Regresion_Logistica.ipynb

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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Importación de librerías y lectura del data frame"
      ],
      "metadata": {
        "id": "2IKijoxVmtHq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IMFxSZJxMmK1"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_secundario = pd.read_csv(\"/content/drive/MyDrive/secondary_data.csv\", delimiter=';')"
      ],
      "metadata": {
        "id": "XDjqlENNMo7z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_secundario.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ihxyfSHDD4rE",
        "outputId": "c0b36ab3-3bae-4dc8-e267-055e9d73bfb3"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 61069 entries, 0 to 61068\n",
            "Data columns (total 21 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   class                 61069 non-null  object \n",
            " 1   cap-diameter          61069 non-null  float64\n",
            " 2   cap-shape             61069 non-null  object \n",
            " 3   cap-surface           46949 non-null  object \n",
            " 4   cap-color             61069 non-null  object \n",
            " 5   does-bruise-or-bleed  61069 non-null  object \n",
            " 6   gill-attachment       51185 non-null  object \n",
            " 7   gill-spacing          36006 non-null  object \n",
            " 8   gill-color            61069 non-null  object \n",
            " 9   stem-height           61069 non-null  float64\n",
            " 10  stem-width            61069 non-null  float64\n",
            " 11  stem-root             9531 non-null   object \n",
            " 12  stem-surface          22945 non-null  object \n",
            " 13  stem-color            61069 non-null  object \n",
            " 14  veil-type             3177 non-null   object \n",
            " 15  veil-color            7413 non-null   object \n",
            " 16  has-ring              61069 non-null  object \n",
            " 17  ring-type             58598 non-null  object \n",
            " 18  spore-print-color     6354 non-null   object \n",
            " 19  habitat               61069 non-null  object \n",
            " 20  season                61069 non-null  object \n",
            "dtypes: float64(3), object(18)\n",
            "memory usage: 9.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_2[\"class\"].loc[1] = \"e\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iwcLV8AJEUus",
        "outputId": "d6b90d8d-18b6-4ae1-8a69-9d1f640cd1a3"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-ead9be5d8ec5>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_2[\"class\"].loc[1] = \"e\"\n"
          ]
        }
      ]
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    {
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      "source": [
        "df_2"
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            "text/plain": [
              "      class  cap-diameter cap-shape cap-surface cap-color  \\\n",
              "0         p         15.26         x           g         o   \n",
              "1         e         16.60         x           g         o   \n",
              "2         p         14.07         x           g         o   \n",
              "3         p         14.17         f           h         e   \n",
              "4         p         14.64         x           h         o   \n",
              "...     ...           ...       ...         ...       ...   \n",
              "61064     p          1.18         s           s         y   \n",
              "61065     p          1.27         f           s         y   \n",
              "61066     p          1.27         s           s         y   \n",
              "61067     p          1.24         f           s         y   \n",
              "61068     p          1.17         s           s         y   \n",
              "\n",
              "      does-bruise-or-bleed gill-attachment gill-spacing gill-color  \\\n",
              "0                        f               e          NaN          w   \n",
              "1                        f               e          NaN          w   \n",
              "2                        f               e          NaN          w   \n",
              "3                        f               e          NaN          w   \n",
              "4                        f               e          NaN          w   \n",
              "...                    ...             ...          ...        ...   \n",
              "61064                    f               f            f          f   \n",
              "61065                    f               f            f          f   \n",
              "61066                    f               f            f          f   \n",
              "61067                    f               f            f          f   \n",
              "61068                    f               f            f          f   \n",
              "\n",
              "       stem-height  ...  stem-root stem-surface stem-color veil-type  \\\n",
              "0            16.95  ...          s            y          w         u   \n",
              "1            17.99  ...          s            y          w         u   \n",
              "2            17.80  ...          s            y          w         u   \n",
              "3            15.77  ...          s            y          w         u   \n",
              "4            16.53  ...          s            y          w         u   \n",
              "...            ...  ...        ...          ...        ...       ...   \n",
              "61064         3.93  ...        NaN          NaN          y       NaN   \n",
              "61065         3.18  ...        NaN          NaN          y       NaN   \n",
              "61066         3.86  ...        NaN          NaN          y       NaN   \n",
              "61067         3.56  ...        NaN          NaN          y       NaN   \n",
              "61068         3.25  ...        NaN          NaN          y       NaN   \n",
              "\n",
              "      veil-color has-ring ring-type spore-print-color habitat season  \n",
              "0              w        t         g               NaN       d      w  \n",
              "1              w        t         g               NaN       d      u  \n",
              "2              w        t         g               NaN       d      w  \n",
              "3              w        t         p               NaN       d      w  \n",
              "4              w        t         p               NaN       d      w  \n",
              "...          ...      ...       ...               ...     ...    ...  \n",
              "61064        NaN        f         f               NaN       d      a  \n",
              "61065        NaN        f         f               NaN       d      a  \n",
              "61066        NaN        f         f               NaN       d      u  \n",
              "61067        NaN        f         f               NaN       d      u  \n",
              "61068        NaN        f         f               NaN       d      u  \n",
              "\n",
              "[61069 rows x 21 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>class</th>\n",
              "      <th>cap-diameter</th>\n",
              "      <th>cap-shape</th>\n",
              "      <th>cap-surface</th>\n",
              "      <th>cap-color</th>\n",
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              "      <th>stem-surface</th>\n",
              "      <th>stem-color</th>\n",
              "      <th>veil-type</th>\n",
              "      <th>veil-color</th>\n",
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              "      <th>ring-type</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>p</td>\n",
              "      <td>15.26</td>\n",
              "      <td>x</td>\n",
              "      <td>g</td>\n",
              "      <td>o</td>\n",
              "      <td>f</td>\n",
              "      <td>e</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>16.95</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>e</td>\n",
              "      <td>16.60</td>\n",
              "      <td>x</td>\n",
              "      <td>g</td>\n",
              "      <td>o</td>\n",
              "      <td>f</td>\n",
              "      <td>e</td>\n",
              "      <td>NaN</td>\n",
              "      <td>w</td>\n",
              "      <td>17.99</td>\n",
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              "      <td>s</td>\n",
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              "      <td>u</td>\n",
              "      <td>w</td>\n",
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              "      <td>g</td>\n",
              "      <td>NaN</td>\n",
              "      <td>d</td>\n",
              "      <td>u</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>p</td>\n",
              "      <td>14.07</td>\n",
              "      <td>x</td>\n",
              "      <td>g</td>\n",
              "      <td>o</td>\n",
              "      <td>f</td>\n",
              "      <td>e</td>\n",
              "      <td>NaN</td>\n",
              "      <td>w</td>\n",
              "      <td>17.80</td>\n",
              "      <td>...</td>\n",
              "      <td>s</td>\n",
              "      <td>y</td>\n",
              "      <td>w</td>\n",
              "      <td>u</td>\n",
              "      <td>w</td>\n",
              "      <td>t</td>\n",
              "      <td>g</td>\n",
              "      <td>NaN</td>\n",
              "      <td>d</td>\n",
              "      <td>w</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>p</td>\n",
              "      <td>14.17</td>\n",
              "      <td>f</td>\n",
              "      <td>h</td>\n",
              "      <td>e</td>\n",
              "      <td>f</td>\n",
              "      <td>e</td>\n",
              "      <td>NaN</td>\n",
              "      <td>w</td>\n",
              "      <td>15.77</td>\n",
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              "      <td>u</td>\n",
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              "      <th>4</th>\n",
              "      <td>p</td>\n",
              "      <td>14.64</td>\n",
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          "text": [
            "<ipython-input-17-0425d746b2ad>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_secundario[\"class\"].loc[1] = \"e\"\n"
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      "cell_type": "code",
      "source": [
        "hola = df_secundario.groupby([\"class\"])"
      ],
      "metadata": {
        "id": "E2HSQQwH4WXZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hola.transform(\"count\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "rAV3QKj-4zih",
