Lectura_15_Cross_Validation.ipynb

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    "# ***Cross-Validation***\n",
    "\n",
    "## ***Universidad Central***\n",
    "\n",
    "## ***Maestría en analítica de datos***\n",
    "\n",
    "## ***Métodos estadísticos para analítica de datos.***\n",
    "## ***Docente: Luis Andrés Campos Maldonado.***"
   ]
  },
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   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pprint import pprint\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder, label_binarize\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_auc_score, roc_curve, auc\n",
    "from sklearn.model_selection import (\n",
    "    GridSearchCV,\n",
    "    RandomizedSearchCV,\n",
    "    train_test_split,\n",
    "    cross_validate\n",
    ")\n",
    "\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.rcParams[\"figure.figsize\"] = (15,6)\n",
    "url_base = \"https://raw.githubusercontent.com/lacamposm/Metodos-Estadisticos/main/data/\""
   ]
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   "cell_type": "markdown",
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    "id": "ZAG2MCUJBU26"
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   "source": [
    "## ***Cross-Validation.***\n",
    "\n",
    "Pensemos en la siguiente situación de la vida \"real\":\n",
    "\n",
    "_Usted está estudiando para un examen y ha logrado memorizar muchos de los ejercicios que ha desarrollado como preparación, dado que usted \"memorizó\" (no-generalizó) seguramente en un exámen con las mismas preguntas (que usted ya \"memorizó\") tendrá un buen resultado. Siguiendo en la analogía, ¿cómo cree que será su calificación si el exámen tiene un grupo de preguntas que usted nunca ha estudiado? Recuerde que solo \"memorizó\"..._"
   ]
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   "source": [
    "\n",
    "\n",
    "Suponga ahora que está construyendo un modelo de `machine learning`, ¿cómo puede eliminar el problema  de \"memorizar\" del sistema? Intuitivamente, parece completamente lógico dejar un conjunto de datos reservados (`set_test`) que NO hará parte del entrenamiento del modelo y luego de entrenado dicho modelo aplicarlo sobre los datos reservados para ver que tan bien se comporta. En este caso, usaría la mayoría de los datos para ajustar el modelo (entrenar) y usar una porción más pequeña para probar el modelo. ¿Qué tan diferente sería la evaluación si selecciona una muestra reservada diferente?\n",
    "\n",
    "\n",
    "La técnica de _**[cross validation](https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation)**_ amplía la idea de una muestra reservada a múltiples muestras reservas secuenciales. \n",
    "\n",
    "1. Reserve $1/k$ de los datos como muestra reservada.\n",
    "\n",
    "2. Entrene el modelo con los datos restantes.\n",
    "\n",
    "3. Aplique (score) el modelo a la reserva de $1/k$ y registre la métrica(s) de evaluación del modelo que desea.\n",
    "\n",
    "4. Restaure el primer $1/k$ de los datos y reserve el siguiente $1/k$ (excluyendo cualquier registro que fue elegido la primera vez).\n",
    "\n",
    "5. Repita los pasos 2 y 3.\n",
    "\n",
    "6. Repita hasta que se haya utilizado cada registro en la parte reservada.\n",
    "\n",
    "7. Promediar o combinar las métricas de evaluación del modelo.\n",
    "\n",
    "La división de los datos en la muestra de entrenamiento y la muestra reservada también se denomina\n",
    "un ***fold.***\n",
    "\n",
    "Para hacer uso de esta técnica podemos usar las siguientes herramientas:\n",
    "\n",
    "1. [`sklearn.model_selection.StratifiedKFold`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html)\n",
    "\n",
    "2. [`sklearn.model_selection.KFold`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html)\n",
    "\n",
    "3. [`sklearn.model_selection.cross_validate`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html)"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>estado_del_prestamo</th>\n",
       "      <th>ingreso_anual</th>\n",
       "      <th>anios_de_experiencia_laboral</th>\n",
       "      <th>tenencia_de_vivienda</th>\n",
       "      <th>tasa_de_interes</th>\n",
       "      <th>monto_del_prestamo</th>\n",
       "      <th>proposito</th>\n",
       "      <th>plazo</th>\n",
       "      <th>calificacion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>completamente_pagado</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>10+ años</td>\n",
       "      <td>alquiler</td>\n",
       "      <td>10.65</td>\n",
       "      <td>5000</td>\n",
       "      <td>tarjeta_de_credito</td>\n",
       "      <td>36 meses</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dado_de_baja</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>&lt; 1 año</td>\n",
       "      <td>alquiler</td>\n",
       "      <td>15.27</td>\n",
       "      <td>2500</td>\n",
       "      <td>auto</td>\n",
       "      <td>60 meses</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>completamente_pagado</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>10+ años</td>\n",
       "      <td>alquiler</td>\n",
       "      <td>15.96</td>\n",
       "      <td>2400</td>\n",
       "      <td>pequeno_negocio</td>\n",
       "      <td>36 meses</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>completamente_pagado</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>10+ años</td>\n",
       "      <td>alquiler</td>\n",
       "      <td>13.49</td>\n",
       "      <td>10000</td>\n",
       "      <td>otro</td>\n",
       "      <td>36 meses</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>completamente_pagado</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>1 año</td>\n",
       "      <td>alquiler</td>\n",
       "      <td>12.69</td>\n",
       "      <td>3000</td>\n",
       "      <td>otro</td>\n",
       "      <td>60 meses</td>\n",
       "      <td>B</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38700</th>\n",
       "      <td>completamente_pagado</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>4 años</td>\n",
       "      <td>hipoteca</td>\n",
       "      <td>8.07</td>\n",
       "      <td>2500</td>\n",
       "      <td>remodelacion_del_hogar</td>\n",
       "      <td>36 meses</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38701</th>\n",
       "      <td>completamente_pagado</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>3 años</td>\n",
       "      <td>alquiler</td>\n",
       "      <td>10.28</td>\n",
       "      <td>8500</td>\n",
       "      <td>tarjeta_de_credito</td>\n",
       "      <td>36 meses</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38702</th>\n",
       "      <td>completamente_pagado</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>&lt; 1 año</td>\n",
       "      <td>hipoteca</td>\n",
       "      <td>8.07</td>\n",
       "      <td>5000</td>\n",
       "      <td>consolidacion_de_deudas</td>\n",
       "      <td>36 meses</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38703</th>\n",
       "      <td>completamente_pagado</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>&lt; 1 año</td>\n",
       "      <td>hipoteca</td>\n",
       "      <td>7.43</td>\n",
       "      <td>5000</td>\n",
       "      <td>otro</td>\n",
       "      <td>36 meses</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38704</th>\n",
       "      <td>completamente_pagado</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>&lt; 1 año</td>\n",
       "      <td>propia</td>\n",
       "      <td>13.75</td>\n",
       "      <td>7500</td>\n",
       "      <td>consolidacion_de_deudas</td>\n",
       "      <td>36 meses</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38689 rows × 9 columns</p>\n",
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      "text/plain": [
       "        estado_del_prestamo  ingreso_anual anios_de_experiencia_laboral  \\\n",
       "0      completamente_pagado        24000.0                     10+ años   \n",
       "1              dado_de_baja        30000.0                      < 1 año   \n",
       "2      completamente_pagado        12252.0                     10+ años   \n",
       "3      completamente_pagado        49200.0                     10+ años   \n",
       "4      completamente_pagado        80000.0                        1 año   \n",
       "...                     ...            ...                          ...   \n",
       "38700  completamente_pagado       110000.0                       4 años   \n",
       "38701  completamente_pagado        18000.0                       3 años   \n",
       "38702  completamente_pagado       100000.0                      < 1 año   \n",
       "38703  completamente_pagado       200000.0                      < 1 año   \n",
       "38704  completamente_pagado        22000.0                      < 1 año   \n",
       "\n",
       "      tenencia_de_vivienda  tasa_de_interes  monto_del_prestamo  \\\n",
       "0                 alquiler            10.65                5000   \n",
       "1                 alquiler            15.27                2500   \n",
       "2                 alquiler            15.96                2400   \n",
       "3                 alquiler            13.49               10000   \n",
       "4                 alquiler            12.69                3000   \n",
       "...                    ...              ...                 ...   \n",
       "38700             hipoteca             8.07                2500   \n",
       "38701             alquiler            10.28                8500   \n",
       "38702             hipoteca             8.07                5000   \n",
       "38703             hipoteca             7.43                5000   \n",
       "38704               propia            13.75                7500   \n",
       "\n",
       "                     proposito      plazo calificacion  \n",
       "0           tarjeta_de_credito   36 meses            B  \n",
       "1                         auto   60 meses            C  \n",
       "2              pequeno_negocio   36 meses            C  \n",
       "3                         otro   36 meses            C  \n",
       "4                         otro   60 meses            B  \n",
       "...                        ...        ...          ...  \n",
       "38700   remodelacion_del_hogar   36 meses            A  \n",
       "38701       tarjeta_de_credito   36 meses            C  \n",
       "38702  consolidacion_de_deudas   36 meses            A  \n",
       "38703                     otro   36 meses            A  \n",
       "38704  consolidacion_de_deudas   36 meses            E  \n",
       "\n",
       "[38689 rows x 9 columns]"
      ]
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preparamos la Data\n",
    "df_to_model = pd.read_parquet(url_base + \"Logistic_Regression_1.parquet\",)\n",
    "df_to_model.drop_duplicates(inplace=True)\n",
    "df_to_model[\"tasa_de_interes\"] = df_to_model[\"tasa_de_interes\"].str.replace(\"%\", \"\").astype(\"float\")\n",
    "df_to_model.drop(columns=[\"estado_de_verificacion\"], inplace=True)\n",
    "df_to_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features = [col for col in df_to_model.select_dtypes(exclude=np.number).columns if col != \"estado_del_prestamo\"]\n",
    "numeric_features = [col for col in df_to_model.columns if col not in categorical_features and col != \"estado_del_prestamo\"]\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\"onehot\", OneHotEncoder(drop=\"first\"), categorical_features),\n",
    "        (\"num\", \"passthrough\", numeric_features)        \n",
    "    ]\n",
    ")\n",
    "\n",
    "X = df_to_model[categorical_features + numeric_features]\n",
    "X = preprocessor.fit_transform(X)\n",
    "y = label_binarize(df_to_model[\"estado_del_prestamo\"], classes=[\"dado_de_baja\", \"completamente_pagado\"])\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ***Modelo Logistic Regression.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression AUC Train = 0.692\n",
      "Logistic Regression AUC Test = 0.688\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression con sklearn.\n",
    "model_lr = LogisticRegression(C=1e10, solver=\"newton-cg\", fit_intercept=True, random_state=123)\n",
    "model_lr.fit(X_train,y_train)\n",
    "\n",
    "predict_proba_train = model_lr.predict_proba(X_train)[:,1]\n",
    "predict_proba_test = model_lr.predict_proba(X_test)[:,1]\n",
    "\n",
