Dataset_no_me_comas.ipynb

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        "<a href=\"https://colab.research.google.com/github/bringol/IA_TP_GRUPO_2/blob/main/Dataset_no_me_comas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F4R22rMMtXnf"
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      "source": [
        "# Dataset: Yellow Submarine\n",
        "----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8zbtY8MXtXnh"
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      "source": [
        "Cada fila representa un hongo, cada columna contiene atributos de este.\n",
        "\n",
        "La columna *classes* indica si el hongo es venenoso o no.\n",
        "\n",
        "hongos | atributos\n",
        ":----:   | :----:\n",
        "8124     | 23\n"
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "_enlxsmytXni"
      },
      "source": [
        "# Variables\n",
        "\n",
        "Nombre   |  Posibles valores\n",
        ":------- |  :---\n",
        "cap-shape   | (bell, conical, convex, flat, knobbed, sunken)\n",
        "cap-surface | (fibrous, grooves, scaly, smooth)\n",
        "cap-color | (brown, buff, cinnamon, gray, green, pink, purple, red, white, yellow)\n",
        "bruises | (bruises, no)\n",
        "odor | (almond, anise, creosote, fishy, foul, musty, none, pungent, spicy)\n",
        "gill-attachment | (attached, descending, free, notched)\n",
        "gill-spacing | (close, crowded, distant)\n",
        "gill-size | (broad, narrow)\n",
        "gill-color | (black, brown, buff, chocolate, gray, green, orange, pink, purple, red, white, yellow)\n",
        "stalk-shape | (enlarging, tapering)\n",
        "stalk-root | (bulbous, club, cup, equal, rhizomorphs, rooted, missing)\n",
        "stalk-surface-above-ring | (fibrous, scaly, silky, smooth)\n",
        "stalk-surface-below-ring | (fibrous, scaly, silky, smooth)\n",
        "stalk-color-above-ring | (brown, buff, cinnamon, gray, orange, pink, red, white, yellow)\n",
        "stalk-color-below-ring | (brown, buff, cinnamon, gray, orange, pink, red, white, yellow)\n",
        "veil-type | (partial, universal)\n",
        "veil-color | (brown, orange, white, yellow)\n",
        "ring-number | (none, one, two)\n",
        "ring-type | (cobwebby, evanescent, flaring, large, none, pendant, sheathing, zone)\n",
        "spore-print-color | (black, brown, buff, chocolate, green, orange, purple, white, yellow)\n",
        "population | (abundant, clustered, numerous, scattered ,several, solitary)\n",
        "habitat | (grasses, leaves, meadows, paths, urban, waste, woods)\n",
        "classes   |  (edible, poisonous)"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "id": "2-v_vM09tXni"
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      "source": [
        "#@title Librerías y Cargado de Datos\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.naive_bayes import CategoricalNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score,GridSearchCV,RandomizedSearchCV,cross_validate\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.metrics import make_scorer, precision_score, recall_score, f1_score\n",
        "import seaborn as sns\n",
        "from scipy.stats import randint\n",
        "\n",
        "\n",
        "# Cargar los datos\n",
        "mushroom_data = pd.read_csv('Yellow_Submarine.csv')"
      ]
    },
    {
      "cell_type": "code",
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        "#Genera estadísticas descriptivas de las columnas numéricas del DataFrame.\n",
        "#Muestra información como la media, desviación estándar, valores mínimo y máximo, y percentiles.\n",
        "#Ayuda a entender la distribución y rango de los datos numéricos.\n",
        "mushroom_data.describe()\n"
      ],
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              "         class cap-shape cap-surface cap-color bruises  odor gill-attachment  \\\n",
              "count     8124      8124        8124      8124    8124  8124            8124   \n",
              "unique       2         6           4        10       2     9               2   \n",
              "top     edible    convex       scaly     brown      no  none            free   \n",
              "freq      4208      3656        3244      2284    4748  3528            7914   \n",
              "\n",
              "       gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
              "count          8124      8124       8124  ...                     8124   \n",
              "unique            2         2         12  ...                        4   \n",
              "top           close     broad       buff  ...                   smooth   \n",
              "freq           6812      5612       1728  ...                     4936   \n",
              "\n",
              "       stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
              "count                    8124                   8124      8124       8124   \n",
              "unique                      9                      9         1          4   \n",
              "top                     white                  white   partial      white   \n",
              "freq                     4464                   4384      8124       7924   \n",