        "outputId": "c82ebe0b-8a47-409d-8e77-016ee299484f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       cap-diameter  cap-shape  cap-surface  cap-color  does-bruise-or-bleed  \\\n",
              "0             33888      33888        26475      33888                 33888   \n",
              "1             33888      33888        26475      33888                 33888   \n",
              "2             33888      33888        26475      33888                 33888   \n",
              "3             33888      33888        26475      33888                 33888   \n",
              "4             33888      33888        26475      33888                 33888   \n",
              "...             ...        ...          ...        ...                   ...   \n",
              "61064         33888      33888        26475      33888                 33888   \n",
              "61065         33888      33888        26475      33888                 33888   \n",
              "61066         33888      33888        26475      33888                 33888   \n",
              "61067         33888      33888        26475      33888                 33888   \n",
              "61068         33888      33888        26475      33888                 33888   \n",
              "\n",
              "       gill-attachment  gill-spacing  gill-color  stem-height  stem-width  \\\n",
              "0                27534         19768       33888        33888       33888   \n",
              "1                27534         19768       33888        33888       33888   \n",
              "2                27534         19768       33888        33888       33888   \n",
              "3                27534         19768       33888        33888       33888   \n",
              "4                27534         19768       33888        33888       33888   \n",
              "...                ...           ...         ...          ...         ...   \n",
              "61064            27534         19768       33888        33888       33888   \n",
              "61065            27534         19768       33888        33888       33888   \n",
              "61066            27534         19768       33888        33888       33888   \n",
              "61067            27534         19768       33888        33888       33888   \n",
              "61068            27534         19768       33888        33888       33888   \n",
              "\n",
              "       stem-root  stem-surface  stem-color  veil-type  veil-color  has-ring  \\\n",
              "0           6001         14473       33888       2118        4236     33888   \n",
              "1           6001         14473       33888       2118        4236     33888   \n",
              "2           6001         14473       33888       2118        4236     33888   \n",
              "3           6001         14473       33888       2118        4236     33888   \n",
              "4           6001         14473       33888       2118        4236     33888   \n",
              "...          ...           ...         ...        ...         ...       ...   \n",
              "61064       6001         14473       33888       2118        4236     33888   \n",
              "61065       6001         14473       33888       2118        4236     33888   \n",
              "61066       6001         14473       33888       2118        4236     33888   \n",
              "61067       6001         14473       33888       2118        4236     33888   \n",
              "61068       6001         14473       33888       2118        4236     33888   \n",
              "\n",
              "       ring-type  spore-print-color  habitat  season  \n",
              "0          32829               4589    33888   33888  \n",
              "1          32829               4589    33888   33888  \n",
              "2          32829               4589    33888   33888  \n",
              "3          32829               4589    33888   33888  \n",
              "4          32829               4589    33888   33888  \n",
              "...          ...                ...      ...     ...  \n",
              "61064      32829               4589    33888   33888  \n",
              "61065      32829               4589    33888   33888  \n",
              "61066      32829               4589    33888   33888  \n",
              "61067      32829               4589    33888   33888  \n",
              "61068      32829               4589    33888   33888  \n",
              "\n",
              "[61069 rows x 20 columns]"
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              "      <th></th>\n",
              "      <th>cap-diameter</th>\n",
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              "      <th>veil-color</th>\n",
              "      <th>has-ring</th>\n",
              "      <th>ring-type</th>\n",
              "      <th>spore-print-color</th>\n",
              "      <th>habitat</th>\n",
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              "      <th>1</th>\n",
              "      <td>33888</td>\n",
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              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>27534</td>\n",
              "      <td>19768</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>6001</td>\n",
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              "      <td>33888</td>\n",
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              "      <td>4589</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>26475</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>27534</td>\n",
              "      <td>19768</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>6001</td>\n",
              "      <td>14473</td>\n",
              "      <td>33888</td>\n",
              "      <td>2118</td>\n",
              "      <td>4236</td>\n",
              "      <td>33888</td>\n",
              "      <td>32829</td>\n",
              "      <td>4589</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>26475</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>27534</td>\n",
              "      <td>19768</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>6001</td>\n",
              "      <td>14473</td>\n",
              "      <td>33888</td>\n",
              "      <td>2118</td>\n",
              "      <td>4236</td>\n",
              "      <td>33888</td>\n",
              "      <td>32829</td>\n",
              "      <td>4589</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>26475</td>\n",
              "      <td>33888</td>\n",
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              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
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              "      <td>14473</td>\n",
              "      <td>33888</td>\n",
              "      <td>2118</td>\n",
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              "      <td>4589</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>61064</th>\n",
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              "      <td>33888</td>\n",
              "      <td>27534</td>\n",
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              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
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              "      <td>33888</td>\n",
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              "      <td>4589</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
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              "    <tr>\n",
              "      <th>61065</th>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>26475</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>27534</td>\n",
              "      <td>19768</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>6001</td>\n",
              "      <td>14473</td>\n",
              "      <td>33888</td>\n",
              "      <td>2118</td>\n",
              "      <td>4236</td>\n",
              "      <td>33888</td>\n",
              "      <td>32829</td>\n",
              "      <td>4589</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
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              "    <tr>\n",
              "      <th>61066</th>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>26475</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>27534</td>\n",
              "      <td>19768</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>6001</td>\n",
              "      <td>14473</td>\n",
              "      <td>33888</td>\n",
              "      <td>2118</td>\n",
              "      <td>4236</td>\n",
              "      <td>33888</td>\n",
              "      <td>32829</td>\n",
              "      <td>4589</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
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              "    <tr>\n",
              "      <th>61067</th>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>26475</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>27534</td>\n",
              "      <td>19768</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>6001</td>\n",
              "      <td>14473</td>\n",
              "      <td>33888</td>\n",
              "      <td>2118</td>\n",
              "      <td>4236</td>\n",
              "      <td>33888</td>\n",
              "      <td>32829</td>\n",
              "      <td>4589</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61068</th>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>26475</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>27534</td>\n",
              "      <td>19768</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "      <td>6001</td>\n",
              "      <td>14473</td>\n",
              "      <td>33888</td>\n",
              "      <td>2118</td>\n",
              "      <td>4236</td>\n",
              "      <td>33888</td>\n",
              "      <td>32829</td>\n",
              "      <td>4589</td>\n",
              "      <td>33888</td>\n",
              "      <td>33888</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61069 rows × 20 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-34c39440-f725-4eef-a311-f7beab99be63')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-34c39440-f725-4eef-a311-f7beab99be63 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-34c39440-f725-4eef-a311-f7beab99be63');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exporación de los datos\n",
        "\n",
        "En esta sección se busca explorar todos los datos del Data Frame en donde se va tomar un criterio de decisión para la imputación de los Nans, en donde dicho criterio se utilizara en el resto de los ejercicios de la entrega."
