td>\n",
       "      <td>elasticnet</td>\n",
       "      <td>{'eta0': 1, 'learning_rate': 'invscaling', 'pe...</td>\n",
       "      <td>0.364642</td>\n",
       "      <td>0.780651</td>\n",
       "      <td>0.789544</td>\n",
       "      <td>0.791894</td>\n",
       "      <td>0.694294</td>\n",
       "      <td>0.684205</td>\n",
       "      <td>0.163840</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>6.568300</td>\n",
       "      <td>1.795441</td>\n",
       "      <td>0.026794</td>\n",
       "      <td>0.003350</td>\n",
       "      <td>1</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'eta0': 1, 'learning_rate': 'adaptive', 'pena...</td>\n",
       "      <td>0.801586</td>\n",
       "      <td>0.798152</td>\n",
       "      <td>0.803637</td>\n",
       "      <td>0.803441</td>\n",
       "      <td>0.800469</td>\n",
       "      <td>0.801457</td>\n",
       "      <td>0.002030</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>4.036290</td>\n",
       "      <td>0.611888</td>\n",
       "      <td>0.023282</td>\n",
       "      <td>0.001333</td>\n",
       "      <td>1</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'eta0': 1, 'learning_rate': 'adaptive', 'pena...</td>\n",
       "      <td>0.795746</td>\n",
       "      <td>0.790026</td>\n",
       "      <td>0.798205</td>\n",
       "      <td>0.794181</td>\n",
       "      <td>0.796586</td>\n",
       "      <td>0.794949</td>\n",
       "      <td>0.002784</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>7.275008</td>\n",
       "      <td>1.166240</td>\n",
       "      <td>0.024008</td>\n",
       "      <td>0.001662</td>\n",
       "      <td>1</td>\n",
       "      <td>adaptive</td>\n",
       "      <td>elasticnet</td>\n",
       "      <td>{'eta0': 1, 'learning_rate': 'adaptive', 'pena...</td>\n",
       "      <td>0.802890</td>\n",
       "      <td>0.797355</td>\n",
       "      <td>0.800251</td>\n",
       "      <td>0.803823</td>\n",
       "      <td>0.802354</td>\n",
       "      <td>0.801334</td>\n",
       "      <td>0.002309</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time param_eta0  \\\n",
       "0        0.774193      0.158204         0.027890        0.011560      0.001   \n",
       "1        1.104324      0.181571         0.022587        0.002260      0.001   \n",
       "2        1.528636      0.345031         0.025352        0.002660      0.001   \n",
       "3        6.948836      0.466000         0.023896        0.001042      0.001   \n",
       "4        3.350017      0.810974         0.021732        0.000322      0.001   \n",
       "..            ...           ...              ...             ...        ...   \n",
       "67       4.560351      0.468787         0.022504        0.000703          1   \n",
       "68       4.757399      0.633029         0.021300        0.000320          1   \n",
       "69       6.568300      1.795441         0.026794        0.003350          1   \n",
       "70       4.036290      0.611888         0.023282        0.001333          1   \n",
       "71       7.275008      1.166240         0.024008        0.001662          1   \n",
       "\n",
       "   param_learning_rate param_penalty  \\\n",
       "0             constant            l2   \n",
       "1             constant            l1   \n",
       "2             constant    elasticnet   \n",
       "3              optimal            l2   \n",
       "4              optimal            l1   \n",
       "..                 ...           ...   \n",
       "67          invscaling            l1   \n",
       "68          invscaling    elasticnet   \n",
       "69            adaptive            l2   \n",
       "70            adaptive            l1   \n",
       "71            adaptive    elasticnet   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0   {'eta0': 0.001, 'learning_rate': 'constant', '...           0.330607   \n",
       "1   {'eta0': 0.001, 'learning_rate': 'constant', '...           0.765391   \n",
       "2   {'eta0': 0.001, 'learning_rate': 'constant', '...           0.731000   \n",
       "3   {'eta0': 0.001, 'learning_rate': 'optimal', 'p...           0.800019   \n",
       "4   {'eta0': 0.001, 'learning_rate': 'optimal', 'p...           0.786147   \n",
       "..                                                ...                ...   \n",
       "67  {'eta0': 1, 'learning_rate': 'invscaling', 'pe...           0.705641   \n",
       "68  {'eta0': 1, 'learning_rate': 'invscaling', 'pe...           0.364642   \n",
       "69  {'eta0': 1, 'learning_rate': 'adaptive', 'pena...           0.801586   \n",
       "70  {'eta0': 1, 'learning_rate': 'adaptive', 'pena...           0.795746   \n",
       "71  {'eta0': 1, 'learning_rate': 'adaptive', 'pena...           0.802890   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0            0.666885           0.774907           0.594648   \n",
       "1            0.782056           0.674010           0.526796   \n",
       "2            0.667005           0.671432           0.591399   \n",
       "3            0.789422           0.771414           0.651949   \n",
       "4            0.786332           0.796811           0.795890   \n",
       "..                ...                ...                ...   \n",
       "67           0.797634           0.694284           0.793780   \n",
       "68           0.780651           0.789544           0.791894   \n",
       "69           0.798152           0.803637           0.803441   \n",