              "\n",
              "       ring-number ring-type spore-print-color population habitat  \n",
              "count         8124      8124              8124       8124    8124  \n",
              "unique           3         5                 9          6       7  \n",
              "top            one   pendant             white    several   woods  \n",
              "freq          7488      3968              2388       4040    3148  \n",
              "\n",
              "[4 rows x 23 columns]"
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              "      <td>4208</td>\n",
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      "cell_type": "code",
      "source": [
        "#Proporciona un resumen conciso del DataFrame.\n",
        "#Muestra el número de filas y columnas, los nombres de las columnas, los tipos de datos de cada columna y la cantidad de valores no nulos.\n",
        "#Útil para identificar rápidamente columnas con valores faltantes y verificar los tipos de datos.\n",
        "mushroom_data.info()"
      ],
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      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8124 entries, 0 to 8123\n",
            "Data columns (total 23 columns):\n",
            " #   Column                    Non-Null Count  Dtype \n",
            "---  ------                    --------------  ----- \n",
            " 0   class                     8124 non-null   object\n",
            " 1   cap-shape                 8124 non-null   object\n",
            " 2   cap-surface               8124 non-null   object\n",
            " 3   cap-color                 8124 non-null   object\n",
            " 4   bruises                   8124 non-null   object\n",
            " 5   odor                      8124 non-null   object\n",
            " 6   gill-attachment           8124 non-null   object\n",
            " 7   gill-spacing              8124 non-null   object\n",
            " 8   gill-size                 8124 non-null   object\n",
            " 9   gill-color                8124 non-null   object\n",
            " 10  stalk-shape               8124 non-null   object\n",
            " 11  stalk-root                8124 non-null   object\n",
            " 12  stalk-surface-above-ring  8124 non-null   object\n",
            " 13  stalk-surface-below-ring  8124 non-null   object\n",
            " 14  stalk-color-above-ring    8124 non-null   object\n",
            " 15  stalk-color-below-ring    8124 non-null   object\n",
            " 16  veil-type                 8124 non-null   object\n",
            " 17  veil-color                8124 non-null   object\n",
            " 18  ring-number               8124 non-null   object\n",
            " 19  ring-type                 8124 non-null   object\n",
            " 20  spore-print-color         8124 non-null   object\n",
            " 21  population                8124 non-null   object\n",
            " 22  habitat                   8124 non-null   object\n",
            "dtypes: object(23)\n",
            "memory usage: 1.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Muestra las primeras 5 filas del DataFrame.\n",
        "#Permite visualizar rápidamente la estructura de los datos, los nombres de las columnas y algunos valores de ejemplo.\n",
        "#Sirve para tener una primera impresión del contenido del DataFrame.\n",
        "print(mushroom_data.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLUpbv-cZHeM",
        "outputId": "978da93e-33ef-44dd-b23d-5b0ba48091a9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     class cap-shape cap-surface cap-color  bruises     odor gill-attachment  \\\n",
            "0  poisson    convex      smooth     brown  bruises  pungent            free   \n",
            "1   edible    convex      smooth    yellow  bruises   almond            free   \n",
            "2   edible      bell      smooth     white  bruises    anise            free   \n",
            "3  poisson    convex       scaly     white  bruises  pungent            free   \n",
            "4   edible    convex      smooth      gray       no     none            free   \n",
            "\n",
            "  gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
            "0        close    narrow      black  ...                   smooth   \n",
            "1        close     broad      black  ...                   smooth   \n",
            "2        close     broad      brown  ...                   smooth   \n",
            "3        close    narrow      brown  ...                   smooth   \n",
            "4      crowded     broad      black  ...                   smooth   \n",
            "\n",
            "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
            "0                  white                  white   partial      white   \n",
            "1                  white                  white   partial      white   \n",
            "2                  white                  white   partial      white   \n",
            "3                  white                  white   partial      white   \n",
            "4                  white                  white   partial      white   \n",
            "\n",