      ],
      "metadata": {
        "id": "D8FVlwy7JwR0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_secundario.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "Eg-3R_y4s77t",
        "outputId": "4bc98ccb-edd4-4972-b2da-10f03f575f00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  class  cap-diameter cap-shape cap-surface cap-color does-bruise-or-bleed  \\\n",
              "0     p         15.26         x           g         o                    f   \n",
              "1     p         16.60         x           g         o                    f   \n",
              "2     p         14.07         x           g         o                    f   \n",
              "3     p         14.17         f           h         e                    f   \n",
              "4     p         14.64         x           h         o                    f   \n",
              "\n",
              "  gill-attachment gill-spacing gill-color  stem-height  ...  stem-root  \\\n",
              "0               e          NaN          w        16.95  ...          s   \n",
              "1               e          NaN          w        17.99  ...          s   \n",
              "2               e          NaN          w        17.80  ...          s   \n",
              "3               e          NaN          w        15.77  ...          s   \n",
              "4               e          NaN          w        16.53  ...          s   \n",
              "\n",
              "  stem-surface stem-color veil-type veil-color has-ring ring-type  \\\n",
              "0            y          w         u          w        t         g   \n",
              "1            y          w         u          w        t         g   \n",
              "2            y          w         u          w        t         g   \n",
              "3            y          w         u          w        t         p   \n",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 61069 entries, 0 to 61068\n",
            "Data columns (total 21 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   class                 61069 non-null  object \n",
            " 1   cap-diameter          61069 non-null  float64\n",
            " 2   cap-shape             61069 non-null  object \n",
            " 3   cap-surface           46949 non-null  object \n",
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            " 5   does-bruise-or-bleed  61069 non-null  object \n",
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            " 13  stem-color            61069 non-null  object \n",
            " 14  veil-type             3177 non-null   object \n",
            " 15  veil-color            7413 non-null   object \n",
            " 16  has-ring              61069 non-null  object \n",
            " 17  ring-type             58598 non-null  object \n",
            " 18  spore-print-color     6354 non-null   object \n",
            " 19  habitat               61069 non-null  object \n",
            " 20  season                61069 non-null  object \n",
            "dtypes: float64(3), object(18)\n",
            "memory usage: 9.8+ MB\n"
          ]
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    {
      "cell_type": "markdown",
      "source": [
        "A continuación voy a ver que puede estar sucediendo con las features que tienen Nans. Para darme una idea de a que se deben los Nans, voy a utilizar la información original del data set, y en base a esto voy a tomar un criterio de decisión para imputarlos."
      ],
      "metadata": {
        "id": "PIuggLu0KTBP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_secundario['cap-surface'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NCC347VsKMIv",
        "outputId": "cff5ff14-f96a-4907-ca53-47ab525f1ab9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['g', 'h', nan, 't', 'y', 'e', 's', 'l', 'd', 'w', 'i', 'k'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
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      "cell_type": "code",
      "source": [
        "df_secundario['gill-attachment'].unique()"
      ],
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      "execution_count": null,
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              "array(['e', nan, 'a', 'd', 's', 'x', 'p', 'f'], dtype=object)"
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              "array([nan, 'c', 'd', 'f'], dtype=object)"
            ]
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              "array(['s', nan, 'b', 'r', 'c', 'f'], dtype=object)"
            ]
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              "array(['u', nan], dtype=object)"
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        "id": "Hetc979FLGy-",
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          "data": {
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              "array(['w', 'y', nan, 'n', 'e', 'u', 'k'], dtype=object)"
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          "metadata": {},
          "execution_count": 11
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      "cell_type": "code",
      "source": [
        "df_secundario['ring-type'].unique()"
      ],
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        "colab": {
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        "id": "hlyqKqSjLOai",
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            "text/plain": [
              "array(['g', 'p', 'e', 'l', 'f', 'm', nan, 'r', 'z'], dtype=object)"
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    {
      "cell_type": "code",
      "source": [
        "df_secundario['spore-print-color'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tjDfQlSuLSoI",
        "outputId": "f8371832-7842-42a0-bb5d-937d3a4c1bb3"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([nan, 'w', 'p', 'k', 'r', 'u', 'n', 'g'], dtype=object)"
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          "metadata": {},
          "execution_count": 13
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Más abajo se muestra el criterio para imputar los Nans dependiendo de la feature. Un dato a tener en cuenta es que el autor utilizar el carácter 'f' para referirse a una característica faltante del hongo, y usa la 'o' para aquellas características que por alguna cuestión de implementación fue incluida en la categoría 'otro'.\n",
        "\n",
        "* Cap-surface: Teniendo en cuenta que todos los hongos tienen una \"gorra\" y que en el data set, está la 'f',  voy a suponer que los Nans pertenecen a la clase 'o'.\n",
        "\n",
        "* gill-attachment: Como hay hongos que no tienen branquias (clase 'f' en gill-color), en esta sección coinciden las 'f'; entonces los Nans los considero de la clase 'o'.\n",
        "\n",
        "* gill-spacing: Sucede los mismo que el caso anterior; entonces los Nans son de la clase 'o'.\n",
        "\n",
        "* stem-root: En la información del data set aparecen clases faltantes en este data set, que son 'z', 'e' y 'u'. A su vez, aparece una clase no especificada llamada 'f'. Para este caso voy a suponer que 'f' quiere decir que no tiene \"raíz\", y que los Nans se distribuyen en alguna de las otras tres clases como 'o'.\n",
        "\n",
        "* stem-surface: Como nos estamos refiriendo a la superficie del tallo, los Nans van a pertenecer a la clase 'o'.\n",
        "\n",
        "* veil-type: Esta clase la tuve que comparar con \"veil-color\", en donde según la información del data set, hay dos tipos de velo: 'u' y 'p', pero la segunda clase no figura. Como en “vail-color” para algunas filas hay colores asignados cuando en \"veil-type\" hay un Nan, voy a suponer que en esos casos hay una 'p' asignada. En los demás casos voy a suponer que el hongo no tiene velo, entonces no hay color y en ambas clases va 'f'.\n",
        "\n",
        "* veil-color: Use el mismo criterio que \"veil-type\".\n",
        "\n",
        "* ring-type: Como en las 'f' de esta categoría y \"has-ring\" coinciden, los Nan va a 'o'.\n",
        "\n",
        "* spore-print-color: Tengo entendido que todos los hongos tienen esporas, entonces los Nans los reemplazo por la clase 'o'.\n"