    "print(f\"Logistic Regression AUC Train = {roc_auc_score(y_train, predict_proba_train):.3f}\")\n",
    "print(f\"Logistic Regression AUC Test = {roc_auc_score(y_test, predict_proba_test):.3f}\")"
   ]
  },
  {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_roc_auc</th>\n",
       "      <th>train_roc_auc</th>\n",
       "      <th>test_f1</th>\n",
       "      <th>train_f1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.472225</td>\n",
       "      <td>0.010654</td>\n",
       "      <td>0.695260</td>\n",
       "      <td>0.690147</td>\n",
       "      <td>0.924080</td>\n",
       "      <td>0.924381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.239501</td>\n",
       "      <td>0.010728</td>\n",
       "      <td>0.669770</td>\n",
       "      <td>0.696917</td>\n",
       "      <td>0.923786</td>\n",
       "      <td>0.924422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.684600</td>\n",
       "      <td>0.005953</td>\n",
       "      <td>0.705949</td>\n",
       "      <td>0.687860</td>\n",
       "      <td>0.924389</td>\n",
       "      <td>0.924411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.066561</td>\n",
       "      <td>0.005805</td>\n",
       "      <td>0.685432</td>\n",
       "      <td>0.692836</td>\n",
       "      <td>0.924441</td>\n",
       "      <td>0.924251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.169047</td>\n",
       "      <td>0.004714</td>\n",
       "      <td>0.680731</td>\n",
       "      <td>0.694208</td>\n",
       "      <td>0.924509</td>\n",
       "      <td>0.924325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_roc_auc  train_roc_auc   test_f1  train_f1\n",
       "0  2.472225    0.010654      0.695260       0.690147  0.924080  0.924381\n",
       "1  2.239501    0.010728      0.669770       0.696917  0.923786  0.924422\n",
       "2  2.684600    0.005953      0.705949       0.687860  0.924389  0.924411\n",
       "3  1.066561    0.005805      0.685432       0.692836  0.924441  0.924251\n",
       "4  1.169047    0.004714      0.680731       0.694208  0.924509  0.924325"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_results_lr = cross_validate(\n",
    "    model_lr,\n",
    "    X_train,\n",
    "    y_train,\n",
    "    cv=5,\n",
    "    scoring=(\"roc_auc\",\"f1\"),\n",
    "    return_train_score=True,\n",
    ")\n",
    "\n",
    "pd.DataFrame(cv_results_lr)"
   ]
  },
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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_lr, test_lr = cv_results_lr[\"train_roc_auc\"], cv_results_lr[\"test_roc_auc\"]\n",
    "means_lr = (train_lr.mean(), test_lr.mean())\n",
    "# Para los límites en el eje y\n",
    "values_aucs_lr = np.concatenate((train_lr, test_lr), axis=None)\n",
    "min_y, max_y = values_aucs_lr.min() -0.01 , values_aucs_lr.max()+ 0.01\n",
    "##\n",
    "plt.plot(cv_results_lr[\"train_roc_auc\"], label=\"Train_data\")\n",
    "plt.plot(cv_results_lr[\"test_roc_auc\"], label = \"Test_data\")\n",
    "plt.title(\"Logistic Regression AUC - 5-fold cross-validation\")\n",
    "plt.legend()\n",
    "plt.text(0, min_y + 4e-03, f\"Mean-AUC in Train: {means_lr[0]:.4f}\")\n",
    "plt.text(0, min_y + 2e-03, f\"Mean-AUC in Test:  {means_lr[1]:.4f}\")\n",
    "plt.ylim(min_y, max_y)\n",
    "plt.xticks(np.arange(0,5), np.arange(1,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y2Jc3HLnEy-l"
   },
   "source": [
    "## ***Modelo Random Forrest.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 800,
     "status": "ok",
     "timestamp": 1668713450797,
     "user": {
      "displayName": "Luis Andres Campos Maldonado",
      "userId": "15478348060554261231"
     },
     "user_tz": 300
    },
    "id": "vYM_p9KTGpfc",
    "outputId": "16c70261-c838-430e-d722-46987883c55d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forrest AUC Train = 0.683\n",
      "Random Forrest AUC Test = 0.685\n"
     ]
    }
   ],
   "source": [
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\"onehot\", OneHotEncoder(), categorical_features),\n",
    "        (\"num\", \"passthrough\", numeric_features)        \n",
    "    ]\n",
    ")\n",
    "\n",
    "X = df_to_model[categorical_features + numeric_features]\n",
    "X = preprocessor.fit_transform(X)\n",
    "y = label_binarize(df_to_model[\"estado_del_prestamo\"], classes=[\"dado_de_baja\", \"completamente_pagado\"])\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=123)\n",
    "\n",
    "model_rf = RandomForestClassifier(n_estimators=100, max_depth=3, random_state=123)\n",
    "model_rf.fit(X_train,y_train)\n",
    "\n",
    "predict_proba_train = model_rf.predict_proba(X_train)[:,1]\n",
    "predict_proba_test = model_rf.predict_proba(X_test)[:,1]\n",
    "\n",
    "print(f\"Random Forrest AUC Train = {roc_auc_score(y_train, predict_proba_train):.3f}\")\n",
    "print(f\"Random Forrest AUC Test = {roc_auc_score(y_test, predict_proba_test):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 5711,
     "status": "ok",
     "timestamp": 1668713456505,
     "user": {
      "displayName": "Luis Andres Campos Maldonado",
      "userId": "15478348060554261231"
     },
     "user_tz": 300
    },
    "id": "i3yRzSwQHCLV"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END  f1: (train=0.924, test=0.924) roc_auc: (train=0.685, test=0.677) total time=   1.2s\n",
      "[CV] END  f1: (train=0.924, test=0.924) roc_auc: (train=0.689, test=0.661) total time=   1.1s\n",
      "[CV] END  f1: (train=0.924, test=0.924) roc_auc: (train=0.682, test=0.694) total time=   0.9s\n",
      "[CV] END  f1: (train=0.924, test=0.924) roc_auc: (train=0.684, test=0.681) total time=   0.7s\n",
      "[CV] END  f1: (train=0.924, test=0.924) roc_auc: (train=0.685, test=0.681) total time=   0.7s\n"
     ]
    }
   ],
   "source": [
    "# cross_validate en RandomForrest.\n",
    "cv_results_rf = cross_validate(\n",
    "    model_rf,\n",
    "    X_train,\n",
    "    y_train,\n",
    "    cv=5,\n",
    "    scoring=(\"roc_auc\",\"f1\"),\n",
    "    return_train_score=True,\n",
    "    verbose=3\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "executionInfo": {
     "elapsed": 27,
     "status": "ok",
     "timestamp": 1668713456507,
     "user": {
      "displayName": "Luis Andres Campos Maldonado",
      "userId": "15478348060554261231"
     },
     "user_tz": 300
    },
    "id": "eMDvsWhzJIqC",
    "outputId": "6ce3c2f1-fa2e-4db6-a0b7-59a81382c451"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_lr, test_lr = cv_results_rf[\"train_roc_auc\"], cv_results_rf[\"test_roc_auc\"]\n",
    "\n",
    "values_aucs_lr = np.concatenate((train_lr, test_lr), axis=None)\n",
    "min_y, max_y = values_aucs_lr.min() -0.01 , values_aucs_lr.max()+ 0.01\n",
    "means_rf = (train_lr.mean(), test_lr.mean())\n",
    "\n",
    "plt.plot(cv_results_rf[\"train_roc_auc\"], label = \"Train_data\")\n",
    "plt.plot(cv_results_rf[\"test_roc_auc\"], label = \"Test_data\")\n",
    "plt.title(\"Random Forrest-AUC - 5-fold cross-validation\")\n",
    "plt.legend()\n",
    "plt.text(0, min_y + 4e-03, f\"Mean-AUC in Train: {means_rf[0]:.4f}\")\n",
    "plt.text(0, min_y + 2e-03, f\"Mean-AUC in Test:  {means_rf[1]:.4f}\")\n",
    "plt.ylim(min_y, max_y)\n",
    "plt.xticks(np.arange(0,5),np.arange(1,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5cJIOqMQNaIb"
   },
   "source": [
    "## ***Búsqueda de parámetros.***\n",
    "\n",
    "Vamos a hacer uso de: [`sklearn.model_selection.GridSearchCV`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mejores parametros:\n",
      "\n",
      "{'criterion': 'gini', 'max_depth': 5, 'min_samples_split': 150, 'n_estimators': 75}\n",
      "Random Forrest AUC (Train)= 0.6939\n",
      "Random Forrest AUC (Test)= 0.6899\n"
     ]
    }
   ],
   "source": [
    "# 1 minuto 30 segundos aprox. corriendo.\n",
    "rf_model = RandomForestClassifier()\n",
    "params = {\n",
    "    \"n_estimators\": [50, 75, 100, 150, 200], \n",
    "    \"max_depth\": [3, 4, 5], \n",
    "    \"criterion\": [\"gini\", \"entropy\"],\n",
    "    \"min_samples_split\" : [150, 200, 300]\n",
    "}\n",
    "\n",
    "clf_rf = GridSearchCV(\n",
    "    rf_model, \n",
    "    params, \n",
    "    scoring=\"roc_auc\", \n",
    "    cv=3, \n",
    "    n_jobs=-1,\n",
    "    return_train_score=True\n",
    ")\n",
    "\n",
    "best_model_grid = clf_rf.fit(X_train,  y_train.ravel())\n",
    "print(\"Mejores parametros:\\n\")\n",
    "print(best_model_grid.best_params_)\n",
    "\n",
    "print(f'Random Forrest AUC (Train)= {roc_auc_score(y_train, best_model_grid.predict_proba(X_train)[:,1]):.4f}')\n",
    "print(f'Random Forrest AUC (Test)= {roc_auc_score(y_test, best_model_grid.predict_proba(X_test)[:,1]):.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merojes parametros GridSearchCV:\n",
      "\n",
      "{'criterion': 'gini',\n",
      " 'max_depth': 5,\n",
      " 'min_samples_split': 150,\n",
      " 'n_estimators': 75}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Merojes parametros GridSearchCV:\\n\")\n",
    "pprint(clf_rf.best_params_)\n",
    "clf_best = clf_rf.best_estimator_.fit(X_train,y_train)\n",
    "\n",
    "# Curva de ROC en el set de Train.\n",
    "probas_train = clf_best.predict_proba(X_train)[:,1]\n",
    "fpr, tpr, _ = roc_curve(y_train, probas_train)\n",
    "plt.plot(fpr, tpr, label=f\"Random Forrest  (AUC Train = {auc(fpr, tpr):.4f})\", color=\"r\")\n",
    "\n",
    "# Curva de ROC en el set de test.\n",
    "probas_test = clf_best.predict_proba(X_test)[:,1]\n",
    "fpr_test, tpr_test, _ = roc_curve(y_test, probas_test)\n",
    "plt.plot(fpr_test, tpr_test, label=f\"Random Forrest (AUC test = {auc(fpr_test, tpr_test):.4f})\", color=\"b\")\n",
    "\n",
    "plt.plot((0,1), (0,1), ls = \"--\", color = \"grey\", label = \"Randow guess\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.title(\"Receiver operating characteristic\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a hacer uso de: [`sklearn.model_selection.RandomizedSearchCV`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 153, 'min_samples_split': 200, 'max_depth': 12, 'criterion': 'entropy'}\n",
      "Random Forrest AUC (Train)= 0.7343\n",
      "Random Forrest AUC (Test)= 0.6934\n"
     ]
    }
   ],
   "source": [
    "rf_model = RandomForestClassifier()\n",
    "\n",
    "params = {\n",
    "    \"max_depth\": [n for n in range(2, 15)], \n",
    "    \"criterion\": [\"gini\", \"entropy\", \"log_loss\"], \n",
    "    \"min_samples_split\" : [100 + 10*n for n in range(0, 28, 5)],\n",
    "    \"n_estimators\":[100 + n for n in range(5, 101, 2)]\n",
    "}\n",
    "\n",
    "clf_rf2 = RandomizedSearchCV(\n",
    "    rf_model,\n",
    "    params,\n",
    "    random_state=0,\n",
    "    scoring=\"roc_auc\",\n",
    "    cv=5,\n",
    "    n_iter=10,\n",
    "    n_jobs=-1,\n",
    "    return_train_score=True\n",
    ")\n",
    "\n",
    "best_model_random = clf_rf2.fit(X_train, y_train.ravel())\n",
    "print(best_model_random.best_params_)\n",
    "\n",
    "print(f'Random Forrest AUC (Train)= {roc_auc_score(y_train, best_model_random.predict_proba(X_train)[:,1]):.4f}')\n",
    "print(f'Random Forrest AUC (Test)= {roc_auc_score(y_test, best_model_random.predict_proba(X_test)[:,1]):.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_to_model[categorical_features + numeric_features]\n",
    "y = label_binarize(df_to_model[\"estado_del_prestamo\"], classes=[\"dado_de_baja\", \"completamente_pagado\"])\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=123)\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\"onehot\", OneHotEncoder(), categorical_features),\n",
    "        (\"num\", \"passthrough\", numeric_features)        \n",
    "    ]\n",
    ")\n",
    "\n",
    "clf_pipeline_full = Pipeline(steps=[\n",
    "    (\"preprocessor\", preprocessor),\n",
    "    (\"classifier\", RandomForestClassifier())\n",
    "])\n",
    "\n",
    "params = {    \n",
    "    \"classifier__max_depth\": [2, 3, 4, 5, 6],\n",
    "    \"classifier__criterion\": [\"gini\", \"entropy\", \"log_loss\"],\n",
    "    \"classifier__min_samples_split\": [300 + 50 * n for n in range(0, 28)],\n",
    "    \"classifier__n_estimators\": [50 + n for n in range(10, 201, 10)],\n",
    "    \"classifier__max_features\": [\"sqrt\", \"log2\"],\n",
    "    \"classifier__min_samples_leaf\": [2, 3, 4, 6, 8]\n",
    "}\n",
    "\n",
    "clf = RandomizedSearchCV(\n",
    "    clf_pipeline_full,\n",
    "    params,\n",
    "    n_iter=20,\n",
    "    scoring=\"roc_auc\",\n",