       "70           0.790026           0.798205           0.794181   \n",
       "71           0.797355           0.800251           0.803823   \n",
       "\n",
       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0            0.000535         0.473516        0.278221               65  \n",
       "1            0.734298         0.696510        0.092520               56  \n",
       "2            0.670910         0.666349        0.044372               61  \n",
       "3            0.801834         0.762928        0.056533               35  \n",
       "4            0.796502         0.792336        0.004987               23  \n",
       "..                ...              ...             ...              ...  \n",
       "67           0.803204         0.758909        0.048356               41  \n",
       "68           0.694294         0.684205        0.163840               57  \n",
       "69           0.800469         0.801457        0.002030                4  \n",
       "70           0.796586         0.794949        0.002784               18  \n",
       "71           0.802354         0.801334        0.002309                5  \n",
       "\n",
       "[72 rows x 16 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GridSearch for SGDClassifier\n",
    "parameters = {'penalty':['l2','l1','elasticnet'], 'learning_rate':['constant','optimal','invscaling','adaptive'], 'eta0':[0.001, 0.05, 0.01, 0.1, 0.5, 1]}\n",
    "model = SGDClassifier(random_state=42)\n",
    "\n",
    "clf = GridSearchCV(model, parameters, scoring=\"f1\")\n",
    "clf.fit(X_train, y_train)\n",
    "df = pd.DataFrame(clf.cv_results_)\n",
    "df.to_csv(\"parameter_search/sgd_param_tuning.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "55jF432Yk0As"
   },
   "source": [
    "## [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 982
    },
    "id": "wmVOiu2RkSxJ",
    "outputId": "eae964f9-2b96-4b57-8628-c5b132e277cc"
   },
   "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_C</th>\n",
       "      <th>param_penalty</th>\n",
       "      <th>param_solver</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.446116</td>\n",
       "      <td>1.209277</td>\n",
       "      <td>0.023749</td>\n",
       "      <td>0.001698</td>\n",
       "      <td>1</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>{'C': 1, 'penalty': 'l2', 'solver': 'newton-cg'}</td>\n",
       "      <td>0.853860</td>\n",
       "      <td>0.874910</td>\n",
       "      <td>0.879334</td>\n",
       "      <td>0.881033</td>\n",
       "      <td>0.877467</td>\n",
       "      <td>0.873321</td>\n",
       "      <td>0.009940</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.126089</td>\n",
       "      <td>0.207282</td>\n",
       "      <td>0.031998</td>\n",
       "      <td>0.007924</td>\n",
       "      <td>1</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'C': 1, 'penalty': 'l2', 'solver': 'lbfgs'}</td>\n",
       "      <td>0.804313</td>\n",
       "      <td>0.811869</td>\n",
       "      <td>0.816862</td>\n",
       "      <td>0.804215</td>\n",
       "      <td>0.815059</td>\n",
       "      <td>0.810464</td>\n",
       "      <td>0.005309</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.740407</td>\n",
       "      <td>0.096197</td>\n",
       "      <td>0.018749</td>\n",
       "      <td>0.000669</td>\n",
       "      <td>1</td>\n",
       "      <td>l2</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>{'C': 1, 'penalty': 'l2', 'solver': 'liblinear'}</td>\n",
       "      <td>0.854161</td>\n",
       "      <td>0.873074</td>\n",
       "      <td>0.876303</td>\n",
       "      <td>0.878244</td>\n",
       "      <td>0.874728</td>\n",
       "      <td>0.871302</td>\n",
       "      <td>0.008740</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.314447</td>\n",
       "      <td>0.596087</td>\n",
       "      <td>0.027944</td>\n",
       "      <td>0.003736</td>\n",
       "      <td>1</td>\n",
       "      <td>l2</td>\n",
       "      <td>sag</td>\n",
       "      <td>{'C': 1, 'penalty': 'l2', 'solver': 'sag'}</td>\n",
       "      <td>0.788725</td>\n",
       "      <td>0.786526</td>\n",
       "      <td>0.790583</td>\n",
       "      <td>0.789262</td>\n",
       "      <td>0.789121</td>\n",
       "      <td>0.788843</td>\n",
       "      <td>0.001316</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.969568</td>\n",
       "      <td>0.065124</td>\n",
       "      <td>0.025471</td>\n",
       "      <td>0.001108</td>\n",
       "      <td>1</td>\n",
       "      <td>l2</td>\n",
       "      <td>saga</td>\n",
       "      <td>{'C': 1, 'penalty': 'l2', 'solver': 'saga'}</td>\n",
       "      <td>0.769933</td>\n",
       "      <td>0.767759</td>\n",
       "      <td>0.769408</td>\n",
       "      <td>0.770983</td>\n",
       "      <td>0.771630</td>\n",
       "      <td>0.769943</td>\n",
       "      <td>0.001340</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.006118</td>\n",
       "      <td>0.000529</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500</td>\n",
       "      <td>elasticnet</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>{'C': 500, 'penalty': 'elasticnet', 'solver': ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.005479</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500</td>\n",
       "      <td>elasticnet</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'C': 500, 'penalty': 'elasticnet', 'solver': ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0.005480</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500</td>\n",