      ],
      "metadata": {
        "id": "u10dOsIFvNBG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Imputa los Nans dados un data frame, el nombre de la features y un caracter.\n",
        "def imputar_nans(df, nombre_feature, caracter):\n",
        "\n",
        "  df[nombre_feature] = df[nombre_feature].fillna(caracter)\n"
      ],
      "metadata": {
        "id": "mY-1ZXI7LWBO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Esta linea es para agregar la p en ciertos hongos en \"veil-type\".\n",
        "serie_velos = df_secundario[(df_secundario['veil-type'].isna() == True) & (df_secundario['veil-color'].isna() == False)]['veil-type']\n",
        "indices_sv = serie_velos.index.values\n",
        "for i in indices_sv:\n",
        "  df_secundario['veil-type'].loc[i] = 'p'"
      ],
      "metadata": {
        "id": "q-Stu9R1MVXv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "045e6c05-15ff-4506-cfda-5a8eafb9dbe7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-c20033db68ee>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_secundario['veil-type'].loc[i] = 'p'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Esta linea es para reemplazar la 'd' por 'o' ya que no esta escificada en la inforamcion del data set.\n",
        "df_secundario['cap-surface'] = df_secundario[\"cap-surface\"].map(lambda x: 'o' if x == 'd' else x)"
      ],
      "metadata": {
        "id": "1JfprqzcMXSp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "imputar_nans(df_secundario, 'cap-surface', 'o')\n",
        "imputar_nans(df_secundario, 'gill-attachment', 'o')\n",
        "imputar_nans(df_secundario, 'gill-spacing', 'o')\n",
        "imputar_nans(df_secundario, 'stem-root', 'o')\n",
        "imputar_nans(df_secundario, 'stem-surface', 'o')\n",
        "imputar_nans(df_secundario, 'veil-color', 'f')\n",
        "imputar_nans(df_secundario, 'veil-type', 'f')\n",
        "imputar_nans(df_secundario, 'ring-type', 'o')\n",
        "imputar_nans(df_secundario, 'spore-print-color', 'o')"
      ],
      "metadata": {
        "id": "91kZVrUBMyV0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preparación de los datos\n",
        "\n",
        "En esta sección se preparan los datos para realizar una Regresión Logística. Cabe aclarar que por el enunciado del ejercicio, si o si se van a utilizar todas las columnas del data set ya que, más allá de que hay features en donde en su mayoría hay Nans, no puedo descartarlas por los criterios dichos anteriormente; y así mismo, considero que podría utilizar todas las features en este ejercicio."
      ],
      "metadata": {
        "id": "ivXIC9lKNNVv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Separo el target del resto del data frame."
      ],
      "metadata": {
        "id": "pdDd8CPsE3dp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_secundario.drop('class', axis=1)"
      ],
      "metadata": {
        "id": "34_sAwjGM0Eh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df_secundario['class']"
      ],
      "metadata": {
        "id": "nnIFgLB9M-LM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creo un set de entrenamiento y uno de medición."
      ],
      "metadata": {
        "id": "u1KOgzISE_0M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_med, y_train, y_med = train_test_split(X, y, test_size=0.2, random_state=13)"
      ],
      "metadata": {
        "id": "5YgWFGVbM_Nu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creo un set de testeo y uno de validación a partir del de medición. Con respecto a al set de entrenamineto y de validación, conviene que el primero tenga muchos mas datos(en este caso el 80%) que el segundo."
      ],
      "metadata": {
        "id": "qYDaSNgUFICC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_test, X_validation, y_test, y_validation = train_test_split(X_med, y_med, test_size=0.5, random_state=13)"
      ],
      "metadata": {
        "id": "7LVv5aY-NChG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kiY8HtVtqnCp"
      },
      "source": [
        "Voy a utilizar una variable binaria {0, 1} para representar al target (\"class\") en donde 1 es para indicar que el hongo es venenoso ('p') y 0 si el hongo es comestible ('e')."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train =  y_train.map(lambda x: 1 if x == 'p' else 0)"
      ],
      "metadata": {
        "id": "nrdaiGsiNJG0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_validation =  y_validation.map(lambda x: 1 if x == 'p' else 0)"
      ],
      "metadata": {
        "id": "uOPEh5ixN11C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test =  y_test.map(lambda x: 1 if x == 'p' else 0)"
      ],
      "metadata": {
        "id": "q4pXPUM1OBQe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-HBc5FyxVTxm"
      },
      "source": [
        "Aplico un encoding a todas las columnas. En este caso, al no tener una gran cantidad de columnas por cada clase, voy a utilizar OHE para todas las features categóricas."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Aplica OHE a cada columna de X_train, X_test y X_validation dado una feature y su nombre.\n",
        "def aplicar_ohe(nombre_feature, columnas):\n",
        "\n",
        "  global X_train, X_test, X_validation\n",
        "  ohe = OneHotEncoder()\n",
        "  encoded_train = ohe.fit_transform(X_train[[nombre_feature]]).todense().astype(int)\n",
        "  encoded_test = ohe.fit_transform(X_test[[nombre_feature]]).todense().astype(int)\n",
        "  encoded_validation = ohe.transform(X_validation[[nombre_feature]]).todense().astype(int)\n",
        "  X_train = X_train.reset_index().drop(['index', nombre_feature],axis=1).join(pd.DataFrame(encoded_train))\n",
        "  X_test = X_test.reset_index().drop(['index', nombre_feature],axis=1).join(pd.DataFrame(encoded_test))\n",
        "  X_validation = X_validation.reset_index().drop(['index', nombre_feature],axis=1).join(pd.DataFrame(encoded_validation))\n",
        "  X_train = X_train.rename(columns=columnas)\n",
        "  X_test = X_test.rename(columns=columnas)\n",
        "  X_validation = X_validation.rename(columns=columnas)"
      ],
      "metadata": {
        "id": "i0I3NuJWOJsU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con \"cap-shape\":"
      ],
      "metadata": {
        "id": "ZEf8p-yKFp1O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('cap-shape', {0:'b0',1:'c0',2:'f0',3:'o0',4:'p0',5:'s0',6:'x0'})"
      ],
      "metadata": {
        "id": "J3KrDH5WFr5V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con \"cap-surface\":"
      ],
      "metadata": {
        "id": "iKxW5j3jFxl6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('cap-surface', {0:'e1',1:'g1',2:'h1',3:'i1',4:'k1',5:'l1',6:'o1',7:'s1',8:'t1',9:'w1',10:'y1'})"
      ],
      "metadata": {
        "id": "kk5x1aHUF9Kz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con \"cap-color\":"
      ],
      "metadata": {
        "id": "C3D7ZEZfGRWE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('cap-color', {0:'b2',1:'e2',2:'g2',3:'k2',4:'l2',5:'n2',6:'o2', 7:'p2', 8:'r2', 9:'u2', 10:'w2', 11:'y2'})"
      ],
      "metadata": {
        "id": "oW1-2Z8QGWS1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con \"does-bruise-or-bleed\":"
      ],
      "metadata": {
        "id": "X0MyYuWoGb6I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('does-bruise-or-bleed', {0:'f3',1:'t3'})"
      ],
      "metadata": {
        "id": "EiRcQbbAGf6R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-kFw7zvUm9Mc"
      },
      "source": [
        "Con \"gill-attachment\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('gill-attachment', {0:'a4',1:'d4',2:'e4',3:'f4',4:'o4',5:'p4',6:'s4', 7:'x4'})"
      ],
      "metadata": {
        "id": "GXKLZfybGqFO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wg7UsPOo85D"
      },
      "source": [
        "Con \"gill-spacing\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('gill-spacing', {0:'c5',1:'d5',2:'f5',3:'o5'})"
      ],
      "metadata": {
        "id": "F1Y9kCZuGw93"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XEhrQKlCpoua"
      },