    "    cv=3,\n",
    "    n_jobs=-1,\n",
    "    random_state=0,\n",
    "    return_train_score=True\n",
    ")\n",
    "\n",
    "search = clf.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merojes parametros RandomizedSearchCV:\n",
      "\n",
      "{'classifier__criterion': 'log_loss',\n",
      " 'classifier__max_depth': 6,\n",
      " 'classifier__max_features': 'sqrt',\n",
      " 'classifier__min_samples_leaf': 6,\n",
      " 'classifier__min_samples_split': 550,\n",
      " 'classifier__n_estimators': 120}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Merojes parametros RandomizedSearchCV:\\n\")\n",
    "pprint(search.best_params_)\n",
    "clf_best = search.best_estimator_.fit(X_train,y_train)\n",
    "\n",
    "# Curva de ROC en el set de Train.\n",
    "probas_train = clf_best.predict_proba(X_train)[:,1]\n",
    "fpr, tpr, _ = roc_curve(y_train, probas_train)\n",
    "plt.plot(fpr, tpr, label=f\"Random Forrest  (AUC Train = {auc(fpr, tpr):.4f})\", color=\"r\")\n",
    "\n",
    "# Curva de ROC en el set de test.\n",
    "probas_test = clf_best.predict_proba(X_test)[:,1]\n",
    "fpr_test, tpr_test, _ = roc_curve(y_test, probas_test)\n",
    "plt.plot(fpr_test, tpr_test, label=f\"Random Forrest (AUC test = {auc(fpr_test, tpr_test):.4f})\", color=\"b\")\n",
    "\n",
    "plt.plot((0,1), (0,1), ls = \"--\", color = \"grey\", label = \"Randow guess\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.title(\"Receiver operating characteristic\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>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_classifier__n_estimators</th>\n",
       "      <th>param_classifier__min_samples_split</th>\n",
       "      <th>param_classifier__min_samples_leaf</th>\n",
       "      <th>param_classifier__max_features</th>\n",
       "      <th>param_classifier__max_depth</th>\n",
       "      <th>param_classifier__criterion</th>\n",
       "      <th>...</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.913645</td>\n",
       "      <td>0.099537</td>\n",
       "      <td>0.263868</td>\n",
       "      <td>0.029496</td>\n",
       "      <td>140</td>\n",
       "      <td>1550</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>4</td>\n",
       "      <td>log_loss</td>\n",
       "      <td>...</td>\n",
       "      <td>0.689639</td>\n",
       "      <td>0.680822</td>\n",
       "      <td>0.680533</td>\n",
       "      <td>0.007556</td>\n",
       "      <td>15</td>\n",
       "      <td>0.688090</td>\n",
       "      <td>0.682878</td>\n",
       "      <td>0.691095</td>\n",
       "      <td>0.687354</td>\n",
       "      <td>0.003395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.021640</td>\n",
       "      <td>0.226971</td>\n",
       "      <td>0.280566</td>\n",
       "      <td>0.010007</td>\n",
       "      <td>130</td>\n",
       "      <td>1400</td>\n",
       "      <td>4</td>\n",
       "      <td>log2</td>\n",
       "      <td>4</td>\n",
       "      <td>entropy</td>\n",
       "      <td>...</td>\n",
       "      <td>0.690288</td>\n",
       "      <td>0.681141</td>\n",
       "      <td>0.680666</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>14</td>\n",
       "      <td>0.688579</td>\n",
       "      <td>0.682976</td>\n",
       "      <td>0.689534</td>\n",
       "      <td>0.687030</td>\n",
       "      <td>0.002893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.230767</td>\n",
       "      <td>0.750617</td>\n",
       "      <td>0.343619</td>\n",
       "      <td>0.061646</td>\n",
       "      <td>190</td>\n",
       "      <td>400</td>\n",
       "      <td>3</td>\n",
       "      <td>log2</td>\n",
       "      <td>4</td>\n",
       "      <td>entropy</td>\n",
       "      <td>...</td>\n",
       "      <td>0.688869</td>\n",
       "      <td>0.679917</td>\n",
       "      <td>0.680300</td>\n",
       "      <td>0.006845</td>\n",
       "      <td>17</td>\n",
       "      <td>0.691832</td>\n",
       "      <td>0.683030</td>\n",
       "      <td>0.691094</td>\n",
       "      <td>0.688652</td>\n",
       "      <td>0.003986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.960938</td>\n",
       "      <td>0.539384</td>\n",
       "      <td>0.346234</td>\n",
       "      <td>0.057898</td>\n",
       "      <td>170</td>\n",
       "      <td>1600</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>5</td>\n",
       "      <td>entropy</td>\n",
       "      <td>...</td>\n",
       "      <td>0.692305</td>\n",
       "      <td>0.680951</td>\n",
       "      <td>0.681794</td>\n",
       "      <td>0.008259</td>\n",
       "      <td>8</td>\n",
       "      <td>0.691028</td>\n",
       "      <td>0.685936</td>\n",
       "      <td>0.691477</td>\n",
       "      <td>0.689480</td>\n",
       "      <td>0.002513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.543609</td>\n",
       "      <td>0.260430</td>\n",
       "      <td>0.197143</td>\n",
       "      <td>0.007216</td>\n",
       "      <td>90</td>\n",
       "      <td>1600</td>\n",
       "      <td>4</td>\n",
       "      <td>log2</td>\n",
       "      <td>5</td>\n",
       "      <td>gini</td>\n",
       "      <td>...</td>\n",
       "      <td>0.690800</td>\n",
       "      <td>0.680738</td>\n",
       "      <td>0.681201</td>\n",
       "      <td>0.007656</td>\n",
       "      <td>12</td>\n",
       "      <td>0.689573</td>\n",
       "      <td>0.684772</td>\n",
       "      <td>0.689843</td>\n",
       "      <td>0.688063</td>\n",
       "      <td>0.002330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       3.913645      0.099537         0.263868        0.029496   \n",
       "1       4.021640      0.226971         0.280566        0.010007   \n",
       "2       5.230767      0.750617         0.343619        0.061646   \n",
       "3       5.960938      0.539384         0.346234        0.057898   \n",
       "4       2.543609      0.260430         0.197143        0.007216   \n",
       "\n",
       "   param_classifier__n_estimators  param_classifier__min_samples_split  \\\n",
       "0                             140                                 1550   \n",
       "1                             130                                 1400   \n",
       "2                             190                                  400   \n",
       "3                             170                                 1600   \n",
       "4                              90                                 1600   \n",
       "\n",
       "   param_classifier__min_samples_leaf param_classifier__max_features  \\\n",
       "0                                   3                           sqrt   \n",
       "1                                   4                           log2   \n",
       "2                                   3                           log2   \n",
       "3                                   3                           sqrt   \n",
       "4                                   4                           log2   \n",
       "\n",
       "   param_classifier__max_depth param_classifier__criterion  ...  \\\n",
       "0                            4                    log_loss  ...   \n",
       "1                            4                     entropy  ...   \n",
       "2                            4                     entropy  ...   \n",
       "3                            5                     entropy  ...   \n",
       "4                            5                        gini  ...   \n",
       "\n",
       "  split1_test_score  split2_test_score  mean_test_score  std_test_score  \\\n",
       "0          0.689639           0.680822         0.680533        0.007556   \n",
       "1          0.690288           0.681141         0.680666        0.008057   \n",
       "2          0.688869           0.679917         0.680300        0.006845   \n",
       "3          0.692305           0.680951         0.681794        0.008259   \n",
       "4          0.690800           0.680738         0.681201        0.007656   \n",
       "\n",
       "   rank_test_score  split0_train_score  split1_train_score  \\\n",
       "0               15            0.688090            0.682878   \n",
       "1               14            0.688579            0.682976   \n",
       "2               17            0.691832            0.683030   \n",
       "3                8            0.691028            0.685936   \n",
       "4               12            0.689573            0.684772   \n",
       "\n",
       "   split2_train_score  mean_train_score  std_train_score  \n",
       "0            0.691095          0.687354         0.003395  \n",
       "1            0.689534          0.687030         0.002893  \n",
       "2            0.691094          0.688652         0.003986  \n",
       "3            0.691477          0.689480         0.002513  \n",
       "4            0.689843          0.688063         0.002330  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(search.cv_results_).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters RandomizedSearchCV:\n",
      "\n",
      "{'classifier__class_weight': None,\n",
      " 'classifier__max_depth': 6,\n",
      " 'classifier__max_features': 'sqrt',\n",
      " 'classifier__min_samples_leaf': 8,\n",
      " 'classifier__min_samples_split': 200,\n",
      " 'classifier__n_estimators': 300}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3 mins aprox corriendo.\n",
    "param_distributions = {\n",
    "    \"classifier__n_estimators\": [50, 100, 200, 300, 500],\n",
    "    \"classifier__max_features\": [\"sqrt\", \"log2\"],\n",
    "    \"classifier__max_depth\": [2, 3, 4, 5, 6],\n",
    "    \"classifier__min_samples_split\": [200, 300, 350, 400],\n",
    "    \"classifier__min_samples_leaf\": [2, 4, 8],\n",
    "    \"classifier__class_weight\": [None, \"balanced\"]\n",
    "}\n",
    "\n",
    "random_search = RandomizedSearchCV(\n",
    "    clf_pipeline_full,\n",
    "    param_distributions=param_distributions,\n",
    "    n_iter=50,\n",
    "    scoring=\"roc_auc\",\n",
    "    cv=5,\n",
    "    random_state=42,\n",
    "    n_jobs=-1,\n",
    "    return_train_score=True,\n",
    ")\n",
    "\n",
    "random_search.fit(X_train, y_train.ravel())\n",
    "best_clf_rf = random_search.best_estimator_\n",
    "print(f\"Best hyperparameters RandomizedSearchCV:\\n\")\n",
    "pprint(random_search.best_params_)\n",
    "\n",
    "# AUC en train.\n",
    "fpr, tpr, _ = roc_curve(y_train, best_clf_rf.predict_proba(X_train)[:, 1])\n",
    "roc_auc = auc(fpr,tpr)\n",
    "plt.plot(fpr, tpr, label=f\"Logistic Regression (AUC Train={roc_auc:.3f})\")\n",
    "\n",
    "# AUC en test.\n",
    "fpr, tpr, _ = roc_curve(y_test, best_clf_rf.predict_proba(X_test)[:, 1])\n",
    "roc_auc = auc(fpr,tpr)\n",
    "plt.plot(fpr, tpr, label=f\"Logistic Regression (AUC Test={roc_auc:.3f})\")\n",
    "\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"Receiver operating characteristic\")\n",
    "plt.plot((0,1), (0,1), ls=\"--\", color=\"grey\", label=\"Randow guess\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>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_classifier__n_estimators</th>\n",
       "      <th>param_classifier__min_samples_split</th>\n",
       "      <th>param_classifier__min_samples_leaf</th>\n",
       "      <th>param_classifier__max_features</th>\n",
       "      <th>param_classifier__max_depth</th>\n",
       "      <th>param_classifier__class_weight</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16.120745</td>\n",
       "      <td>0.756754</td>\n",
       "      <td>0.451684</td>\n",
       "      <td>0.049141</td>\n",
       "      <td>300</td>\n",
       "      <td>200</td>\n",
       "      <td>8</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.684039</td>\n",
       "      <td>0.010981</td>\n",
       "      <td>1</td>\n",
       "      <td>0.697580</td>\n",
       "      <td>0.703678</td>\n",
       "      <td>0.696113</td>\n",
       "      <td>0.699628</td>\n",
       "      <td>0.700399</td>\n",
       "      <td>0.699479</td>\n",
       "      <td>0.002586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.641539</td>\n",
       "      <td>0.606206</td>\n",
       "      <td>0.386319</td>\n",
       "      <td>0.044912</td>\n",
       "      <td>300</td>\n",
       "      <td>400</td>\n",
       "      <td>4</td>\n",
       "      <td>log2</td>\n",
       "      <td>6</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.683863</td>\n",