       "      <td>elasticnet</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>{'C': 500, 'penalty': 'elasticnet', 'solver': ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0.005153</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500</td>\n",
       "      <td>elasticnet</td>\n",
       "      <td>sag</td>\n",
       "      <td>{'C': 500, 'penalty': 'elasticnet', 'solver': ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0.003312</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500</td>\n",
       "      <td>elasticnet</td>\n",
       "      <td>saga</td>\n",
       "      <td>{'C': 500, 'penalty': 'elasticnet', 'solver': ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
       "0       10.446116      1.209277         0.023749        0.001698       1   \n",
       "1        1.126089      0.207282         0.031998        0.007924       1   \n",
       "2        0.740407      0.096197         0.018749        0.000669       1   \n",
       "3        3.314447      0.596087         0.027944        0.003736       1   \n",
       "4        2.969568      0.065124         0.025471        0.001108       1   \n",
       "..            ...           ...              ...             ...     ...   \n",
       "85       0.006118      0.000529         0.000000        0.000000     500   \n",
       "86       0.005479      0.000139         0.000000        0.000000     500   \n",
       "87       0.005480      0.000141         0.000000        0.000000     500   \n",
       "88       0.005153      0.000718         0.000000        0.000000     500   \n",
       "89       0.003312      0.000106         0.000000        0.000000     500   \n",
       "\n",
       "   param_penalty param_solver  \\\n",
       "0             l2    newton-cg   \n",
       "1             l2        lbfgs   \n",
       "2             l2    liblinear   \n",
       "3             l2          sag   \n",
       "4             l2         saga   \n",
       "..           ...          ...   \n",
       "85    elasticnet    newton-cg   \n",
       "86    elasticnet        lbfgs   \n",
       "87    elasticnet    liblinear   \n",
       "88    elasticnet          sag   \n",
       "89    elasticnet         saga   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0    {'C': 1, 'penalty': 'l2', 'solver': 'newton-cg'}           0.853860   \n",
       "1        {'C': 1, 'penalty': 'l2', 'solver': 'lbfgs'}           0.804313   \n",
       "2    {'C': 1, 'penalty': 'l2', 'solver': 'liblinear'}           0.854161   \n",
       "3          {'C': 1, 'penalty': 'l2', 'solver': 'sag'}           0.788725   \n",
       "4         {'C': 1, 'penalty': 'l2', 'solver': 'saga'}           0.769933   \n",
       "..                                                ...                ...   \n",
       "85  {'C': 500, 'penalty': 'elasticnet', 'solver': ...                NaN   \n",
       "86  {'C': 500, 'penalty': 'elasticnet', 'solver': ...                NaN   \n",
       "87  {'C': 500, 'penalty': 'elasticnet', 'solver': ...                NaN   \n",
       "88  {'C': 500, 'penalty': 'elasticnet', 'solver': ...                NaN   \n",
       "89  {'C': 500, 'penalty': 'elasticnet', 'solver': ...                NaN   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0            0.874910           0.879334           0.881033   \n",
       "1            0.811869           0.816862           0.804215   \n",
       "2            0.873074           0.876303           0.878244   \n",
       "3            0.786526           0.790583           0.789262   \n",
       "4            0.767759           0.769408           0.770983   \n",
       "..                ...                ...                ...   \n",
       "85                NaN                NaN                NaN   \n",
       "86                NaN                NaN                NaN   \n",
       "87                NaN                NaN                NaN   \n",
       "88                NaN                NaN                NaN   \n",
       "89                NaN                NaN                NaN   \n",
       "\n",
       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0            0.877467         0.873321        0.009940                6  \n",
       "1            0.815059         0.810464        0.005309               23  \n",
       "2            0.874728         0.871302        0.008740                7  \n",
       "3            0.789121         0.788843        0.001316               25  \n",
       "4            0.771630         0.769943        0.001340               31  \n",
       "..                ...              ...             ...              ...  \n",
       "85                NaN              NaN             NaN               54  \n",
       "86                NaN              NaN             NaN               55  \n",
       "87                NaN              NaN             NaN               56  \n",
       "88                NaN              NaN             NaN               84  \n",
       "89                NaN              NaN             NaN               90  \n",
       "\n",
       "[90 rows x 16 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GridSearch for LogisticRegression\n",
    "parameters = {'penalty':['l2','l1','elasticnet'], 'C':[1, 10, 50, 100, 200, 500], 'solver':['newton-cg','lbfgs','liblinear','sag','saga']}\n",