      "source": [
        "Con \"gill-color\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('gill-color', {0:'b6',1:'e6',2:'f6',3:'g6',4:'k6',5:'n6',6:'o6', 7:'p6', 8:'r6', 9:'u6', 10:'w6', 11:'y6'})"
      ],
      "metadata": {
        "id": "sXOV_2M_G7Iq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQkm_l8eqK14"
      },
      "source": [
        "Con \"stem-root\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('stem-root', {0:'b7',1:'c7',2:'f7',3:'o7',4:'r7',5:'s7'})"
      ],
      "metadata": {
        "id": "09slsZtPHEld"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VKSKObWMrCtF"
      },
      "source": [
        "Con \"stem-surface\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('stem-surface', {0:'f8',1:'g8',2:'h8',3:'i8',4:'k8',5:'o8',6:'s8', 7:'t8',8:'y8'})"
      ],
      "metadata": {
        "id": "6rLNb1IfHMGB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LLwKgt4ir9KL"
      },
      "source": [
        "Con \"stem-color\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('stem-color', {0:'b9',1:'e9',2:'f9',3:'g9',4:'k9',5:'l9',6:'n9', 7:'o9', 8:'p9', 9:'r9', 10:'u9', 11:'w9', 12:'y9'})"
      ],
      "metadata": {
        "id": "0RFuDi2WHSt6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hLLCfJlOsn5e"
      },
      "source": [
        "Con \"veil-type\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('veil-type', {0:'f10',1:'p10',2:'u10'})"
      ],
      "metadata": {
        "id": "aARq3pAnHbeX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T9sXR0Fzs-ZV"
      },
      "source": [
        "Con \"veil-color\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('veil-color', {0:'e11',1:'h11',2:'n11',3:'o11',4:'u11',5:'w11',6:'y11'})"
      ],
      "metadata": {
        "id": "fW-dRMM9HkrR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "by1Uz3RIuxmf"
      },
      "source": [
        "Con \"has-ring\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('has-ring', {0:'f12',1:'t12'})"
      ],
      "metadata": {
        "id": "4Uh0Kt0zHo_c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HqMqt4rpvfgp"
      },
      "source": [
        "Con \"ring-type\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('ring-type', {0:'e13',1:'f13',2:'g13',3:'l13',4:'m13',5:'o13',6:'p13', 7:'r13', 8:'z13'})"
      ],
      "metadata": {
        "id": "fz9XyQVMHv7Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m5kE3wzowSST"
      },
      "source": [
        "Con \"spore-print-color\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('spore-print-color', {0:'g14',1:'k14',2:'n14',3:'o14',4:'p14',5:'r14',6:'u14', 7:'w14'})"
      ],
      "metadata": {
        "id": "N5Behl4XH54T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FDOleFR7yGgZ"
      },
      "source": [
        "Con \"habitat\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('habitat', {0:'d15',1:'g15',2:'h15',3:'l15',4:'m15',5:'p15',6:'u15', 7:'w15'})"
      ],
      "metadata": {
        "id": "XWDj_7MQH_pp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_kId_v6Uy6kD"
      },
      "source": [
        "Con \"season\":"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aplicar_ohe('season', {0:'a16',1:'s16',2:'u16',3:'w16'})"
      ],
      "metadata": {
        "id": "ttOb0llwIGr1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aclaración: Ya sé que los nombres no son los ideales para las columnas, la idea es que pueda aplicar OHE para poder realizar el entrenamiento y se pueda reconocer a que feature pertenece cada categoría; también por esta razón no utilice un ciclo for para encodear todas las features al mismo tiempo y preferí separarlas por nombre."
      ],
      "metadata": {
        "id": "5gZUQiLwsHIE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "Ru5h8OtfO8sA",
        "outputId": "11bb75c3-9d39-4f94-979a-bb61a325ecf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       cap-diameter  stem-height  stem-width  b0  c0  f0  o0  p0  s0  x0  ...  \\\n",
              "0             15.74         5.02       28.73   0   0   1   0   0   0   0  ...   \n",
              "1              1.27         3.79        5.63   0   0   1   0   0   0   0  ...   \n",
              "2             12.05         6.03       23.98   0   0   1   0   0   0   0  ...   \n",
              "3              8.08         6.04       14.95   0   0   0   0   0   0   1  ...   \n",
              "4              6.44        12.23       12.77   0   0   0   0   0   0   1  ...   \n",
              "...             ...          ...         ...  ..  ..  ..  ..  ..  ..  ..  ...   \n",
              "48850          8.53         3.63       25.28   0   0   0   1   0   0   0  ...   \n",
              "48851         10.66         5.10       19.52   0   0   1   0   0   0   0  ...   \n",
              "48852          1.62         3.73        2.63   0   0   0   0   0   0   1  ...   \n",
              "48853          1.29         5.00        2.45   0   0   0   0   0   0   1  ...   \n",
              "48854          2.46         3.41        2.38   0   0   1   0   0   0   0  ...   \n",
              "\n",
              "       h15  l15  m15  p15  u15  w15  a16  s16  u16  w16  \n",
              "0        0    0    0    0    0    0    1    0    0    0  \n",
              "1        0    0    0    0    0    0    1    0    0    0  \n",
              "2        0    0    0    0    0    0    1    0    0    0  \n",
              "3        0    0    0    0    0    0    1    0    0    0  \n",
              "4        0    0    0    0    0    0    0    0    1    0  \n",
              "...    ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
              "48850    0    0    0    0    0    0    0    1    0    0  \n",
              "48851    0    0    0    0    0    0    1    0    0    0  \n",
              "48852    0    0    0    0    0    0    1    0    0    0  \n",
              "48853    0    0    0    0    0    0    1    0    0    0  \n",
              "48854    0    0    0    0    0    0    0    0    1    0  \n",
              "\n",
              "[48855 rows x 128 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0a126521-115c-4351-a5e0-08d7d269f0b3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>cap-diameter</th>\n",
              "      <th>stem-height</th>\n",
              "      <th>stem-width</th>\n",
              "      <th>b0</th>\n",
              "      <th>c0</th>\n",
              "      <th>f0</th>\n",
              "      <th>o0</th>\n",
              "      <th>p0</th>\n",
              "      <th>s0</th>\n",
              "      <th>x0</th>\n",
              "      <th>...</th>\n",
              "      <th>h15</th>\n",
              "      <th>l15</th>\n",
              "      <th>m15</th>\n",
              "      <th>p15</th>\n",
              "      <th>u15</th>\n",
              "      <th>w15</th>\n",
              "      <th>a16</th>\n",
              "      <th>s16</th>\n",
              "      <th>u16</th>\n",
              "      <th>w16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15.74</td>\n",
              "      <td>5.02</td>\n",
              "      <td>28.73</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.27</td>\n",
              "      <td>3.79</td>\n",
              "      <td>5.63</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12.05</td>\n",
              "      <td>6.03</td>\n",
              "      <td>23.98</td>\n",
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              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.08</td>\n",
              "      <td>6.04</td>\n",
              "      <td>14.95</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.44</td>\n",
              "      <td>12.23</td>\n",
              "      <td>12.77</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>48850</th>\n",
              "      <td>8.53</td>\n",
              "      <td>3.63</td>\n",
              "      <td>25.28</td>\n",
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              "      <th>48851</th>\n",
              "      <td>10.66</td>\n",
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              "      <td>1.62</td>\n",