       "      <td>0.011024</td>\n",
       "      <td>2</td>\n",
       "      <td>0.694940</td>\n",
       "      <td>0.700450</td>\n",
       "      <td>0.692768</td>\n",
       "      <td>0.696293</td>\n",
       "      <td>0.697424</td>\n",
       "      <td>0.696375</td>\n",
       "      <td>0.002561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>12.706938</td>\n",
       "      <td>0.363876</td>\n",
       "      <td>0.430411</td>\n",
       "      <td>0.088084</td>\n",
       "      <td>300</td>\n",
       "      <td>400</td>\n",
       "      <td>8</td>\n",
       "      <td>log2</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.683502</td>\n",
       "      <td>0.010245</td>\n",
       "      <td>3</td>\n",
       "      <td>0.695165</td>\n",
       "      <td>0.700377</td>\n",
       "      <td>0.692794</td>\n",
       "      <td>0.696415</td>\n",
       "      <td>0.696551</td>\n",
       "      <td>0.696260</td>\n",
       "      <td>0.002460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>15.361053</td>\n",
       "      <td>0.360445</td>\n",
       "      <td>0.481432</td>\n",
       "      <td>0.098843</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>4</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>6</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.683237</td>\n",
       "      <td>0.011474</td>\n",
       "      <td>4</td>\n",
       "      <td>0.696085</td>\n",
       "      <td>0.702120</td>\n",
       "      <td>0.694693</td>\n",
       "      <td>0.698762</td>\n",
       "      <td>0.698904</td>\n",
       "      <td>0.698113</td>\n",
       "      <td>0.002566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>8.534808</td>\n",
       "      <td>0.452824</td>\n",
       "      <td>0.290133</td>\n",
       "      <td>0.048471</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>2</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.682859</td>\n",
       "      <td>0.011200</td>\n",
       "      <td>5</td>\n",
       "      <td>0.693568</td>\n",
       "      <td>0.697365</td>\n",
       "      <td>0.690215</td>\n",
       "      <td>0.693960</td>\n",
       "      <td>0.694769</td>\n",
       "      <td>0.693975</td>\n",
       "      <td>0.002299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>4.329882</td>\n",
       "      <td>0.384941</td>\n",
       "      <td>0.177673</td>\n",
       "      <td>0.025607</td>\n",
       "      <td>100</td>\n",
       "      <td>400</td>\n",
       "      <td>8</td>\n",
       "      <td>log2</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.682825</td>\n",
       "      <td>0.010329</td>\n",
       "      <td>6</td>\n",
       "      <td>0.693750</td>\n",
       "      <td>0.699881</td>\n",
       "      <td>0.692396</td>\n",
       "      <td>0.695537</td>\n",
       "      <td>0.695518</td>\n",
       "      <td>0.695416</td>\n",
       "      <td>0.002524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20.883098</td>\n",
       "      <td>2.392378</td>\n",
       "      <td>0.576285</td>\n",
       "      <td>0.045558</td>\n",
       "      <td>500</td>\n",
       "      <td>350</td>\n",
       "      <td>2</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>5</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.682535</td>\n",
       "      <td>0.011183</td>\n",
       "      <td>7</td>\n",
       "      <td>0.691745</td>\n",
       "      <td>0.697505</td>\n",
       "      <td>0.689118</td>\n",
       "      <td>0.692871</td>\n",
       "      <td>0.693922</td>\n",
       "      <td>0.693032</td>\n",
       "      <td>0.002750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.739533</td>\n",
       "      <td>0.234485</td>\n",
       "      <td>0.133403</td>\n",
       "      <td>0.042839</td>\n",
       "      <td>50</td>\n",
       "      <td>200</td>\n",
       "      <td>8</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>6</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.682533</td>\n",
       "      <td>0.010942</td>\n",
       "      <td>8</td>\n",
       "      <td>0.695563</td>\n",
       "      <td>0.703438</td>\n",
       "      <td>0.695088</td>\n",
       "      <td>0.701147</td>\n",
       "      <td>0.700154</td>\n",
       "      <td>0.699078</td>\n",
       "      <td>0.003247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2.237976</td>\n",
       "      <td>0.183861</td>\n",
       "      <td>0.138870</td>\n",
       "      <td>0.055458</td>\n",
       "      <td>50</td>\n",
       "      <td>350</td>\n",
       "      <td>4</td>\n",
       "      <td>log2</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.682242</td>\n",
       "      <td>0.011230</td>\n",
       "      <td>9</td>\n",
       "      <td>0.694072</td>\n",
       "      <td>0.700296</td>\n",
       "      <td>0.692561</td>\n",
       "      <td>0.697746</td>\n",
       "      <td>0.696477</td>\n",
       "      <td>0.696230</td>\n",
       "      <td>0.002721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.376231</td>\n",
       "      <td>0.256418</td>\n",
       "      <td>0.183316</td>\n",
       "      <td>0.053242</td>\n",
       "      <td>100</td>\n",
       "      <td>350</td>\n",
       "      <td>8</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.682233</td>\n",
       "      <td>0.011151</td>\n",
       "      <td>10</td>\n",
       "      <td>0.691473</td>\n",
       "      <td>0.696697</td>\n",
       "      <td>0.690045</td>\n",
       "      <td>0.691742</td>\n",
       "      <td>0.693273</td>\n",
       "      <td>0.692646</td>\n",
       "      <td>0.002270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2.073558</td>\n",
       "      <td>0.239400</td>\n",
       "      <td>0.117784</td>\n",
       "      <td>0.021059</td>\n",
       "      <td>50</td>\n",
       "      <td>350</td>\n",
       "      <td>4</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.682171</td>\n",
       "      <td>0.011629</td>\n",
       "      <td>11</td>\n",
       "      <td>0.691399</td>\n",
       "      <td>0.696251</td>\n",
       "      <td>0.689745</td>\n",
       "      <td>0.692414</td>\n",
       "      <td>0.692223</td>\n",
       "      <td>0.692407</td>\n",
       "      <td>0.002141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>7.533586</td>\n",
       "      <td>0.501849</td>\n",
       "      <td>0.257335</td>\n",
       "      <td>0.031852</td>\n",
       "      <td>200</td>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "      <td>log2</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.682092</td>\n",
       "      <td>0.010704</td>\n",
       "      <td>12</td>\n",
       "      <td>0.691000</td>\n",
       "      <td>0.696646</td>\n",
       "      <td>0.688540</td>\n",
       "      <td>0.693092</td>\n",
       "      <td>0.694178</td>\n",
       "      <td>0.692691</td>\n",
       "      <td>0.002760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10.962007</td>\n",
       "      <td>0.574778</td>\n",
       "      <td>0.358688</td>\n",
       "      <td>0.027562</td>\n",
       "      <td>300</td>\n",
       "      <td>200</td>\n",
       "      <td>2</td>\n",
       "      <td>log2</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.681950</td>\n",
       "      <td>0.010883</td>\n",
       "      <td>13</td>\n",
       "      <td>0.692178</td>\n",
       "      <td>0.697177</td>\n",
       "      <td>0.689187</td>\n",
       "      <td>0.692952</td>\n",
       "      <td>0.694574</td>\n",
       "      <td>0.693213</td>\n",
       "      <td>0.002642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.103668</td>\n",
       "      <td>0.317394</td>\n",
       "      <td>0.098545</td>\n",
       "      <td>0.011505</td>\n",
       "      <td>50</td>\n",
       "      <td>200</td>\n",
       "      <td>4</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.681674</td>\n",
       "      <td>0.011001</td>\n",
       "      <td>14</td>\n",
       "      <td>0.691145</td>\n",
       "      <td>0.699600</td>\n",
       "      <td>0.689926</td>\n",
       "      <td>0.694043</td>\n",
       "      <td>0.694541</td>\n",
       "      <td>0.693851</td>\n",
       "      <td>0.003355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.626674</td>\n",
       "      <td>1.342634</td>\n",
       "      <td>0.574237</td>\n",
       "      <td>0.041517</td>\n",
       "      <td>500</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>log2</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.681604</td>\n",
       "      <td>0.010878</td>\n",
       "      <td>15</td>\n",
       "      <td>0.690463</td>\n",
       "      <td>0.695424</td>\n",
       "      <td>0.687813</td>\n",
       "      <td>0.692265</td>\n",
       "      <td>0.693044</td>\n",
       "      <td>0.691802</td>\n",
       "      <td>0.002553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.826581</td>\n",
       "      <td>0.098950</td>\n",
       "      <td>0.118395</td>\n",
       "      <td>0.031796</td>\n",
       "      <td>50</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>4</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.681194</td>\n",
       "      <td>0.011579</td>\n",
       "      <td>16</td>\n",
       "      <td>0.686941</td>\n",
       "      <td>0.691974</td>\n",
       "      <td>0.684690</td>\n",
       "      <td>0.688937</td>\n",
       "      <td>0.686513</td>\n",
       "      <td>0.687811</td>\n",
       "      <td>0.002481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2.161657</td>\n",
       "      <td>0.334028</td>\n",
       "      <td>0.121646</td>\n",
       "      <td>0.029652</td>\n",
       "      <td>50</td>\n",
       "      <td>300</td>\n",
       "      <td>8</td>\n",
       "      <td>log2</td>\n",
       "      <td>5</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.681050</td>\n",
       "      <td>0.011138</td>\n",
       "      <td>17</td>\n",
       "      <td>0.691714</td>\n",
       "      <td>0.696238</td>\n",
       "      <td>0.686335</td>\n",
       "      <td>0.690643</td>\n",
       "      <td>0.690556</td>\n",
       "      <td>0.691097</td>\n",
       "      <td>0.003162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.863539</td>\n",
       "      <td>0.276670</td>\n",
       "      <td>0.119617</td>\n",
       "      <td>0.041825</td>\n",
       "      <td>50</td>\n",
       "      <td>200</td>\n",
       "      <td>2</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.680969</td>\n",
       "      <td>0.010322</td>\n",
       "      <td>18</td>\n",
       "      <td>0.688415</td>\n",
       "      <td>0.691194</td>\n",
       "      <td>0.684241</td>\n",
       "      <td>0.688602</td>\n",
       "      <td>0.688684</td>\n",
       "      <td>0.688227</td>\n",
       "      <td>0.002239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>10.432647</td>\n",
       "      <td>1.122015</td>\n",
       "      <td>0.457679</td>\n",
       "      <td>0.128345</td>\n",
       "      <td>300</td>\n",
       "      <td>350</td>\n",
       "      <td>8</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>4</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.680895</td>\n",
       "      <td>0.011153</td>\n",
       "      <td>19</td>\n",
       "      <td>0.686858</td>\n",
       "      <td>0.692583</td>\n",
       "      <td>0.684766</td>\n",
       "      <td>0.688114</td>\n",
       "      <td>0.688167</td>\n",
       "      <td>0.688097</td>\n",
       "      <td>0.002560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.505686</td>\n",
       "      <td>0.131261</td>\n",
       "      <td>0.110669</td>\n",
       "      <td>0.030812</td>\n",
       "      <td>50</td>\n",
       "      <td>300</td>\n",
       "      <td>4</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>4</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.680803</td>\n",
       "      <td>0.011272</td>\n",
       "      <td>20</td>\n",
       "      <td>0.687101</td>\n",
       "      <td>0.690594</td>\n",
       "      <td>0.683461</td>\n",
       "      <td>0.689091</td>\n",
       "      <td>0.688071</td>\n",
       "      <td>0.687663</td>\n",
       "      <td>0.002399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16.498600</td>\n",
       "      <td>0.812424</td>\n",
       "      <td>0.595335</td>\n",
       "      <td>0.093352</td>\n",
       "      <td>500</td>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>4</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.680798</td>\n",