    "model = LogisticRegression(random_state=42)\n",
    "\n",
    "clf = GridSearchCV(model, parameters, scoring=\"f1\")\n",
    "clf.fit(X_train, y_train)\n",
    "df = pd.DataFrame(clf.cv_results_)\n",
    "df.to_csv(\"parameter_search/logistic_param_tuning.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [SVM with Pipeline](https://scikit-learn.org/stable/tutorial/statistical_inference/putting_together.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 932
    },
    "id": "zeUDosPqPgBa",
    "outputId": "e3184e64-eb3e-4a11-849e-9947d34746f1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  21 tasks      | elapsed: 13.7min\n",
      "[Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed: 62.2min finished\n"
     ]
    },
    {
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       "      <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",
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       "      <th>0</th>\n",
       "      <td>69.166824</td>\n",
       "      <td>2.860669</td>\n",
       "      <td>10.908463</td>\n",
       "      <td>0.363894</td>\n",
       "      <td>1</td>\n",
       "      <td>scale</td>\n",
       "      <td>{'svc__C': 1, 'svc__gamma': 'scale'}</td>\n",
       "      <td>0.959391</td>\n",
       "      <td>0.974181</td>\n",
       "      <td>0.974588</td>\n",
       "      <td>0.972775</td>\n",
       "      <td>0.973455</td>\n",
       "      <td>0.970878</td>\n",
       "      <td>0.005777</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.927757</td>\n",
       "      <td>3.204570</td>\n",
       "      <td>10.865314</td>\n",
       "      <td>0.383837</td>\n",
       "      <td>1</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'svc__C': 1, 'svc__gamma': 'auto'}</td>\n",
       "      <td>0.959391</td>\n",
       "      <td>0.974181</td>\n",
       "      <td>0.974588</td>\n",
       "      <td>0.972775</td>\n",
       "      <td>0.973455</td>\n",
       "      <td>0.970878</td>\n",
       "      <td>0.005777</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61.723366</td>\n",
       "      <td>3.873058</td>\n",
       "      <td>8.103805</td>\n",
       "      <td>0.334313</td>\n",
       "      <td>10</td>\n",
       "      <td>scale</td>\n",
       "      <td>{'svc__C': 10, 'svc__gamma': 'scale'}</td>\n",
       "      <td>0.964014</td>\n",
       "      <td>0.978964</td>\n",
       "      <td>0.979421</td>\n",
       "      <td>0.978614</td>\n",
       "      <td>0.979577</td>\n",
       "      <td>0.976118</td>\n",
       "      <td>0.006061</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62.992307</td>\n",
       "      <td>3.969130</td>\n",
       "      <td>8.053909</td>\n",
       "      <td>0.333345</td>\n",
       "      <td>10</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'svc__C': 10, 'svc__gamma': 'auto'}</td>\n",
       "      <td>0.964014</td>\n",
       "      <td>0.978964</td>\n",
       "      <td>0.979421</td>\n",
       "      <td>0.978614</td>\n",
       "      <td>0.979577</td>\n",
       "      <td>0.976118</td>\n",
       "      <td>0.006061</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80.517618</td>\n",
       "      <td>6.961829</td>\n",
       "      <td>6.867064</td>\n",
       "      <td>0.355988</td>\n",
       "      <td>50</td>\n",
       "      <td>scale</td>\n",
       "      <td>{'svc__C': 50, 'svc__gamma': 'scale'}</td>\n",
       "      <td>0.966642</td>\n",
       "      <td>0.981913</td>\n",
       "      <td>0.982194</td>\n",
       "      <td>0.982295</td>\n",
       "      <td>0.981861</td>\n",
       "      <td>0.978981</td>\n",
       "      <td>0.006172</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>80.666216</td>\n",
       "      <td>6.326312</td>\n",
       "      <td>6.861264</td>\n",
       "      <td>0.347786</td>\n",
       "      <td>50</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'svc__C': 50, 'svc__gamma': 'auto'}</td>\n",
       "      <td>0.966642</td>\n",
       "      <td>0.981913</td>\n",
       "      <td>0.982194</td>\n",
       "      <td>0.982295</td>\n",
       "      <td>0.981861</td>\n",
       "      <td>0.978981</td>\n",
       "      <td>0.006172</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>101.489403</td>\n",
       "      <td>8.386863</td>\n",
       "      <td>6.501961</td>\n",
       "      <td>0.339588</td>\n",
       "      <td>100</td>\n",
       "      <td>scale</td>\n",
       "      <td>{'svc__C': 100, 'svc__gamma': 'scale'}</td>\n",
       "      <td>0.966890</td>\n",
       "      <td>0.982412</td>\n",
       "      <td>0.982827</td>\n",
       "      <td>0.983156</td>\n",
       "      <td>0.982409</td>\n",
       "      <td>0.979539</td>\n",
       "      <td>0.006330</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>102.894329</td>\n",
       "      <td>8.646539</td>\n",
       "      <td>6.448124</td>\n",
       "      <td>0.360220</td>\n",
       "      <td>100</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'svc__C': 100, 'svc__gamma': 'auto'}</td>\n",
       "      <td>0.966890</td>\n",
       "      <td>0.982412</td>\n",
       "      <td>0.982827</td>\n",
       "      <td>0.983156</td>\n",
       "      <td>0.982409</td>\n",
       "      <td>0.979539</td>\n",
       "      <td>0.006330</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>141.993136</td>\n",
       "      <td>15.025401</td>\n",
       "      <td>6.153601</td>\n",
       "      <td>0.339881</td>\n",
       "      <td>200</td>\n",
