5.1_modelo_casual1.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a realizar el primer modelo para la VR casual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tratamiento de datos\n",
    "# ------------------------------------------------------------------------------\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Gráficos\n",
    "# ------------------------------------------------------------------------------\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Modelado y evaluación\n",
    "# ------------------------------------------------------------------------------\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Configuración warnings\n",
    "# ------------------------------------------------------------------------------\n",
    "import warnings\n",
    "warnings.filterwarnings('once') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>date</th>\n",
       "      <th>season</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.306137</td>\n",
       "      <td>0.856346</td>\n",
       "      <td>0.17643</td>\n",
       "      <td>1.724547</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.025802</td>\n",
       "      <td>0.746165</td>\n",
       "      <td>14.110847</td>\n",
       "      <td>80.5833</td>\n",
       "      <td>10.749882</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>0.306137</td>\n",
       "      <td>0.856346</td>\n",
       "      <td>0.17643</td>\n",
       "      <td>0.992329</td>\n",
       "      <td>1.369596</td>\n",
       "      <td>0.992329</td>\n",
       "      <td>0.746165</td>\n",
       "      <td>14.902598</td>\n",
       "      <td>69.6087</td>\n",
       "      <td>16.652113</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>0.306137</td>\n",
       "      <td>0.856346</td>\n",
       "      <td>0.17643</td>\n",
       "      <td>0.992329</td>\n",
       "      <td>0.929568</td>\n",
       "      <td>0.992329</td>\n",
       "      <td>1.156206</td>\n",
       "      <td>8.050924</td>\n",
       "      <td>43.7273</td>\n",
       "      <td>16.636703</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>0.306137</td>\n",
       "      <td>0.856346</td>\n",
       "      <td>0.17643</td>\n",
       "      <td>0.992329</td>\n",
       "      <td>0.876569</td>\n",
       "      <td>0.992329</td>\n",
       "      <td>1.156206</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>59.0435</td>\n",
       "      <td>10.739832</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>0.306137</td>\n",
       "      <td>0.856346</td>\n",
       "      <td>0.17643</td>\n",
       "      <td>0.992329</td>\n",
       "      <td>0.749651</td>\n",
       "      <td>0.992329</td>\n",
       "      <td>1.156206</td>\n",
       "      <td>9.305237</td>\n",
       "      <td>43.6957</td>\n",
       "      <td>12.522300</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date    season      year    month   holiday   weekday  workingday  \\\n",
       "0 2018-01-01  0.306137  0.856346  0.17643  1.724547  2.000000    1.025802   \n",
       "1 2018-01-02  0.306137  0.856346  0.17643  0.992329  1.369596    0.992329   \n",
       "2 2018-01-03  0.306137  0.856346  0.17643  0.992329  0.929568    0.992329   \n",
       "3 2018-01-04  0.306137  0.856346  0.17643  0.992329  0.876569    0.992329   \n",
       "4 2018-01-05  0.306137  0.856346  0.17643  0.992329  0.749651    0.992329   \n",
       "\n",
       "    weather       temp      hum  windspeed  casual  \n",
       "0  0.746165  14.110847  80.5833  10.749882     331  \n",
       "1  0.746165  14.902598  69.6087  16.652113     131  \n",
       "2  1.156206   8.050924  43.7273  16.636703     120  \n",
       "3  1.156206   8.200000  59.0435  10.739832     108  \n",
       "4  1.156206   9.305237  43.6957  12.522300      82  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_pickle(\"datos/casual_cod.pkl\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(730, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X1 = df.drop([\"casual\", \"date\"], axis = 1)\n",
    "y1 = df[\"casual\"]\n",
    "x_train, x_test, y_train, y_test = train_test_split(X1, y1, test_size = 0.2, random_state = 42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(random_state=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arbol = DecisionTreeRegressor(random_state =0)\n",
    "arbol.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig = plt.figure(figsize = (10,6))\n",
    "# tree.plot_tree(arbol, feature_names = x_train.columns, filled = True)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1622776601683795"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_features = np.sqrt(len(x_train.columns))\n",
    "max_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n"