       "      <td>0.010905</td>\n",
       "      <td>21</td>\n",
       "      <td>0.687358</td>\n",
       "      <td>0.693331</td>\n",
       "      <td>0.684472</td>\n",
       "      <td>0.688970</td>\n",
       "      <td>0.689715</td>\n",
       "      <td>0.688769</td>\n",
       "      <td>0.002905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>15.395073</td>\n",
       "      <td>0.977171</td>\n",
       "      <td>0.593261</td>\n",
       "      <td>0.134874</td>\n",
       "      <td>500</td>\n",
       "      <td>200</td>\n",
       "      <td>8</td>\n",
       "      <td>log2</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.680558</td>\n",
       "      <td>0.011216</td>\n",
       "      <td>22</td>\n",
       "      <td>0.687835</td>\n",
       "      <td>0.692031</td>\n",
       "      <td>0.684742</td>\n",
       "      <td>0.687239</td>\n",
       "      <td>0.689570</td>\n",
       "      <td>0.688283</td>\n",
       "      <td>0.002430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>7.026132</td>\n",
       "      <td>0.405267</td>\n",
       "      <td>0.179307</td>\n",
       "      <td>0.012492</td>\n",
       "      <td>300</td>\n",
       "      <td>200</td>\n",
       "      <td>2</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.680522</td>\n",
       "      <td>0.011565</td>\n",
       "      <td>23</td>\n",
       "      <td>0.687683</td>\n",
       "      <td>0.692717</td>\n",
       "      <td>0.685569</td>\n",
       "      <td>0.689119</td>\n",
       "      <td>0.688933</td>\n",
       "      <td>0.688804</td>\n",
       "      <td>0.002330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>14.167139</td>\n",
       "      <td>0.537839</td>\n",
       "      <td>0.532594</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>500</td>\n",
       "      <td>350</td>\n",
       "      <td>8</td>\n",
       "      <td>log2</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.680323</td>\n",
       "      <td>0.010919</td>\n",
       "      <td>24</td>\n",
       "      <td>0.686655</td>\n",
       "      <td>0.691613</td>\n",
       "      <td>0.683935</td>\n",
       "      <td>0.687450</td>\n",
       "      <td>0.688825</td>\n",
       "      <td>0.687695</td>\n",
       "      <td>0.002526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.469868</td>\n",
       "      <td>0.353936</td>\n",
       "      <td>0.329408</td>\n",
       "      <td>0.060259</td>\n",
       "      <td>300</td>\n",
       "      <td>200</td>\n",
       "      <td>4</td>\n",
       "      <td>log2</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.680212</td>\n",
       "      <td>0.011509</td>\n",
       "      <td>25</td>\n",
       "      <td>0.686565</td>\n",
       "      <td>0.691973</td>\n",
       "      <td>0.685289</td>\n",
       "      <td>0.688355</td>\n",
       "      <td>0.689350</td>\n",
       "      <td>0.688307</td>\n",
       "      <td>0.002310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7.190473</td>\n",
       "      <td>0.805285</td>\n",
       "      <td>0.244394</td>\n",
       "      <td>0.048460</td>\n",
       "      <td>200</td>\n",
       "      <td>400</td>\n",
       "      <td>4</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.680163</td>\n",
       "      <td>0.011220</td>\n",
       "      <td>26</td>\n",
       "      <td>0.686478</td>\n",
       "      <td>0.691110</td>\n",
       "      <td>0.683665</td>\n",
       "      <td>0.687951</td>\n",
       "      <td>0.688443</td>\n",
       "      <td>0.687529</td>\n",
       "      <td>0.002444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>8.738867</td>\n",
       "      <td>0.498346</td>\n",
       "      <td>0.328832</td>\n",
       "      <td>0.024704</td>\n",
       "      <td>300</td>\n",
       "      <td>400</td>\n",
       "      <td>8</td>\n",
       "      <td>log2</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.680093</td>\n",
       "      <td>0.010814</td>\n",
       "      <td>27</td>\n",
       "      <td>0.686108</td>\n",
       "      <td>0.690232</td>\n",
       "      <td>0.683810</td>\n",
       "      <td>0.686899</td>\n",
       "      <td>0.688897</td>\n",
       "      <td>0.687189</td>\n",
       "      <td>0.002229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3.532913</td>\n",
       "      <td>0.484375</td>\n",
       "      <td>0.178086</td>\n",
       "      <td>0.040821</td>\n",
       "      <td>100</td>\n",
       "      <td>200</td>\n",
       "      <td>4</td>\n",
       "      <td>log2</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.679920</td>\n",
       "      <td>0.010469</td>\n",
       "      <td>28</td>\n",
       "      <td>0.687113</td>\n",
       "      <td>0.691749</td>\n",
       "      <td>0.684868</td>\n",
       "      <td>0.687074</td>\n",
       "      <td>0.689432</td>\n",
       "      <td>0.688047</td>\n",
       "      <td>0.002347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.539486</td>\n",
       "      <td>0.206834</td>\n",
       "      <td>0.108952</td>\n",
       "      <td>0.020393</td>\n",
       "      <td>50</td>\n",
       "      <td>300</td>\n",
       "      <td>8</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.679642</td>\n",
       "      <td>0.010943</td>\n",
       "      <td>29</td>\n",
       "      <td>0.685590</td>\n",
       "      <td>0.689247</td>\n",
       "      <td>0.680827</td>\n",
       "      <td>0.684855</td>\n",
       "      <td>0.683024</td>\n",
       "      <td>0.684709</td>\n",
       "      <td>0.002803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>6.057740</td>\n",
       "      <td>0.134378</td>\n",
       "      <td>0.291595</td>\n",
       "      <td>0.064903</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>8</td>\n",
       "      <td>log2</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.679593</td>\n",
       "      <td>0.011658</td>\n",
       "      <td>30</td>\n",
       "      <td>0.686323</td>\n",
       "      <td>0.690985</td>\n",
       "      <td>0.684919</td>\n",
       "      <td>0.687381</td>\n",
       "      <td>0.688379</td>\n",
       "      <td>0.687597</td>\n",
       "      <td>0.002046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1.514015</td>\n",
       "      <td>0.296282</td>\n",
       "      <td>0.093716</td>\n",
       "      <td>0.011299</td>\n",
       "      <td>50</td>\n",
       "      <td>400</td>\n",
       "      <td>8</td>\n",
       "      <td>log2</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.679164</td>\n",
       "      <td>0.010390</td>\n",
       "      <td>31</td>\n",
       "      <td>0.689204</td>\n",
       "      <td>0.691818</td>\n",
       "      <td>0.681386</td>\n",
       "      <td>0.686454</td>\n",
       "      <td>0.688740</td>\n",
       "      <td>0.687520</td>\n",
       "      <td>0.003509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1.650851</td>\n",
       "      <td>0.111136</td>\n",
       "      <td>0.096380</td>\n",
       "      <td>0.014254</td>\n",
       "      <td>50</td>\n",
       "      <td>350</td>\n",
       "      <td>8</td>\n",
       "      <td>log2</td>\n",
       "      <td>4</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.679006</td>\n",
       "      <td>0.009414</td>\n",
       "      <td>32</td>\n",
       "      <td>0.687403</td>\n",
       "      <td>0.691714</td>\n",
       "      <td>0.683418</td>\n",
       "      <td>0.685986</td>\n",
       "      <td>0.688036</td>\n",
       "      <td>0.687311</td>\n",
       "      <td>0.002714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5.131035</td>\n",
       "      <td>0.328847</td>\n",
       "      <td>0.228705</td>\n",
       "      <td>0.018288</td>\n",
       "      <td>200</td>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.678841</td>\n",
       "      <td>0.010733</td>\n",
       "      <td>33</td>\n",
       "      <td>0.683792</td>\n",
       "      <td>0.689312</td>\n",
       "      <td>0.679600</td>\n",
       "      <td>0.683706</td>\n",
       "      <td>0.684785</td>\n",
       "      <td>0.684239</td>\n",
       "      <td>0.003100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.811473</td>\n",
       "      <td>0.641759</td>\n",
       "      <td>0.314990</td>\n",
       "      <td>0.052629</td>\n",
       "      <td>300</td>\n",
       "      <td>400</td>\n",
       "      <td>8</td>\n",
       "      <td>log2</td>\n",
       "      <td>3</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.678505</td>\n",
       "      <td>0.010788</td>\n",
       "      <td>34</td>\n",
       "      <td>0.683745</td>\n",
       "      <td>0.689870</td>\n",
       "      <td>0.680317</td>\n",
       "      <td>0.682271</td>\n",
       "      <td>0.683527</td>\n",
       "      <td>0.683946</td>\n",
       "      <td>0.003203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.533135</td>\n",
       "      <td>0.273691</td>\n",
       "      <td>0.095058</td>\n",
       "      <td>0.005039</td>\n",
       "      <td>50</td>\n",
       "      <td>350</td>\n",
       "      <td>8</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.677466</td>\n",
       "      <td>0.011033</td>\n",
       "      <td>35</td>\n",
       "      <td>0.680935</td>\n",
       "      <td>0.685483</td>\n",
       "      <td>0.678103</td>\n",
       "      <td>0.683690</td>\n",
       "      <td>0.683037</td>\n",
       "      <td>0.682250</td>\n",
       "      <td>0.002533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.145530</td>\n",
       "      <td>0.471337</td>\n",
       "      <td>0.215849</td>\n",
       "      <td>0.039624</td>\n",
       "      <td>200</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>log2</td>\n",
       "      <td>2</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.676829</td>\n",
       "      <td>0.011165</td>\n",
       "      <td>36</td>\n",
       "      <td>0.679048</td>\n",
       "      <td>0.684004</td>\n",
       "      <td>0.675850</td>\n",
       "      <td>0.681361</td>\n",
       "      <td>0.682733</td>\n",
       "      <td>0.680599</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4.117847</td>\n",
       "      <td>0.284574</td>\n",
       "      <td>0.175442</td>\n",
       "      <td>0.013987</td>\n",
       "      <td>200</td>\n",
       "      <td>350</td>\n",
       "      <td>8</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>2</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.676762</td>\n",
       "      <td>0.010851</td>\n",
       "      <td>37</td>\n",
       "      <td>0.679234</td>\n",
       "      <td>0.683862</td>\n",
       "      <td>0.676359</td>\n",
       "      <td>0.678387</td>\n",
       "      <td>0.680427</td>\n",
       "      <td>0.679654</td>\n",
       "      <td>0.002488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4.305047</td>\n",
       "      <td>0.395058</td>\n",
       "      <td>0.165247</td>\n",
       "      <td>0.010751</td>\n",
       "      <td>200</td>\n",
       "      <td>350</td>\n",
       "      <td>4</td>\n",
       "      <td>log2</td>\n",
       "      <td>2</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.676508</td>\n",
       "      <td>0.010077</td>\n",
       "      <td>38</td>\n",
       "      <td>0.677898</td>\n",
       "      <td>0.685620</td>\n",
       "      <td>0.676430</td>\n",
       "      <td>0.678297</td>\n",
       "      <td>0.681794</td>\n",
       "      <td>0.680008</td>\n",
       "      <td>0.003313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.116478</td>\n",
       "      <td>0.333756</td>\n",
       "      <td>0.124019</td>\n",
       "      <td>0.025196</td>\n",
       "      <td>100</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>log2</td>\n",
       "      <td>2</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.676491</td>\n",
       "      <td>0.010230</td>\n",
       "      <td>39</td>\n",
       "      <td>0.679777</td>\n",
       "      <td>0.686171</td>\n",
       "      <td>0.674982</td>\n",
       "      <td>0.678476</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>0.679881</td>\n",
       "      <td>0.003621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.553237</td>\n",
       "      <td>0.798287</td>\n",
       "      <td>0.431662</td>\n",
       "      <td>0.114725</td>\n",
       "      <td>500</td>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>2</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.676438</td>\n",
       "      <td>0.010271</td>\n",
       "      <td>40</td>\n",