       "      <td>scale</td>\n",
       "      <td>{'svc__C': 200, 'svc__gamma': 'scale'}</td>\n",
       "      <td>0.967983</td>\n",
       "      <td>0.982911</td>\n",
       "      <td>0.983680</td>\n",
       "      <td>0.983387</td>\n",
       "      <td>0.982955</td>\n",
       "      <td>0.980183</td>\n",
       "      <td>0.006107</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>142.180076</td>\n",
       "      <td>13.463157</td>\n",
       "      <td>6.129394</td>\n",
       "      <td>0.324919</td>\n",
       "      <td>200</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'svc__C': 200, 'svc__gamma': 'auto'}</td>\n",
       "      <td>0.967983</td>\n",
       "      <td>0.982911</td>\n",
       "      <td>0.983680</td>\n",
       "      <td>0.983387</td>\n",
       "      <td>0.982955</td>\n",
       "      <td>0.980183</td>\n",
       "      <td>0.006107</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>244.443701</td>\n",
       "      <td>31.612088</td>\n",
       "      <td>5.814389</td>\n",
       "      <td>0.338186</td>\n",
       "      <td>500</td>\n",
       "      <td>scale</td>\n",
       "      <td>{'svc__C': 500, 'svc__gamma': 'scale'}</td>\n",
       "      <td>0.969468</td>\n",
       "      <td>0.983902</td>\n",
       "      <td>0.984499</td>\n",
       "      <td>0.984479</td>\n",
       "      <td>0.984004</td>\n",
       "      <td>0.981270</td>\n",
       "      <td>0.005906</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>245.308241</td>\n",
       "      <td>30.650653</td>\n",
       "      <td>5.338797</td>\n",
       "      <td>0.920006</td>\n",
       "      <td>500</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'svc__C': 500, 'svc__gamma': 'auto'}</td>\n",
       "      <td>0.969468</td>\n",
       "      <td>0.983902</td>\n",
       "      <td>0.984499</td>\n",
       "      <td>0.984479</td>\n",
       "      <td>0.984004</td>\n",
       "      <td>0.981270</td>\n",
       "      <td>0.005906</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  ...  std_test_score  rank_test_score\n",
       "0       69.166824      2.860669  ...        0.005777               11\n",
       "1       68.927757      3.204570  ...        0.005777               11\n",
       "2       61.723366      3.873058  ...        0.006061                9\n",
       "3       62.992307      3.969130  ...        0.006061                9\n",
       "4       80.517618      6.961829  ...        0.006172                7\n",
       "5       80.666216      6.326312  ...        0.006172                7\n",
       "6      101.489403      8.386863  ...        0.006330                5\n",
       "7      102.894329      8.646539  ...        0.006330                5\n",
       "8      141.993136     15.025401  ...        0.006107                3\n",
       "9      142.180076     13.463157  ...        0.006107                3\n",
       "10     244.443701     31.612088  ...        0.005906                1\n",
       "11     245.308241     30.650653  ...        0.005906                1\n",
       "\n",
       "[12 rows x 15 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GridSearch for SVM, StandardScaler - Run on Google Colab\n",
    "parameters = {'svc__C':[1, 10, 50, 100, 200, 500], 'svc__gamma':['scale', 'auto']}\n",
    "model = Pipeline([('scaler', StandardScaler()), ('svc', SVC(random_state=42))])\n",
    "\n",
    "clf = GridSearchCV(model, parameters, scoring=\"f1\", verbose=4, n_jobs=-1)\n",
    "clf.fit(X_train, y_train)\n",
    "df = pd.DataFrame(clf.cv_results_)\n",
    "df.to_csv(\"/content/drive/MyDrive/ms4610_project/parameter_search/svm_stdscaler_param_tuning.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [SVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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_C</th>\n",
       "      <th>param_gamma</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>463.783861</td>\n",
       "      <td>81.205084</td>\n",
       "      <td>42.242590</td>\n",
       "      <td>0.849936</td>\n",
       "      <td>1</td>\n",
       "      <td>scale</td>\n",
       "      <td>{'C': 1, 'gamma': 'scale'}</td>\n",
       "      <td>0.709638</td>\n",
       "      <td>0.702923</td>\n",
       "      <td>0.707484</td>\n",
       "      <td>0.709113</td>\n",
       "      <td>0.708854</td>\n",
       "      <td>0.707602</td>\n",
       "      <td>0.002445</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1377.503292</td>\n",
       "      <td>16.153404</td>\n",
       "      <td>58.870070</td>\n",
       "      <td>0.351073</td>\n",
       "      <td>1</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'C': 1, 'gamma': 'auto'}</td>\n",
       "      <td>0.941614</td>\n",
       "      <td>0.922296</td>\n",
       "      <td>0.924946</td>\n",
       "      <td>0.919779</td>\n",
       "      <td>0.923339</td>\n",
       "      <td>0.926395</td>\n",
       "      <td>0.007793</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>401.752753</td>\n",
       "      <td>62.291967</td>\n",
       "      <td>37.603628</td>\n",
       "      <td>0.663652</td>\n",
       "      <td>10</td>\n",
       "      <td>scale</td>\n",
       "      <td>{'C': 10, 'gamma': 'scale'}</td>\n",
       "      <td>0.746713</td>\n",
       "      <td>0.742399</td>\n",
       "      <td>0.745507</td>\n",
       "      <td>0.742988</td>\n",
       "      <td>0.744192</td>\n",
       "      <td>0.744360</td>\n",