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              "      <th>48853</th>\n",
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              "    <tr>\n",
              "      <th>48854</th>\n",
              "      <td>2.46</td>\n",
              "      <td>3.41</td>\n",
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              "<p>48855 rows × 128 columns</p>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_validation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "1pTWCUO-O9aw",
        "outputId": "6324a169-f443-4b67-a120-c4372c62af22"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
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              "      cap-diameter  stem-height  stem-width  b0  c0  f0  o0  p0  s0  x0  ...  \\\n",
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              "...            ...          ...         ...  ..  ..  ..  ..  ..  ..  ..  ...   \n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.69</td>\n",
              "      <td>7.84</td>\n",
              "      <td>14.56</td>\n",
              "      <td>0</td>\n",
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              "      <td>10.65</td>\n",
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              "      <th>6103</th>\n",
              "      <td>5.20</td>\n",
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              "      <td>8.51</td>\n",
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              "      <th>6105</th>\n",
              "      <td>9.27</td>\n",
              "      <td>11.46</td>\n",
              "      <td>32.54</td>\n",
              "      <td>0</td>\n",
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              "      <th>6106</th>\n",
              "      <td>6.26</td>\n",
              "      <td>6.09</td>\n",
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              "  </tbody>\n",
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              "<p>6107 rows × 128 columns</p>\n",
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              "                                                     [key], {});\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "oQqbYsYdPHUr",
        "outputId": "1b366375-8fa1-47eb-ea8c-3623e60b21a3"
      },
      "execution_count": null,
      "outputs": [
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              "      cap-diameter  stem-height  stem-width  b0  c0  f0  o0  p0  s0  x0  ...  \\\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6104</th>\n",
              "      <td>4.16</td>\n",
              "      <td>5.16</td>\n",
              "      <td>10.51</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6105</th>\n",
              "      <td>4.51</td>\n",
              "      <td>6.44</td>\n",
              "      <td>9.02</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6106</th>\n",
              "      <td>4.10</td>\n",
              "      <td>5.69</td>\n",
              "      <td>7.02</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6107 rows × 128 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d02358a-9cd9-48b5-9525-d4f8b8df341b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0d02358a-9cd9-48b5-9525-d4f8b8df341b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0d02358a-9cd9-48b5-9525-d4f8b8df341b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Entrenamiento y validación del modelo\n",
        "\n",
        "En esta sección se encuentra el entrenamiento y búsqueda de híper parámetros del modelo mediante los set de entrenamiento y validación."
      ],
      "metadata": {
        "id": "KnzgwSqcI-cw"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b6Y5W4tVRRoH"
      },
      "source": [
        "Entreno una Regresión Logística."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "log_reg = LogisticRegression(random_state = 13, max_iter=2000)\n",
        "\n",
        "params = [\n",
        "    {'penalty': ['l1', 'l2', 'elasticnet', 'none']},\n",
        "    {'C': [0.001, 0.01,0.1,1 ,10, 100]},\n",
        "]\n",
        "\n",
        "rs = RandomizedSearchCV(log_reg, param_distributions=params, n_iter=10, random_state=13, scoring='roc_auc')"
      ],
      "metadata": {
        "id": "7eHE0ZufNlLP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rs.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        },
        "id": "So86uG-PN_Ut",
        "outputId": "f67ae85d-738d-4b4a-d8cf-2b851f039d6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n",
            "10 fits failed out of a total of 50.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "5 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 1162, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 54, in _check_solver\n",
            "    raise ValueError(\n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "5 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 1162, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 54, in _check_solver\n",
            "    raise ValueError(\n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:952: UserWarning: One or more of the test scores are non-finite: [       nan 0.93597695        nan 0.9374179  0.86065063 0.9105792\n",
            " 0.92938704 0.93597695 0.9372724  0.93740717]\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(estimator=LogisticRegression(max_iter=2000, random_state=13),\n",
              "                   param_distributions=[{'penalty': ['l1', 'l2', 'elasticnet',\n",
              "                                                     'none']},\n",
              "                                        {'C': [0.001, 0.01, 0.1, 1, 10, 100]}],\n",
              "                   random_state=13, scoring='roc_auc')"
            ],
            "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>RandomizedSearchCV(estimator=LogisticRegression(max_iter=2000, random_state=13),\n",
              "                   param_distributions=[{&#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;,\n",
              "                                                     &#x27;none&#x27;]},\n",
              "                                        {&#x27;C&#x27;: [0.001, 0.01, 0.1, 1, 10, 100]}],\n",
              "                   random_state=13, scoring=&#x27;roc_auc&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(estimator=LogisticRegression(max_iter=2000, random_state=13),\n",
              "                   param_distributions=[{&#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;,\n",
              "                                                     &#x27;none&#x27;]},\n",
              "                                        {&#x27;C&#x27;: [0.001, 0.01, 0.1, 1, 10, 100]}],\n",
              "                   random_state=13, scoring=&#x27;roc_auc&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=2000, random_state=13)</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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=2000, random_state=13)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rs.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yir29lbsODBh",
        "outputId": "196cb928-aee3-4610-c64c-ee2aa314ccbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'penalty': 'none'}"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = rs.best_estimator_"
      ],
      "metadata": {
        "id": "AF7Rd44cbJvC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Entreno el modelo con los mejores parámetros encontrados."
      ],
      "metadata": {
        "id": "Cdf7yEL4vZ9c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "KZrtmp6qbNbD",
        "outputId": "d8b0890a-b824-46a6-9388-da5da05ca989"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=2000, penalty='none', random_state=13)"
            ],
            "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>LogisticRegression(max_iter=2000, penalty=&#x27;none&#x27;, random_state=13)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=2000, penalty=&#x27;none&#x27;, random_state=13)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Utilizo el set de validación para la búsqueda de los híper parámetros."