       "      <td>0.679693</td>\n",
       "      <td>0.685091</td>\n",
       "      <td>0.676375</td>\n",
       "      <td>0.679722</td>\n",
       "      <td>0.680221</td>\n",
       "      <td>0.680220</td>\n",
       "      <td>0.002794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.524576</td>\n",
       "      <td>0.281466</td>\n",
       "      <td>0.081630</td>\n",
       "      <td>0.002134</td>\n",
       "      <td>50</td>\n",
       "      <td>200</td>\n",
       "      <td>8</td>\n",
       "      <td>log2</td>\n",
       "      <td>3</td>\n",
       "      <td>balanced</td>\n",
       "      <td>...</td>\n",
       "      <td>0.675832</td>\n",
       "      <td>0.010624</td>\n",
       "      <td>41</td>\n",
       "      <td>0.680786</td>\n",
       "      <td>0.689075</td>\n",
       "      <td>0.678871</td>\n",
       "      <td>0.682642</td>\n",
       "      <td>0.683066</td>\n",
       "      <td>0.682888</td>\n",
       "      <td>0.003433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.971639</td>\n",
       "      <td>0.187856</td>\n",
       "      <td>0.107794</td>\n",
       "      <td>0.008805</td>\n",
       "      <td>100</td>\n",
       "      <td>200</td>\n",
       "      <td>8</td>\n",
       "      <td>log2</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.675504</td>\n",
       "      <td>0.010425</td>\n",
       "      <td>42</td>\n",
       "      <td>0.679291</td>\n",
       "      <td>0.683818</td>\n",
       "      <td>0.675425</td>\n",
       "      <td>0.678325</td>\n",
       "      <td>0.679784</td>\n",
       "      <td>0.679329</td>\n",
       "      <td>0.002706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.230253</td>\n",
       "      <td>0.275220</td>\n",
       "      <td>0.171049</td>\n",
       "      <td>0.026982</td>\n",
       "      <td>200</td>\n",
       "      <td>300</td>\n",
       "      <td>8</td>\n",
       "      <td>log2</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.675364</td>\n",
       "      <td>0.010568</td>\n",
       "      <td>43</td>\n",
       "      <td>0.679145</td>\n",
       "      <td>0.682780</td>\n",
       "      <td>0.673849</td>\n",
       "      <td>0.679605</td>\n",
       "      <td>0.681610</td>\n",
       "      <td>0.679398</td>\n",
       "      <td>0.003073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>9.876326</td>\n",
       "      <td>0.686199</td>\n",
       "      <td>0.340800</td>\n",
       "      <td>0.020144</td>\n",
       "      <td>500</td>\n",
       "      <td>300</td>\n",
       "      <td>8</td>\n",
       "      <td>log2</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.675223</td>\n",
       "      <td>0.010587</td>\n",
       "      <td>44</td>\n",
       "      <td>0.677987</td>\n",
       "      <td>0.683247</td>\n",
       "      <td>0.674950</td>\n",
       "      <td>0.677599</td>\n",
       "      <td>0.679244</td>\n",
       "      <td>0.678605</td>\n",
       "      <td>0.002710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>6.882808</td>\n",
       "      <td>1.090894</td>\n",
       "      <td>0.310430</td>\n",
       "      <td>0.062984</td>\n",
       "      <td>300</td>\n",
       "      <td>200</td>\n",
       "      <td>4</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.675189</td>\n",
       "      <td>0.010153</td>\n",
       "      <td>45</td>\n",
       "      <td>0.678632</td>\n",
       "      <td>0.684270</td>\n",
       "      <td>0.675258</td>\n",
       "      <td>0.677125</td>\n",
       "      <td>0.679915</td>\n",
       "      <td>0.679040</td>\n",
       "      <td>0.003042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.753073</td>\n",
       "      <td>0.314057</td>\n",
       "      <td>0.189903</td>\n",
       "      <td>0.043841</td>\n",
       "      <td>300</td>\n",
       "      <td>200</td>\n",
       "      <td>8</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.675164</td>\n",
       "      <td>0.011016</td>\n",
       "      <td>46</td>\n",
       "      <td>0.678695</td>\n",
       "      <td>0.683297</td>\n",
       "      <td>0.675967</td>\n",
       "      <td>0.677613</td>\n",
       "      <td>0.678123</td>\n",
       "      <td>0.678739</td>\n",
       "      <td>0.002454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.939743</td>\n",
       "      <td>0.819781</td>\n",
       "      <td>0.414020</td>\n",
       "      <td>0.048205</td>\n",
       "      <td>500</td>\n",
       "      <td>200</td>\n",
       "      <td>8</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.674908</td>\n",
       "      <td>0.011137</td>\n",
       "      <td>47</td>\n",
       "      <td>0.678704</td>\n",
       "      <td>0.683048</td>\n",
       "      <td>0.675636</td>\n",
       "      <td>0.676852</td>\n",
       "      <td>0.677795</td>\n",
       "      <td>0.678407</td>\n",
       "      <td>0.002534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10.483544</td>\n",
       "      <td>0.628677</td>\n",
       "      <td>0.531626</td>\n",
       "      <td>0.143267</td>\n",
       "      <td>500</td>\n",
       "      <td>300</td>\n",
       "      <td>8</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.674789</td>\n",
       "      <td>0.010587</td>\n",
       "      <td>48</td>\n",
       "      <td>0.677970</td>\n",
       "      <td>0.683088</td>\n",
       "      <td>0.675379</td>\n",
       "      <td>0.677620</td>\n",
       "      <td>0.679142</td>\n",
       "      <td>0.678640</td>\n",
       "      <td>0.002536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.552659</td>\n",
       "      <td>0.890362</td>\n",
       "      <td>0.261461</td>\n",
       "      <td>0.043635</td>\n",
       "      <td>300</td>\n",
       "      <td>400</td>\n",
       "      <td>8</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.674400</td>\n",
       "      <td>0.011429</td>\n",
       "      <td>49</td>\n",
       "      <td>0.677713</td>\n",
       "      <td>0.681223</td>\n",
       "      <td>0.674751</td>\n",
       "      <td>0.677710</td>\n",
       "      <td>0.679371</td>\n",
       "      <td>0.678154</td>\n",
       "      <td>0.002139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1.073801</td>\n",
       "      <td>0.030192</td>\n",
       "      <td>0.084940</td>\n",
       "      <td>0.009541</td>\n",
       "      <td>50</td>\n",
       "      <td>350</td>\n",
       "      <td>2</td>\n",
       "      <td>log2</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0.673607</td>\n",
       "      <td>0.009648</td>\n",
       "      <td>50</td>\n",
       "      <td>0.679291</td>\n",
       "      <td>0.683190</td>\n",
       "      <td>0.671352</td>\n",
       "      <td>0.676714</td>\n",
       "      <td>0.678831</td>\n",
       "      <td>0.677876</td>\n",
       "      <td>0.003875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "16      16.120745      0.756754         0.451684        0.049141   \n",
       "0       12.641539      0.606206         0.386319        0.044912   \n",
       "29      12.706938      0.363876         0.430411        0.088084   \n",
       "42      15.361053      0.360445         0.481432        0.098843   \n",
       "34       8.534808      0.452824         0.290133        0.048471   \n",
       "35       4.329882      0.384941         0.177673        0.025607   \n",
       "7       20.883098      2.392378         0.576285        0.045558   \n",
       "5        2.739533      0.234485         0.133403        0.042839   \n",
       "27       2.237976      0.183861         0.138870        0.055458   \n",
       "8        4.376231      0.256418         0.183316        0.053242   \n",
       "30       2.073558      0.239400         0.117784        0.021059   \n",
       "36       7.533586      0.501849         0.257335        0.031852   \n",
       "19      10.962007      0.574778         0.358688        0.027562   \n",
       "20       2.103668      0.317394         0.098545        0.011505   \n",
       "3       17.626674      1.342634         0.574237        0.041517   \n",
       "9        1.826581      0.098950         0.118395        0.031796   \n",
       "40       2.161657      0.334028         0.121646        0.029652   \n",
       "14       1.863539      0.276670         0.119617        0.041825   \n",
       "31      10.432647      1.122015         0.457679        0.128345   \n",
       "1        1.505686      0.131261         0.110669        0.030812   \n",
       "13      16.498600      0.812424         0.595335        0.093352   \n",
       "23      15.395073      0.977171         0.593261        0.134874   \n",
       "49       7.026132      0.405267         0.179307        0.012492   \n",
       "46      14.167139      0.537839         0.532594        0.062210   \n",
       "6        9.469868      0.353936         0.329408        0.060259   \n",
       "25       7.190473      0.805285         0.244394        0.048460   \n",
       "28       8.738867      0.498346         0.328832        0.024704   \n",
       "38       3.532913      0.484375         0.178086        0.040821   \n",
       "17       1.539486      0.206834         0.108952        0.020393   \n",
       "45       6.057740      0.134378         0.291595        0.064903   \n",
       "43       1.514015      0.296282         0.093716        0.011299   \n",
       "44       1.650851      0.111136         0.096380        0.014254   \n",
       "33       5.131035      0.328847         0.228705        0.018288   \n",
       "18       6.811473      0.641759         0.314990        0.052629   \n",
       "15       1.533135      0.273691         0.095058        0.005039   \n",
       "26       4.145530      0.471337         0.215849        0.039624   \n",
       "39       4.117847      0.284574         0.175442        0.013987   \n",
       "32       4.305047      0.395058         0.165247        0.010751   \n",
       "22       2.116478      0.333756         0.124019        0.025196   \n",
       "21      10.553237      0.798287         0.431662        0.114725   \n",
       "10       1.524576      0.281466         0.081630        0.002134   \n",
       "2        1.971639      0.187856         0.107794        0.008805   \n",
       "41       4.230253      0.275220         0.171049        0.026982   \n",
       "24       9.876326      0.686199         0.340800        0.020144   \n",
       "47       6.882808      1.090894         0.310430        0.062984   \n",
       "48       5.753073      0.314057         0.189903        0.043841   \n",
       "11      10.939743      0.819781         0.414020        0.048205   \n",
       "12      10.483544      0.628677         0.531626        0.143267   \n",
       "4        6.552659      0.890362         0.261461        0.043635   \n",
       "37       1.073801      0.030192         0.084940        0.009541   \n",
       "\n",
       "    param_classifier__n_estimators  param_classifier__min_samples_split  \\\n",
       "16                             300                                  200   \n",
       "0                              300                                  400   \n",
       "29                             300                                  400   \n",
       "42                             300                                  300   \n",
       "34                             200                                  200   \n",
       "35                             100                                  400   \n",
       "7                              500                                  350   \n",
       "5                               50                                  200   \n",
       "27                              50                                  350   \n",
       "8                              100                                  350   \n",
       "30                              50                                  350   \n",
       "36                             200                                  300   \n",
       "19                             300                                  200   \n",
       "20                              50                                  200   \n",
       "3                              500                                  400   \n",
       "9                               50                                  400   \n",
       "40                              50                                  300   \n",
       "14                              50                                  200   \n",