     ]
    }
   ],
   "source": [
    "print(arbol.tree_.max_depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test_dt = arbol.predict(x_test)\n",
    "y_pred_train_dt = arbol.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metricas(y_test, y_train, y_test_pred, y_train_pred, tipo_modelo):\n",
    "    \n",
    "    \n",
    "    resultados = {'MAE': [mean_absolute_error(y_test, y_test_pred), mean_absolute_error(y_train, y_train_pred)],\n",
    "                'MSE': [mean_squared_error(y_test, y_test_pred), mean_squared_error(y_train, y_train_pred)],\n",
    "                'RMSE': [np.sqrt(mean_squared_error(y_test, y_test_pred)), np.sqrt(mean_squared_error(y_train, y_train_pred))],\n",
    "                'R2':  [r2_score(y_test, y_test_pred), r2_score(y_train, y_train_pred)],\n",
    "                 \"set\": [\"test\", \"train\"]}\n",
    "    df = pd.DataFrame(resultados)\n",
    "    df[\"modelo\"] = tipo_modelo\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>set</th>\n",
       "      <th>modelo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>228.267123</td>\n",
       "      <td>123340.746575</td>\n",
       "      <td>351.199013</td>\n",
       "      <td>0.743267</td>\n",
       "      <td>test</td>\n",
       "      <td>Decission Tree I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>train</td>\n",
       "      <td>Decission Tree I</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          MAE            MSE        RMSE        R2    set            modelo\n",
       "0  228.267123  123340.746575  351.199013  0.743267   test  Decission Tree I\n",
       "1    0.000000       0.000000    0.000000  1.000000  train  Decission Tree I"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "dt_results1 = metricas(y_test, y_train, y_pred_test_dt, y_pred_train_dt, \"Decission Tree I\")\n",
    "dt_results1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "param = {\"max_depth\": [2,4,6,8,10], \n",
    "        \"max_features\": [1,2,3,4],  \n",
    "        \"min_samples_split\": [10,50,100],\n",
    "        \"min_samples_leaf\": [10,50,100]} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "gs = GridSearchCV(\n",
    "            estimator=DecisionTreeRegressor(), # tipo de modelo que queremos hacer\n",
    "            param_grid= param, # que hiperparámetros queremos que testee\n",
    "            cv=10, # crossvalidation que aprendimos en la lección de regresión lineal intro. \n",
    "            verbose=-1, # para que no nos printee ningún mensaje en pantalla\n",
    "            return_train_score = True, # para que nos devuelva el valor de las métricas de set de datos de entrenamiento\n",
    "            scoring=\"neg_mean_squared_error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeRegressor(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [2, 4, 6, 8, 10],\n",
       "                         &#x27;max_features&#x27;: [1, 2, 3, 4],\n",
       "                         &#x27;min_samples_leaf&#x27;: [10, 50, 100],\n",
       "                         &#x27;min_samples_split&#x27;: [10, 50, 100]},\n",
       "             return_train_score=True, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "             verbose=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeRegressor(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [2, 4, 6, 8, 10],\n",
       "                         &#x27;max_features&#x27;: [1, 2, 3, 4],\n",
       "                         &#x27;min_samples_leaf&#x27;: [10, 50, 100],\n",
       "                         &#x27;min_samples_split&#x27;: [10, 50, 100]},\n",
       "             return_train_score=True, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "             verbose=-1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=DecisionTreeRegressor(),\n",
       "             param_grid={'max_depth': [2, 4, 6, 8, 10],\n",
       "                         'max_features': [1, 2, 3, 4],\n",
       "                         'min_samples_leaf': [10, 50, 100],\n",
       "                         'min_samples_split': [10, 50, 100]},\n",
       "             return_train_score=True, scoring='neg_mean_squared_error',\n",
       "             verbose=-1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=6, max_features=4, min_samples_leaf=10,\n",
       "                      min_samples_split=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=6, max_features=4, min_samples_leaf=10,\n",
       "                      min_samples_split=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=6, max_features=4, min_samples_leaf=10,\n",
       "                      min_samples_split=10)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mejor_modelo = gs.best_estimator_\n",