      ],
      "metadata": {
        "id": "1Jm6yY1kZ9if"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_validation = model.predict(X_validation)"
      ],
      "metadata": {
        "id": "ajrym4KTbR9t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_validation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MCG1efQEbacw",
        "outputId": "1bfaca39-2305-4682-f35c-0f64408b8806"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Realizo una matriz de confusión para el set de validación."
      ],
      "metadata": {
        "id": "DuCG0rN0aXJx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matriz_confusion_validation = confusion_matrix(y_validation, y_pred_validation)"
      ],
      "metadata": {
        "id": "FpA3ucCCaTsg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "matriz_confusion_validation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DuEttHYaafDH",
        "outputId": "6d8ec635-4cb1-4dab-c5aa-2701e91690fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2320,  403],\n",
              "       [ 420, 2964]])"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(matriz_confusion_validation, annot=True, cmap='Blues', fmt='g')\n",
        "plt.title('Matriz de Confusión')\n",
        "plt.xlabel('Predicción')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "zTk3H3xIahq4",
        "outputId": "80208546-3133-4f5d-a37e-821673ee217d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Doy las mediciones del set de validación.\n"
      ],
      "metadata": {
        "id": "DwgI5qLcDllx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "roc_auc = roc_auc_score(y_validation, model.predict_proba(X_validation)[:, 1])\n",
        "print(\"ROC AUC SCORE:\", roc_auc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t3CLg8Oibw1a",
        "outputId": "71c8b922-8673-4129-af20-267eed315852"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROC AUC SCORE: 0.9385660762144381\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_validation, y_pred_validation)\n",
        "print(\"ACCURACY SCORE:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_hzCa92hcGz0",
        "outputId": "ba92fe8d-df94-420c-bc7a-de19e3522c69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ACCURACY SCORE: 0.8652366137219584\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pruebas realizadas\n",
        "\n",
        "En esta sección están las pruebas realizados con el set de validación en busca de los híper parámetros. Cabe aclarar que al ser un modelo más simple que los otros, no se profundizo demasiado en esta sección.\n"
      ],
      "metadata": {
        "id": "0tY3iDH7EBpz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pruebas:\n",
        "* Prueba 1: Con \"C\" = 10 y todos los demás parámetros por defecto sin convergencia del modelo:\n",
        "ROC AUC SCORE: 0.9312605213100209, ACCURACY SCORE: 0.8550843294579991, Matriz de confusión:([[2302,421],[ 464,2920]]).\n",
        "\n",
        "* Prueba 2: Con \"penalty\" = \"none\", \"max iter\" = 500 y todos los demás parámetros por defecto sin convergencia del modelo:\n",
        "ROC AUC SCORE: 0.9385575028932247, ACCURACY SCORE: 0.8634354019977075, Matriz de confusión:([[2314,409],[ 425,2959]]).\n",
        "\n",
        "* Prueba 3: Con \"C\" = 100, max iter = 1000 y todos los demás parámetros por defeco sin convergencia del modelo:\n",
        "ROC AUC SCORE: 0.9385584796007046, ACCURACY SCORE: 0.8652366137219584, Matriz de confusión:([[2320,403],[ 420,2964]]).\n",
        "\n",
        "* Prueba 4: Con \"penality\" = \"none\", \"max iter\" = 2000 y todos los demás parámetros por defeco con convergencia del modelo:\n",
        "ROC AUC SCORE: 0.9385660762144381, ACCURACY SCORE: 0.8652366137219584, Matriz de confusión:([[2320,403],[420,2964]]).\n",
        "\n",
        "Como puede verse, con respecto al set de validación, el mejor Roc Auc Score para las pruebas realizadas es de\n",
        "0.9385660762144381, el cual, es la métrica utilizada en este informe para evaluar la eficacia del metodo utilizado.\n",
        "Para este modelo,  la diferencia de las métricas obtenidas durante todas las pruebas, resulta ser  mínima; en donde el modelo no se pensó para ser perfecto, sino que sea un modelo simple y que cumpla con lo pedido por la consigna.\n"
      ],
      "metadata": {
        "id": "D8AXFw8fih8d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testeo del modelo con el set de testeo\n",
        "\n",
        "La idea de esta seccion es ver algunas metricas del modelo final.\n"
      ],
      "metadata": {
        "id": "1ZiJsxO0vfFx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_test = model.predict(X_test)"
      ],
      "metadata": {
        "id": "vQ-cG1AuuvlY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Realizo una matriz de confusión para el set de testeo."
      ],
      "metadata": {
        "id": "OdrBCKJCwF8S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matriz_confusion_test = confusion_matrix(y_test, y_pred_test)"
      ],
      "metadata": {
        "id": "1xtfLniCwKuS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(matriz_confusion_test, annot=True, cmap='Blues', fmt='g')\n",
        "plt.title('Matriz de Confusión')\n",
        "plt.xlabel('Predicción')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "WP7jL7uXwUUZ",
        "outputId": "6bcb11d7-0fa8-4f06-ea49-28e79609faf6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Doy las métricas del set de testeo."
      ],
      "metadata": {
        "id": "B8wfnmoWwZHx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "roc_auc_test = roc_auc_score(y_test, model.predict_proba(X_test)[:, 1])\n",
        "print(\"ROC AUC SCORE:\", roc_auc_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ulw8mwC5wdwa",
        "outputId": "75f665ac-9c3f-4c39-d4a8-516ebc57d37d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROC AUC SCORE: 0.9387020609811669\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred_test)\n",
        "print(\"ACCURACY SCORE:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AFyEQcV5wmcr",
        "outputId": "d0dc6320-b528-4431-9e9c-a1cfc31dee1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ACCURACY SCORE: 0.8603242181103652\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "En este caso el score obtenido es muy similar al de validación, el cual, en  ROC AUC SCORE, da 0. 9387020609811669. A continuación doy un archivo.csv con las predicciones."
      ],
      "metadata": {
        "id": "_LzWSAfPxBj4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_test = pd.DataFrame(y_test)"
      ],
      "metadata": {
        "id": "uFZ5C2FJxR6U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.columns = ['Actual']"
      ],
      "metadata": {
        "id": "CBUcrdXGxh8A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test[\"Prediccion\"] = y_pred_test"
      ],
      "metadata": {
        "id": "tRcm47K2xqqm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "DgmVDipRx4Ma",
        "outputId": "dce7fbe8-df78-448c-feb1-e51968265792"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Actual  Prediccion\n",
              "22594       1           1\n",
              "60018       1           1\n",
              "38408       1           1\n",
              "43407       1           1\n",
              "52538       0           0\n",
              "...       ...         ...\n",
              "53557       1           1\n",
              "35871       1           1\n",
              "51464       0           0\n",
              "37312       1           1\n",
              "19368       1           1\n",
              "\n",
              "[6107 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f1cf5a57-e1ac-4afd-89cb-d37e0f64ed56\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Actual</th>\n",
              "      <th>Prediccion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>22594</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60018</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38408</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43407</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52538</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53557</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35871</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51464</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37312</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19368</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6107 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f1cf5a57-e1ac-4afd-89cb-d37e0f64ed56')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f1cf5a57-e1ac-4afd-89cb-d37e0f64ed56 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f1cf5a57-e1ac-4afd-89cb-d37e0f64ed56');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.to_csv('test.csv')"
      ],
      "metadata": {
        "id": "SwNfHzoazqbj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "El archivo.csv aparecerá para descargarse en la notebook."