       "      <td>0.001588</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2055.733747</td>\n",
       "      <td>226.270449</td>\n",
       "      <td>56.637838</td>\n",
       "      <td>0.399267</td>\n",
       "      <td>10</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'C': 10, 'gamma': 'auto'}</td>\n",
       "      <td>0.947184</td>\n",
       "      <td>0.930161</td>\n",
       "      <td>0.931272</td>\n",
       "      <td>0.927914</td>\n",
       "      <td>0.930486</td>\n",
       "      <td>0.933403</td>\n",
       "      <td>0.006980</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>339.430520</td>\n",
       "      <td>11.105198</td>\n",
       "      <td>35.606125</td>\n",
       "      <td>0.393805</td>\n",
       "      <td>50</td>\n",
       "      <td>scale</td>\n",
       "      <td>{'C': 50, 'gamma': 'scale'}</td>\n",
       "      <td>0.764183</td>\n",
       "      <td>0.761439</td>\n",
       "      <td>0.768448</td>\n",
       "      <td>0.765451</td>\n",
       "      <td>0.766335</td>\n",
       "      <td>0.765171</td>\n",
       "      <td>0.002327</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2058.680929</td>\n",
       "      <td>232.434636</td>\n",
       "      <td>56.747633</td>\n",
       "      <td>0.471007</td>\n",
       "      <td>50</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'C': 50, 'gamma': 'auto'}</td>\n",
       "      <td>0.947184</td>\n",
       "      <td>0.930204</td>\n",
       "      <td>0.931316</td>\n",
       "      <td>0.927914</td>\n",
       "      <td>0.930492</td>\n",
       "      <td>0.933422</td>\n",
       "      <td>0.006973</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>319.650438</td>\n",
       "      <td>9.583468</td>\n",
       "      <td>34.341377</td>\n",
       "      <td>0.265583</td>\n",
       "      <td>100</td>\n",
       "      <td>scale</td>\n",
       "      <td>{'C': 100, 'gamma': 'scale'}</td>\n",
       "      <td>0.779614</td>\n",
       "      <td>0.774060</td>\n",
       "      <td>0.782112</td>\n",
       "      <td>0.779799</td>\n",
       "      <td>0.778215</td>\n",
       "      <td>0.778760</td>\n",
       "      <td>0.002662</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2048.400970</td>\n",
       "      <td>235.036565</td>\n",
       "      <td>56.376540</td>\n",
       "      <td>0.360202</td>\n",
       "      <td>100</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'C': 100, 'gamma': 'auto'}</td>\n",
       "      <td>0.947184</td>\n",
       "      <td>0.930204</td>\n",
       "      <td>0.931316</td>\n",
       "      <td>0.927914</td>\n",
       "      <td>0.930492</td>\n",
       "      <td>0.933422</td>\n",
       "      <td>0.006973</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>299.651059</td>\n",
       "      <td>9.396462</td>\n",
       "      <td>32.864570</td>\n",
       "      <td>0.158337</td>\n",
       "      <td>200</td>\n",
       "      <td>scale</td>\n",
       "      <td>{'C': 200, 'gamma': 'scale'}</td>\n",
       "      <td>0.794390</td>\n",
       "      <td>0.788654</td>\n",
       "      <td>0.796328</td>\n",
       "      <td>0.794311</td>\n",
       "      <td>0.791212</td>\n",
       "      <td>0.792979</td>\n",
       "      <td>0.002713</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2066.440012</td>\n",
       "      <td>227.764149</td>\n",
       "      <td>57.039270</td>\n",
       "      <td>0.441291</td>\n",
       "      <td>200</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'C': 200, 'gamma': 'auto'}</td>\n",
       "      <td>0.947184</td>\n",
       "      <td>0.930204</td>\n",
       "      <td>0.931316</td>\n",
       "      <td>0.927914</td>\n",
       "      <td>0.930492</td>\n",
       "      <td>0.933422</td>\n",
       "      <td>0.006973</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>283.321423</td>\n",
       "      <td>10.093653</td>\n",
       "      <td>30.528316</td>\n",
       "      <td>0.165063</td>\n",
       "      <td>500</td>\n",
       "      <td>scale</td>\n",
       "      <td>{'C': 500, 'gamma': 'scale'}</td>\n",
       "      <td>0.809028</td>\n",
       "      <td>0.802779</td>\n",
       "      <td>0.811524</td>\n",
       "      <td>0.810729</td>\n",
       "      <td>0.804884</td>\n",
       "      <td>0.807789</td>\n",
       "      <td>0.003396</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1854.907218</td>\n",
       "      <td>215.167743</td>\n",
       "      <td>48.295579</td>\n",
       "      <td>7.734666</td>\n",
       "      <td>500</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'C': 500, 'gamma': 'auto'}</td>\n",
       "      <td>0.947184</td>\n",
       "      <td>0.930204</td>\n",
       "      <td>0.931316</td>\n",
       "      <td>0.927914</td>\n",
       "      <td>0.930492</td>\n",
       "      <td>0.933422</td>\n",
       "      <td>0.006973</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 param_C  \\\n",
       "0      463.783861     81.205084        42.242590        0.849936       1   \n",
       "1     1377.503292     16.153404        58.870070        0.351073       1   \n",
       "2      401.752753     62.291967        37.603628        0.663652      10   \n",
       "3     2055.733747    226.270449        56.637838        0.399267      10   \n",
       "4      339.430520     11.105198        35.606125        0.393805      50   \n",
       "5     2058.680929    232.434636        56.747633        0.471007      50   \n",