       "31                             300                                  350   \n",
       "1                               50                                  300   \n",
       "13                             500                                  300   \n",
       "23                             500                                  200   \n",
       "49                             300                                  200   \n",
       "46                             500                                  350   \n",
       "6                              300                                  200   \n",
       "25                             200                                  400   \n",
       "28                             300                                  400   \n",
       "38                             100                                  200   \n",
       "17                              50                                  300   \n",
       "45                             200                                  200   \n",
       "43                              50                                  400   \n",
       "44                              50                                  350   \n",
       "33                             200                                  300   \n",
       "18                             300                                  400   \n",
       "15                              50                                  350   \n",
       "26                             200                                  400   \n",
       "39                             200                                  350   \n",
       "32                             200                                  350   \n",
       "22                             100                                  400   \n",
       "21                             500                                  300   \n",
       "10                              50                                  200   \n",
       "2                              100                                  200   \n",
       "41                             200                                  300   \n",
       "24                             500                                  300   \n",
       "47                             300                                  200   \n",
       "48                             300                                  200   \n",
       "11                             500                                  200   \n",
       "12                             500                                  300   \n",
       "4                              300                                  400   \n",
       "37                              50                                  350   \n",
       "\n",
       "    param_classifier__min_samples_leaf param_classifier__max_features  \\\n",
       "16                                   8                           sqrt   \n",
       "0                                    4                           log2   \n",
       "29                                   8                           log2   \n",
       "42                                   4                           sqrt   \n",
       "34                                   2                           sqrt   \n",
       "35                                   8                           log2   \n",
       "7                                    2                           sqrt   \n",
       "5                                    8                           sqrt   \n",
       "27                                   4                           log2   \n",
       "8                                    8                           sqrt   \n",
       "30                                   4                           sqrt   \n",
       "36                                   2                           log2   \n",
       "19                                   2                           log2   \n",
       "20                                   4                           sqrt   \n",
       "3                                    2                           log2   \n",
       "9                                    2                           sqrt   \n",
       "40                                   8                           log2   \n",
       "14                                   2                           sqrt   \n",
       "31                                   8                           sqrt   \n",
       "1                                    4                           sqrt   \n",
       "13                                   2                           sqrt   \n",
       "23                                   8                           log2   \n",
       "49                                   2                           sqrt   \n",
       "46                                   8                           log2   \n",
       "6                                    4                           log2   \n",
       "25                                   4                           sqrt   \n",
       "28                                   8                           log2   \n",
       "38                                   4                           log2   \n",
       "17                                   8                           sqrt   \n",
       "45                                   8                           log2   \n",
       "43                                   8                           log2   \n",
       "44                                   8                           log2   \n",
       "33                                   2                           sqrt   \n",
       "18                                   8                           log2   \n",
       "15                                   8                           sqrt   \n",
       "26                                   2                           log2   \n",
       "39                                   8                           sqrt   \n",
       "32                                   4                           log2   \n",
       "22                                   2                           log2   \n",
       "21                                   2                           sqrt   \n",
       "10                                   8                           log2   \n",
       "2                                    8                           log2   \n",
       "41                                   8                           log2   \n",
       "24                                   8                           log2   \n",
       "47                                   4                           sqrt   \n",
       "48                                   8                           sqrt   \n",
       "11                                   8                           sqrt   \n",
       "12                                   8                           sqrt   \n",
       "4                                    8                           sqrt   \n",
       "37                                   2                           log2   \n",
       "\n",
       "    param_classifier__max_depth param_classifier__class_weight  ...  \\\n",
       "16                            6                           None  ...   \n",
       "0                             6                       balanced  ...   \n",
       "29                            6                           None  ...   \n",
       "42                            6                       balanced  ...   \n",
       "34                            5                           None  ...   \n",
       "35                            6                           None  ...   \n",
       "7                             5                       balanced  ...   \n",
       "5                             6                       balanced  ...   \n",
       "27                            6                           None  ...   \n",
       "8                             5                           None  ...   \n",
       "30                            5                           None  ...   \n",
       "36                            5                           None  ...   \n",
       "19                            5                           None  ...   \n",
       "20                            5                           None  ...   \n",
       "3                             5                           None  ...   \n",
       "9                             4                       balanced  ...   \n",
       "40                            5                       balanced  ...   \n",
       "14                            4                           None  ...   \n",
       "31                            4                       balanced  ...   \n",
       "1                             4                       balanced  ...   \n",
       "13                            4                       balanced  ...   \n",
       "23                            4                           None  ...   \n",
       "49                            4                           None  ...   \n",
       "46                            4                           None  ...   \n",
       "6                             4                           None  ...   \n",
       "25                            4                           None  ...   \n",
       "28                            4                           None  ...   \n",
       "38                            4                           None  ...   \n",
       "17                            3                       balanced  ...   \n",
       "45                            4                           None  ...   \n",
       "43                            4                           None  ...   \n",
       "44                            4                       balanced  ...   \n",
       "33                            3                       balanced  ...   \n",
       "18                            3                       balanced  ...   \n",
       "15                            3                           None  ...   \n",
       "26                            2                       balanced  ...   \n",
       "39                            2                       balanced  ...   \n",
       "32                            2                       balanced  ...   \n",
       "22                            2                       balanced  ...   \n",
       "21                            2                       balanced  ...   \n",
       "10                            3                       balanced  ...   \n",
       "2                             2                           None  ...   \n",
       "41                            2                           None  ...   \n",
       "24                            2                           None  ...   \n",
       "47                            2                           None  ...   \n",
       "48                            2                           None  ...   \n",
       "11                            2                           None  ...   \n",
       "12                            2                           None  ...   \n",
       "4                             2                           None  ...   \n",
       "37                            2                           None  ...   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  split0_train_score  \\\n",
       "16        0.684039        0.010981                1            0.697580   \n",
       "0         0.683863        0.011024                2            0.694940   \n",
       "29        0.683502        0.010245                3            0.695165   \n",
       "42        0.683237        0.011474                4            0.696085   \n",
       "34        0.682859        0.011200                5            0.693568   \n",
       "35        0.682825        0.010329                6            0.693750   \n",
       "7         0.682535        0.011183                7            0.691745   \n",
       "5         0.682533        0.010942                8            0.695563   \n",
       "27        0.682242        0.011230                9            0.694072   \n",
       "8         0.682233        0.011151               10            0.691473   \n",
       "30        0.682171        0.011629               11            0.691399   \n",
       "36        0.682092        0.010704               12            0.691000   \n",
       "19        0.681950        0.010883               13            0.692178   \n",