    "mejor_modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(40, 20))\n",
    "tree.plot_tree(mejor_modelo, feature_names=x_train.columns, filled=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test_dt2 = mejor_modelo.predict(x_test)\n",
    "y_pred_train_dt2 = mejor_modelo.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>set</th>\n",
       "      <th>modelo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>307.319834</td>\n",
       "      <td>214387.743473</td>\n",
       "      <td>463.020241</td>\n",
       "      <td>0.553753</td>\n",
       "      <td>test</td>\n",
       "      <td>Decision tree II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>282.506431</td>\n",
       "      <td>157702.261102</td>\n",
       "      <td>397.117440</td>\n",
       "      <td>0.662818</td>\n",
       "      <td>train</td>\n",
       "      <td>Decision tree II</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          MAE            MSE        RMSE        R2    set            modelo\n",
       "0  307.319834  214387.743473  463.020241  0.553753   test  Decision tree II\n",
       "1  282.506431  157702.261102  397.117440  0.662818  train  Decision tree II"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_results2 = metricas(y_test, y_train, y_pred_test_dt2, y_pred_train_dt2, \"Decision tree II\")\n",
    "dt_results2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>set</th>\n",
       "      <th>modelo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>228.267123</td>\n",
       "      <td>123340.746575</td>\n",
       "      <td>351.199013</td>\n",
       "      <td>0.743267</td>\n",
       "      <td>test</td>\n",
       "      <td>Decission Tree I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>train</td>\n",
       "      <td>Decission Tree I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>307.319834</td>\n",
       "      <td>214387.743473</td>\n",
       "      <td>463.020241</td>\n",
       "      <td>0.553753</td>\n",
       "      <td>test</td>\n",
       "      <td>Decision tree II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>282.506431</td>\n",
       "      <td>157702.261102</td>\n",
       "      <td>397.117440</td>\n",
       "      <td>0.662818</td>\n",
       "      <td>train</td>\n",
       "      <td>Decision tree II</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          MAE            MSE        RMSE        R2    set            modelo\n",
       "0  228.267123  123340.746575  351.199013  0.743267   test  Decission Tree I\n",
       "1    0.000000       0.000000    0.000000  1.000000  train  Decission Tree I\n",
       "0  307.319834  214387.743473  463.020241  0.553753   test  Decision tree II\n",
       "1  282.506431  157702.261102  397.117440  0.662818  train  Decision tree II"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_decision_results = pd.concat([dt_results1, dt_results2], axis = 0)\n",
    "df_decision_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "param_prueba2 = {\"max_depth\": [6,8,10], \n",
    "        \"max_features\": [1,2,3,4],  \n",
    "        \"min_samples_split\": [5,10,15],\n",
    "        \"min_samples_leaf\": [5,10,15]} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs2 = GridSearchCV(\n",
    "            estimator=DecisionTreeRegressor(), # tipo de modelo que queremos hacer\n",
    "            param_grid= param_prueba2, # que hiperparámetros queremos que testee\n",
    "            cv=10, # crossvalidation que aprendimos en la lección de regresión lineal intro. \n",
    "            verbose=-1, # para que no nos printee ningún mensaje en pantalla\n",
    "            return_train_score = True, # para que nos devuelva el valor de las métricas de set de datos de entrenamiento\n",
    "            scoring=\"neg_mean_squared_error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeRegressor(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [6, 8, 10], &#x27;max_features&#x27;: [1, 2, 3, 4],\n",
       "                         &#x27;min_samples_leaf&#x27;: [5, 10, 15],\n",
       "                         &#x27;min_samples_split&#x27;: [5, 10, 15]},\n",
       "             return_train_score=True, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "             verbose=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeRegressor(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [6, 8, 10], &#x27;max_features&#x27;: [1, 2, 3, 4],\n",
       "                         &#x27;min_samples_leaf&#x27;: [5, 10, 15],\n",
       "                         &#x27;min_samples_split&#x27;: [5, 10, 15]},\n",