       "6      319.650438      9.583468        34.341377        0.265583     100   \n",
       "7     2048.400970    235.036565        56.376540        0.360202     100   \n",
       "8      299.651059      9.396462        32.864570        0.158337     200   \n",
       "9     2066.440012    227.764149        57.039270        0.441291     200   \n",
       "10     283.321423     10.093653        30.528316        0.165063     500   \n",
       "11    1854.907218    215.167743        48.295579        7.734666     500   \n",
       "\n",
       "   param_gamma                        params  split0_test_score  \\\n",
       "0        scale    {'C': 1, 'gamma': 'scale'}           0.709638   \n",
       "1         auto     {'C': 1, 'gamma': 'auto'}           0.941614   \n",
       "2        scale   {'C': 10, 'gamma': 'scale'}           0.746713   \n",
       "3         auto    {'C': 10, 'gamma': 'auto'}           0.947184   \n",
       "4        scale   {'C': 50, 'gamma': 'scale'}           0.764183   \n",
       "5         auto    {'C': 50, 'gamma': 'auto'}           0.947184   \n",
       "6        scale  {'C': 100, 'gamma': 'scale'}           0.779614   \n",
       "7         auto   {'C': 100, 'gamma': 'auto'}           0.947184   \n",
       "8        scale  {'C': 200, 'gamma': 'scale'}           0.794390   \n",
       "9         auto   {'C': 200, 'gamma': 'auto'}           0.947184   \n",
       "10       scale  {'C': 500, 'gamma': 'scale'}           0.809028   \n",
       "11        auto   {'C': 500, 'gamma': 'auto'}           0.947184   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0            0.702923           0.707484           0.709113   \n",
       "1            0.922296           0.924946           0.919779   \n",
       "2            0.742399           0.745507           0.742988   \n",
       "3            0.930161           0.931272           0.927914   \n",
       "4            0.761439           0.768448           0.765451   \n",
       "5            0.930204           0.931316           0.927914   \n",
       "6            0.774060           0.782112           0.779799   \n",
       "7            0.930204           0.931316           0.927914   \n",
       "8            0.788654           0.796328           0.794311   \n",
       "9            0.930204           0.931316           0.927914   \n",
       "10           0.802779           0.811524           0.810729   \n",
       "11           0.930204           0.931316           0.927914   \n",
       "\n",
       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0            0.708854         0.707602        0.002445               12  \n",
       "1            0.923339         0.926395        0.007793                6  \n",
       "2            0.744192         0.744360        0.001588               11  \n",
       "3            0.930486         0.933403        0.006980                5  \n",
       "4            0.766335         0.765171        0.002327               10  \n",
       "5            0.930492         0.933422        0.006973                1  \n",
       "6            0.778215         0.778760        0.002662                9  \n",
       "7            0.930492         0.933422        0.006973                1  \n",
       "8            0.791212         0.792979        0.002713                8  \n",
       "9            0.930492         0.933422        0.006973                1  \n",
       "10           0.804884         0.807789        0.003396                7  \n",
       "11           0.930492         0.933422        0.006973                1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GridSearch for SVM\n",
    "parameters = {'C':[1, 10, 50, 100, 200, 500], 'gamma':['scale', 'auto']}\n",
    "model = SVC(random_state=42)\n",
    "\n",
    "clf = GridSearchCV(model, parameters, scoring=\"f1\")\n",
    "df = pd.DataFrame(clf.cv_results_)\n",
    "df.to_csv(\"parameter_search/svm_param_tuning.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Consolidating the best set of parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "knn_stdscaler_param_tuning {'knn__n_neighbors': 3} \n",
      "\n",
      "adaboost_param_tuning {'algorithm': 'SAMME.R'} \n",
      "\n",
      "decisiontree_param_tuning {'criterion': 'entropy', 'max_depth': None, 'min_samples_split': 2, 'splitter': 'best'} \n",
      "\n",
      "knn_param_tuning {'n_neighbors': 3} \n",
      "\n",
      "rf_param_tuning {'max_depth': None, 'min_samples_split': 2} \n",
      "\n",
      "logistic_param_tuning {'C': 200, 'penalty': 'l2', 'solver': 'newton-cg'} \n",
      "\n",
      "mlp_param_tuning {'early_stopping': True, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant'} {'early_stopping': True, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive'} \n",
      "\n",
      "mlp_stdscaler_param_tuning {'mlp__early_stopping': False, 'mlp__hidden_layer_sizes': (100,), 'mlp__learning_rate': 'constant'} {'mlp__early_stopping': False, 'mlp__hidden_layer_sizes': (100,), 'mlp__learning_rate': 'adaptive'} \n",
      "\n",
      "svm_stdscaler_param_tuning {'svc__C': 500, 'svc__gamma': 'scale'} {'svc__C': 500, 'svc__gamma': 'auto'} \n",
      "\n",
      "sgd_param_tuning {'eta0': 0.5, 'learning_rate': 'adaptive', 'penalty': 'l2'} \n",
      "\n",
      "svm_param_tuning {'C': 50, 'gamma': 'auto'} {'C': 100, 'gamma': 'auto'} {'C': 200, 'gamma': 'auto'} {'C': 500, 'gamma': 'auto'} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "files = os.listdir(\"parameter_search/\")\n",