       "20        0.681674        0.011001               14            0.691145   \n",
       "3         0.681604        0.010878               15            0.690463   \n",
       "9         0.681194        0.011579               16            0.686941   \n",
       "40        0.681050        0.011138               17            0.691714   \n",
       "14        0.680969        0.010322               18            0.688415   \n",
       "31        0.680895        0.011153               19            0.686858   \n",
       "1         0.680803        0.011272               20            0.687101   \n",
       "13        0.680798        0.010905               21            0.687358   \n",
       "23        0.680558        0.011216               22            0.687835   \n",
       "49        0.680522        0.011565               23            0.687683   \n",
       "46        0.680323        0.010919               24            0.686655   \n",
       "6         0.680212        0.011509               25            0.686565   \n",
       "25        0.680163        0.011220               26            0.686478   \n",
       "28        0.680093        0.010814               27            0.686108   \n",
       "38        0.679920        0.010469               28            0.687113   \n",
       "17        0.679642        0.010943               29            0.685590   \n",
       "45        0.679593        0.011658               30            0.686323   \n",
       "43        0.679164        0.010390               31            0.689204   \n",
       "44        0.679006        0.009414               32            0.687403   \n",
       "33        0.678841        0.010733               33            0.683792   \n",
       "18        0.678505        0.010788               34            0.683745   \n",
       "15        0.677466        0.011033               35            0.680935   \n",
       "26        0.676829        0.011165               36            0.679048   \n",
       "39        0.676762        0.010851               37            0.679234   \n",
       "32        0.676508        0.010077               38            0.677898   \n",
       "22        0.676491        0.010230               39            0.679777   \n",
       "21        0.676438        0.010271               40            0.679693   \n",
       "10        0.675832        0.010624               41            0.680786   \n",
       "2         0.675504        0.010425               42            0.679291   \n",
       "41        0.675364        0.010568               43            0.679145   \n",
       "24        0.675223        0.010587               44            0.677987   \n",
       "47        0.675189        0.010153               45            0.678632   \n",
       "48        0.675164        0.011016               46            0.678695   \n",
       "11        0.674908        0.011137               47            0.678704   \n",
       "12        0.674789        0.010587               48            0.677970   \n",
       "4         0.674400        0.011429               49            0.677713   \n",
       "37        0.673607        0.009648               50            0.679291   \n",
       "\n",
       "    split1_train_score  split2_train_score  split3_train_score  \\\n",
       "16            0.703678            0.696113            0.699628   \n",
       "0             0.700450            0.692768            0.696293   \n",
       "29            0.700377            0.692794            0.696415   \n",
       "42            0.702120            0.694693            0.698762   \n",
       "34            0.697365            0.690215            0.693960   \n",
       "35            0.699881            0.692396            0.695537   \n",
       "7             0.697505            0.689118            0.692871   \n",
       "5             0.703438            0.695088            0.701147   \n",
       "27            0.700296            0.692561            0.697746   \n",
       "8             0.696697            0.690045            0.691742   \n",
       "30            0.696251            0.689745            0.692414   \n",
       "36            0.696646            0.688540            0.693092   \n",
       "19            0.697177            0.689187            0.692952   \n",
       "20            0.699600            0.689926            0.694043   \n",
       "3             0.695424            0.687813            0.692265   \n",
       "9             0.691974            0.684690            0.688937   \n",
       "40            0.696238            0.686335            0.690643   \n",
       "14            0.691194            0.684241            0.688602   \n",
       "31            0.692583            0.684766            0.688114   \n",
       "1             0.690594            0.683461            0.689091   \n",
       "13            0.693331            0.684472            0.688970   \n",
       "23            0.692031            0.684742            0.687239   \n",
       "49            0.692717            0.685569            0.689119   \n",
       "46            0.691613            0.683935            0.687450   \n",
       "6             0.691973            0.685289            0.688355   \n",
       "25            0.691110            0.683665            0.687951   \n",
       "28            0.690232            0.683810            0.686899   \n",
       "38            0.691749            0.684868            0.687074   \n",
       "17            0.689247            0.680827            0.684855   \n",
       "45            0.690985            0.684919            0.687381   \n",
       "43            0.691818            0.681386            0.686454   \n",
       "44            0.691714            0.683418            0.685986   \n",
       "33            0.689312            0.679600            0.683706   \n",
       "18            0.689870            0.680317            0.682271   \n",
       "15            0.685483            0.678103            0.683690   \n",
       "26            0.684004            0.675850            0.681361   \n",
       "39            0.683862            0.676359            0.678387   \n",
       "32            0.685620            0.676430            0.678297   \n",
       "22            0.686171            0.674982            0.678476   \n",
       "21            0.685091            0.676375            0.679722   \n",
       "10            0.689075            0.678871            0.682642   \n",
       "2             0.683818            0.675425            0.678325   \n",
       "41            0.682780            0.673849            0.679605   \n",
       "24            0.683247            0.674950            0.677599   \n",
       "47            0.684270            0.675258            0.677125   \n",
       "48            0.683297            0.675967            0.677613   \n",
       "11            0.683048            0.675636            0.676852   \n",
       "12            0.683088            0.675379            0.677620   \n",
       "4             0.681223            0.674751            0.677710   \n",
       "37            0.683190            0.671352            0.676714   \n",
       "\n",
       "    split4_train_score  mean_train_score  std_train_score  \n",
       "16            0.700399          0.699479         0.002586  \n",
       "0             0.697424          0.696375         0.002561  \n",
       "29            0.696551          0.696260         0.002460  \n",
       "42            0.698904          0.698113         0.002566  \n",
       "34            0.694769          0.693975         0.002299  \n",
       "35            0.695518          0.695416         0.002524  \n",
       "7             0.693922          0.693032         0.002750  \n",
       "5             0.700154          0.699078         0.003247  \n",
       "27            0.696477          0.696230         0.002721  \n",
       "8             0.693273          0.692646         0.002270  \n",
       "30            0.692223          0.692407         0.002141  \n",
       "36            0.694178          0.692691         0.002760  \n",
       "19            0.694574          0.693213         0.002642  \n",
       "20            0.694541          0.693851         0.003355  \n",
       "3             0.693044          0.691802         0.002553  \n",
       "9             0.686513          0.687811         0.002481  \n",
       "40            0.690556          0.691097         0.003162  \n",
       "14            0.688684          0.688227         0.002239  \n",
       "31            0.688167          0.688097         0.002560  \n",
       "1             0.688071          0.687663         0.002399  \n",
       "13            0.689715          0.688769         0.002905  \n",
       "23            0.689570          0.688283         0.002430  \n",
       "49            0.688933          0.688804         0.002330  \n",
       "46            0.688825          0.687695         0.002526  \n",
       "6             0.689350          0.688307         0.002310  \n",
       "25            0.688443          0.687529         0.002444  \n",
       "28            0.688897          0.687189         0.002229  \n",
       "38            0.689432          0.688047         0.002347  \n",
       "17            0.683024          0.684709         0.002803  \n",
       "45            0.688379          0.687597         0.002046  \n",
       "43            0.688740          0.687520         0.003509  \n",
       "44            0.688036          0.687311         0.002714  \n",
       "33            0.684785          0.684239         0.003100  \n",
       "18            0.683527          0.683946         0.003203  \n",
       "15            0.683037          0.682250         0.002533  \n",
       "26            0.682733          0.680599         0.002887  \n",
       "39            0.680427          0.679654         0.002488  \n",
       "32            0.681794          0.680008         0.003313  \n",
       "22            0.680000          0.679881         0.003621  \n",
       "21            0.680221          0.680220         0.002794  \n",
       "10            0.683066          0.682888         0.003433  \n",
       "2             0.679784          0.679329         0.002706  \n",
       "41            0.681610          0.679398         0.003073  \n",
       "24            0.679244          0.678605         0.002710  \n",
       "47            0.679915          0.679040         0.003042  \n",
       "48            0.678123          0.678739         0.002454  \n",
       "11            0.677795          0.678407         0.002534  \n",
       "12            0.679142          0.678640         0.002536  \n",
       "4             0.679371          0.678154         0.002139  \n",
       "37            0.678831          0.677876         0.003875  \n",
       "\n",
       "[50 rows x 26 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(random_search.cv_results_).sort_values(\"rank_test_score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "executionInfo": {
     "elapsed": 7178,
     "status": "ok",
     "timestamp": 1668713512653,
     "user": {
      "displayName": "Luis Andres Campos Maldonado",
      "userId": "15478348060554261231"
     },
     "user_tz": 300
    },
    "id": "4duNGjsTR87q"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_results_rf_final = cross_validate(\n",
    "    best_clf_rf, X_train, y_train, cv=5,\n",
    "    scoring=(\"roc_auc\", \"accuracy\"),\n",
    "    return_train_score=True\n",
    ")\n",
    "\n",
    "train_lr, test_lr = cv_results_rf_final[\"train_roc_auc\"], cv_results_rf_final[\"test_roc_auc\"]\n",
    "\n",
    "values_aucs_lr = np.concatenate((train_lr, test_lr), axis=None)\n",
    "min_y, max_y = values_aucs_lr.min() -0.01 , values_aucs_lr.max()+ 0.01\n",
    "means_rf = (train_lr.mean(), test_lr.mean())\n",
    "##\n",
    "plt.plot(cv_results_rf_final[\"train_roc_auc\"], label=\"Train_data\")\n",
    "plt.plot(cv_results_rf_final[\"test_roc_auc\"], label=\"Test_data\")\n",
    "plt.title(\"Random Forrest AUC - 5-fold cross-validation\")\n",
    "plt.legend()\n",
    "plt.text(0, min_y + 4e-03, f\"Mean-AUC in Train: {means_rf[0]:.4f}\")\n",
    "plt.text(0, min_y + 2e-03, f\"Mean-AUC in Test:  {means_rf[1]:.4f}\")\n",
    "plt.ylim(min_y, max_y)\n",
    "plt.xticks(np.arange(0,5), np.arange(1,6))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "c1_recart": "7.1.0-57c20131aabc1dc2a8c675852d80a7da",
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}