      ],
      "metadata": {
        "id": "c7bK_EK31pRn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importancia de las Features\n",
        "\n",
        "En esta sección se encuentra la importancia de las features de la Regresión Logística."
      ],
      "metadata": {
        "id": "WTWjFJnmjRkz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Suma todas las importancias de las features dado un data frame de importancias individuales, uno con las importancias\n",
        "# sumadas, las features individuales y la feature a la que pertenecen.\n",
        "def sumar_importancias(df_importancias, df_importancias_sumadas, features_individuales, feature):\n",
        "\n",
        "  suma_importancias = 0\n",
        "\n",
        "  for i in features_individuales:\n",
        "    suma_importancias += df_importancias[i].sum()\n",
        "  df_importancias_sumadas[feature] = suma_importancias"
      ],
      "metadata": {
        "id": "xjlwbLAxjK3m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "importancias_individuales = pd.DataFrame(np.abs(model.coef_[0])).T\n",
        "importancias_individuales.columns = X_train.columns\n",
        "importancias_sumadas = pd.DataFrame()\n",
        "importancias_sumadas['cap-diameter'] = importancias_individuales['cap-diameter']\n",
        "importancias_sumadas['stem-height'] = importancias_individuales['stem-height']\n",
        "importancias_sumadas['stem-width'] = importancias_individuales['stem-width']\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['b0','c0','f0','o0','p0','s0','x0'], 'cap-shape')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['e1','g1','h1','i1','k1','l1','o1','s1','t1','w1','y1'], 'cap-surface')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['b2','e2','g2','k2','l2','n2','o2','p2','r2','u2','w2','y2'], 'cap-color')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['f3', 't3'], 'does-bruise-or-bleed')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['a4','d4','e4','f4','o4','p4','s4','x4'], 'gill-attachment')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['c5','d5','f5','o5'], 'gill-spacing')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['b6','e6','f6','g6','k6','n6','o6', 'p6','r6','u6','w6','y6'], 'gill-color')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['b7','c7','f7','o7','r7','s7'], 'stem-root')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['f8','g8','h8','i8','k8','o8','s8','t8','y8'], 'stem-surface')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['b9','e9','f9','g9','k9','l9','n9','o9','p9','r9','u9','w9','y9'], 'stem-color')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['f10','p10','u10'], 'veil-type')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['e11','h11','n11','o11','u11','w11','y11'], 'veil-color')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['f12','t12'], 'has-ring')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['e13','f13','g13','l13','m13','o13','p13','r13','z13'], 'ring-type')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['g14','k14','n14','o14','p14','r14','u14','w14'], 'spore-print-color')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['d15','g15','h15','l15','m15','p15','u15','w15'], 'habitat')\n",
        "sumar_importancias(importancias_individuales, importancias_sumadas, ['a16','s16','u16','w16'], 'season')\n",
        "importancias_sumadas = importancias_sumadas.T.rename(columns={0:\"Importancia\"})"
      ],
      "metadata": {
        "id": "0NoJmZ3lj_uJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "importancias_sumadas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "QgIyxHunlhkE",
        "outputId": "0e555dff-f614-44ac-fac7-9020112c3f3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Importancia\n",
              "cap-diameter             0.046432\n",
              "stem-height              0.116543\n",
              "stem-width               0.023430\n",
              "cap-shape                8.952411\n",
              "cap-surface             12.303319\n",
              "cap-color               13.586107\n",
              "does-bruise-or-bleed     6.865965\n",
              "gill-attachment         15.139951\n",
              "gill-spacing             6.865965\n",
              "gill-color               8.870241\n",
              "stem-root               66.441403\n",
              "stem-surface            66.172674\n",
              "stem-color              36.633200\n",
              "veil-type               23.493143\n",
              "veil-color             126.071178\n",
              "has-ring                 6.865965\n",
              "ring-type               80.440801\n",
              "spore-print-color       62.147846\n",
              "habitat                 55.784353\n",
              "season                   6.865965"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a7364adf-341a-46db-8f23-3b5d2f86fa21\">\n",
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              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Importancia</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>cap-diameter</th>\n",
              "      <td>0.046432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stem-height</th>\n",
              "      <td>0.116543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stem-width</th>\n",
              "      <td>0.023430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cap-shape</th>\n",
              "      <td>8.952411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cap-surface</th>\n",
              "      <td>12.303319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cap-color</th>\n",
              "      <td>13.586107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>does-bruise-or-bleed</th>\n",
              "      <td>6.865965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gill-attachment</th>\n",
              "      <td>15.139951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gill-spacing</th>\n",
              "      <td>6.865965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gill-color</th>\n",
              "      <td>8.870241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stem-root</th>\n",
              "      <td>66.441403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stem-surface</th>\n",
              "      <td>66.172674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stem-color</th>\n",
              "      <td>36.633200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>veil-type</th>\n",
              "      <td>23.493143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>veil-color</th>\n",
              "      <td>126.071178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>has-ring</th>\n",
              "      <td>6.865965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ring-type</th>\n",
              "      <td>80.440801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spore-print-color</th>\n",
              "      <td>62.147846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>habitat</th>\n",
              "      <td>55.784353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>season</th>\n",
              "      <td>6.865965</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7364adf-341a-46db-8f23-3b5d2f86fa21')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a7364adf-341a-46db-8f23-3b5d2f86fa21 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a7364adf-341a-46db-8f23-3b5d2f86fa21');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.barh(importancias_sumadas.index, importancias_sumadas[\"Importancia\"],align='center')\n",
        "plt.title(\"Importancia de las Features con Regresión Logística.\")\n",
        "plt.xlabel(\"Importancia\")\n",
        "plt.ylabel(\"Feature\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "_J8y5VDkmGvn",
        "outputId": "c2c58f7b-eb10-4aa0-c689-b3e8e65c4b43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Feature')"
            ]
          },
          "metadata": {},
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Como puede verse en el gráfico, claramente para este modelo las features numéricas \"cap-diameter\", \"stem-height\" y \"stem-width\" no aportan prácticamente nada al modelo. Después en líneas generales, la mayoría de las features aportan poco al modelo, a excepción de \"veil-color\" (la que más aporta y con diferencia), \"habitat\", \"spore-print-color\", \"stem-root\" y \"stem-surface\". Cabe destacar, que estas importancias podrían variar significativamente si se utilizasen distintos híper parámetros a los obtenidos en la última prueba realizada."
      ],
      "metadata": {
        "id": "LSxD2Je5t1YB"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0AlNDE55mOGn"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}