    "for file in files:\n",
    "    df = pd.read_csv(\"parameter_search/\"+file, index_col=0).sort_values(by=[\"rank_test_score\"])\n",
    "    print(file.split(\".\")[0], *df[df[\"rank_test_score\"]==1][\"params\"], \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "knn_stdscaler_param_tuning.csv\n"
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       "   mean_test_score  std_test_score\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.802195</td>\n",
       "      <td>0.002196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_test_score  std_test_score\n",
       "57         0.802195        0.002196"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "svm_param_tuning.csv\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.933422</td>\n",
       "      <td>0.006973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.933422</td>\n",
       "      <td>0.006973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.933422</td>\n",
       "      <td>0.006973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.933422</td>\n",
       "      <td>0.006973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_test_score  std_test_score\n",
       "5          0.933422        0.006973\n",
       "7          0.933422        0.006973\n",
       "9          0.933422        0.006973\n",
       "11         0.933422        0.006973"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = []\n",
    "mean_score = []\n",
    "std_score = []\n",
    "\n",
    "for file in files:\n",
    "    print(file)\n",
    "    df = pd.read_csv(\"parameter_search/\"+file, index_col=0)\n",
    "    display(df[df[\"rank_test_score\"]==1][[\"mean_test_score\", \"std_test_score\"]])\n",
    "    models.append(file.split(\"_param_tuning.csv\")[0])\n",
    "    mean_score.append(df[df[\"rank_test_score\"]==1][\"mean_test_score\"].unique()[0])\n",
    "    std_score.append(df[df[\"rank_test_score\"]==1][\"std_test_score\"].unique()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_old = [i.upper() for i in models]\n",
    "x = []\n",
    "for i in x_old:\n",
    "    if len(i.split(\"_\")) == 2:\n",
    "        x.append(i.split(\"_\")[0]+\"_SCALED\")\n",
    "    else:\n",
    "        x.append(i)\n",
    "        \n",
    "y = [i*100 for i in mean_score]\n",
    "yerr = [i*100 for i in std_score]\n",
    "\n",
    "plt.figure(figsize=[15,8])\n",
    "plt.bar(x, y, yerr=yerr, color=\"dodgerblue\", alpha=0.5, edgecolor=\"darkslategray\")\n",
    "plt.xlabel(\"Models\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Accuracy of the models (best parameter set) on Training data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Models':x, 'Mean Accuracy':y, 'Std Accuracy':yerr})\n",
    "df.sort_values(by=[\"Mean Accuracy\"], inplace=True)\n",
    "df.plot.bar(x=\"Models\", y=\"Mean Accuracy\", yerr=\"Std Accuracy\", color=\"dodgerblue\", alpha=0.5, \n",
    "            edgecolor=\"darkslategray\", title=\"F1 Score of the models (best parameter set) on Training data\", legend=False, figsize=[15,6], rot=0, grid=True, ylim=[0,100])\n",
    "plt.ylabel(\"F1 Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Models</th>\n",
       "      <th>Mean Accuracy</th>\n",
       "      <th>Std Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SGD</td>\n",
       "      <td>80.219525</td>\n",
       "      <td>0.219607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MLP</td>\n",
       "      <td>85.164759</td>\n",
       "      <td>1.746550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LOGISTIC</td>\n",
       "      <td>87.427499</td>\n",
       "      <td>0.912857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADABOOST</td>\n",
       "      <td>92.662860</td>\n",
       "      <td>1.051996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SVM</td>\n",
       "      <td>93.342191</td>\n",
       "      <td>0.697291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNN</td>\n",
       "      <td>94.592194</td>\n",
       "      <td>0.127961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DECISIONTREE</td>\n",
       "      <td>97.744500</td>\n",
       "      <td>0.510452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SVM_SCALED</td>\n",
       "      <td>98.127042</td>\n",
       "      <td>0.590630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN_SCALED</td>\n",
       "      <td>98.317364</td>\n",
       "      <td>0.318184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RF</td>\n",
       "      <td>98.626446</td>\n",
       "      <td>0.514454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MLP_SCALED</td>\n",
       "      <td>98.738634</td>\n",
       "      <td>0.424358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Models  Mean Accuracy  Std Accuracy\n",
       "9            SGD      80.219525      0.219607\n",
       "6            MLP      85.164759      1.746550\n",
       "5       LOGISTIC      87.427499      0.912857\n",
       "1       ADABOOST      92.662860      1.051996\n",
       "10           SVM      93.342191      0.697291\n",
       "3            KNN      94.592194      0.127961\n",
       "2   DECISIONTREE      97.744500      0.510452\n",
       "8     SVM_SCALED      98.127042      0.590630\n",
       "0     KNN_SCALED      98.317364      0.318184\n",
       "4             RF      98.626446      0.514454\n",
       "7     MLP_SCALED      98.738634      0.424358"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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