       "             return_train_score=True, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "             verbose=-1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=DecisionTreeRegressor(),\n",
       "             param_grid={'max_depth': [6, 8, 10], 'max_features': [1, 2, 3, 4],\n",
       "                         'min_samples_leaf': [5, 10, 15],\n",
       "                         'min_samples_split': [5, 10, 15]},\n",
       "             return_train_score=True, scoring='neg_mean_squared_error',\n",
       "             verbose=-1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs2.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=8, max_features=4, min_samples_leaf=5,\n",
       "                      min_samples_split=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=8, max_features=4, min_samples_leaf=5,\n",
       "                      min_samples_split=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=8, max_features=4, min_samples_leaf=5,\n",
       "                      min_samples_split=10)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mejor_modelo2 = gs2.best_estimator_\n",
    "mejor_modelo2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test_dt3 = mejor_modelo2.predict(x_test)\n",
    "y_pred_train_dt3 = mejor_modelo2.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>set</th>\n",
       "      <th>modelo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>251.669122</td>\n",
       "      <td>137297.324368</td>\n",
       "      <td>370.536536</td>\n",
       "      <td>0.714216</td>\n",
       "      <td>test</td>\n",
       "      <td>Decision tree III</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>191.513760</td>\n",
       "      <td>87230.146539</td>\n",
       "      <td>295.347501</td>\n",
       "      <td>0.813494</td>\n",
       "      <td>train</td>\n",
       "      <td>Decision tree III</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          MAE            MSE        RMSE        R2    set             modelo\n",
       "0  251.669122  137297.324368  370.536536  0.714216   test  Decision tree III\n",
       "1  191.513760   87230.146539  295.347501  0.813494  train  Decision tree III"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_results3 = metricas(y_test, y_train, y_pred_test_dt3, y_pred_train_dt3, \"Decision tree III\")\n",
    "dt_results3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>set</th>\n",
       "      <th>modelo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>228.267123</td>\n",
       "      <td>123340.746575</td>\n",
       "      <td>351.199013</td>\n",
       "      <td>0.743267</td>\n",
       "      <td>test</td>\n",
       "      <td>Decission Tree I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>train</td>\n",
       "      <td>Decission Tree I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>307.319834</td>\n",
       "      <td>214387.743473</td>\n",
       "      <td>463.020241</td>\n",
       "      <td>0.553753</td>\n",
       "      <td>test</td>\n",
       "      <td>Decision tree II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>282.506431</td>\n",
       "      <td>157702.261102</td>\n",
       "      <td>397.117440</td>\n",
       "      <td>0.662818</td>\n",
       "      <td>train</td>\n",
       "      <td>Decision tree II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>251.669122</td>\n",
       "      <td>137297.324368</td>\n",
       "      <td>370.536536</td>\n",
       "      <td>0.714216</td>\n",
       "      <td>test</td>\n",
       "      <td>Decision tree III</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>191.513760</td>\n",
       "      <td>87230.146539</td>\n",
       "      <td>295.347501</td>\n",
       "      <td>0.813494</td>\n",
       "      <td>train</td>\n",
       "      <td>Decision tree III</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          MAE            MSE        RMSE        R2    set             modelo\n",
       "0  228.267123  123340.746575  351.199013  0.743267   test   Decission Tree I\n",
       "1    0.000000       0.000000    0.000000  1.000000  train   Decission Tree I\n",
       "0  307.319834  214387.743473  463.020241  0.553753   test   Decision tree II\n",
       "1  282.506431  157702.261102  397.117440  0.662818  train   Decision tree II\n",
       "0  251.669122  137297.324368  370.536536  0.714216   test  Decision tree III\n",
       "1  191.513760   87230.146539  295.347501  0.813494  train  Decision tree III"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_decision_results3 = pd.concat([df_decision_results, dt_results3], axis = 0)\n",
    "df_decision_results3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#parámetros para el random forest\n",
    "param_rf = {\"max_depth\": [2,4,6,8,10], \n",
    "        \"max_features\": [1,2,3,4],  \n",
    "        \"min_samples_split\": [10,50,100],\n",
    "        \"min_samples_leaf\": [10,50,100]} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs_rf = GridSearchCV(\n",
    "            estimator=RandomForestRegressor(), # tipo de modelo que queremos hacer\n",
    "            param_grid= param_rf, # que hiperparámetros queremos que testee\n",
    "            cv=10, # crossvalidation que aprendimos en la lección de regresión lineal intro. \n",
    "            verbose=-1, # para que no nos printee ningún mensaje en pantalla\n",
    "            return_train_score = True, # para que nos devuelva el valor de las métricas de set de datos de entrenamiento\n",
    "            scoring=\"neg_mean_squared_error\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=RandomForestRegressor(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [2, 4, 6, 8, 10],\n",
       "                         &#x27;max_features&#x27;: [1, 2, 3, 4],\n",
       "                         &#x27;min_samples_leaf&#x27;: [10, 50, 100],\n",
       "                         &#x27;min_samples_split&#x27;: [10, 50, 100]},\n",
       "             return_train_score=True, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "             verbose=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=RandomForestRegressor(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [2, 4, 6, 8, 10],\n",
       "                         &#x27;max_features&#x27;: [1, 2, 3, 4],\n",
       "                         &#x27;min_samples_leaf&#x27;: [10, 50, 100],\n",
       "                         &#x27;min_samples_split&#x27;: [10, 50, 100]},\n",
       "             return_train_score=True, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "             verbose=-1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=RandomForestRegressor(),\n",
       "             param_grid={'max_depth': [2, 4, 6, 8, 10],\n",
       "                         'max_features': [1, 2, 3, 4],\n",
       "                         'min_samples_leaf': [10, 50, 100],\n",
       "                         'min_samples_split': [10, 50, 100]},\n",
       "             return_train_score=True, scoring='neg_mean_squared_error',\n",
       "             verbose=-1)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_rf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=8, max_features=4, min_samples_leaf=10,\n",
       "                      min_samples_split=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=8, max_features=4, min_samples_leaf=10,\n",
       "                      min_samples_split=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_depth=8, max_features=4, min_samples_leaf=10,\n",
       "                      min_samples_split=10)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "bosque = gs_rf.best_estimator_\n",
    "bosque"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test_rf = bosque.predict(x_test)\n",
    "y_pred_train_rf = bosque.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>set</th>\n",
       "      <th>modelo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>240.688191</td>\n",
       "      <td>118767.675344</td>\n",
       "      <td>344.626864</td>\n",
       "      <td>0.752786</td>\n",
       "      <td>test</td>\n",
       "      <td>Random Forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>229.264754</td>\n",
       "      <td>117071.285175</td>\n",
       "      <td>342.156814</td>\n",
       "      <td>0.749690</td>\n",
       "      <td>train</td>\n",
       "      <td>Random Forest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          MAE            MSE        RMSE        R2    set         modelo\n",
       "0  240.688191  118767.675344  344.626864  0.752786   test  Random Forest\n",
       "1  229.264754  117071.285175  342.156814  0.749690  train  Random Forest"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_results_rf = metricas(y_test, y_train, y_pred_test_rf, y_pred_train_rf, \"Random Forest\")\n",
    "dt_results_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#este código lo dejamos pendiente porque nos hemos dado cuenta de que no habíamos metido la semilla para que ejecute siempre los mismos \n",
    "#datos\n",
    "\n",
    "#param = {\"max_depth\": [2, 4, 5],         \"max_features\": [1, 2, 3, 4],# Ponemos como límite el 4 ya que es el resultado de la raiz cuadrada. \n",
    "        \"min_samples_split\": [10, 15, 20], # ponemos valores bajos porque solo tenemos 66 filas\n",
    "        \"min_samples_leaf\": [10, 15, 20], # ponemos valores bajos porque solo tenemos 66 filas\n",
    "        \"random_state\": [99]}"
   ]
  }
 ],
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