            "  ring-number   ring-type spore-print-color population  habitat  \n",
            "0         one     pendant             black  scattered    urban  \n",
            "1         one     pendant             brown   numerous  grasses  \n",
            "2         one     pendant             brown   numerous  meadows  \n",
            "3         one     pendant             black  scattered    urban  \n",
            "4         one  evanescent             brown   abundant  grasses  \n",
            "\n",
            "[5 rows x 23 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Verificar valores faltantes\n",
        "missing_values = mushroom_data.isnull().sum()\n",
        "print(\"Valores faltantes en cada columna:\")\n",
        "print(missing_values)"
      ],
      "metadata": {
        "id": "ie-imvQ6vISD",
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2dd1c39c-9430-4a48-9ffd-5a08f40cc875"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Valores faltantes en cada columna:\n",
            "class                       0\n",
            "cap-shape                   0\n",
            "cap-surface                 0\n",
            "cap-color                   0\n",
            "bruises                     0\n",
            "odor                        0\n",
            "gill-attachment             0\n",
            "gill-spacing                0\n",
            "gill-size                   0\n",
            "gill-color                  0\n",
            "stalk-shape                 0\n",
            "stalk-root                  0\n",
            "stalk-surface-above-ring    0\n",
            "stalk-surface-below-ring    0\n",
            "stalk-color-above-ring      0\n",
            "stalk-color-below-ring      0\n",
            "veil-type                   0\n",
            "veil-color                  0\n",
            "ring-number                 0\n",
            "ring-type                   0\n",
            "spore-print-color           0\n",
            "population                  0\n",
            "habitat                     0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Convertir todas las categorías a minúsculas\n",
        "for col in mushroom_data.select_dtypes(include=['object']).columns:\n",
        "    mushroom_data[col] = mushroom_data[col].str.lower()"
      ],
      "metadata": {
        "id": "sFUqs48VyejP"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Remover la columna 'veil-type' y 'stalk-shape'\n",
        "mushroom_data.drop(columns=['veil-type','stalk-shape'], inplace=True)"
      ],
      "metadata": {
        "collapsed": true,
        "id": "jY60jE7TkHl3"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Chequear la distribución de clases\n",
        "class_distribution = mushroom_data['class'].value_counts()\n",
        "print(class_distribution)"
      ],
      "metadata": {
        "collapsed": true,
        "id": "FBiwxOuyiGX5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7270e353-f8e4-4d5f-9b3f-a10d34e78933"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "class\n",
            "edible     4208\n",
            "poisson    3916\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Cambiar \"poisson\" por \"poisonous\"\n",
        "# Original\n",
        "print(mushroom_data['class'].value_counts())\n",
        "\n",
        "# Cambiar el nombre 'poisson' a 'poisonous' en la columna 'class'\n",
        "mushroom_data['class'] = mushroom_data['class'].replace({'poisson': 'poisonous'})\n",
        "\n",
        "# Verificar que el cambio se haya realizado\n",
        "print(mushroom_data['class'].value_counts())"
      ],
      "metadata": {
        "collapsed": true,
        "id": "jBX_uVWqo0-Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ad2d273-7f50-4043-f44e-456d0ece8072"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "class\n",
            "edible     4208\n",
            "poisson    3916\n",
            "Name: count, dtype: int64\n",
            "class\n",
            "edible       4208\n",
            "poisonous    3916\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Verificación de cambios\n",
        "# Lista de columnas a verificar\n",
        "columns_to_check = [\n",
        "    'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',\n",
        "    'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',\n",
        "    'stalk-root', 'stalk-surface-above-ring',\n",
        "    'stalk-surface-below-ring', 'stalk-color-above-ring',\n",
        "    'stalk-color-below-ring', 'veil-color', 'ring-number',\n",
        "    'ring-type', 'spore-print-color', 'population',\n",
        "    'habitat'\n",
        "]\n",
        "\n",
        "# Verificar la distribución de \"edible\" y \"poisonous\" para cada categoría en cada columna\n",
        "for column in columns_to_check:\n",
        "    print(f\"Distribución de 'class' para cada valor en '{column}':\")\n",
        "    distribution = mushroom_data.groupby([column, 'class']).size().unstack(fill_value=0)\n",
        "    print(distribution)\n",
        "    print(\"\\n\")"
      ],
      "metadata": {
        "collapsed": true,
        "id": "gx4PFVnLqOSE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c80c25d-d473-496a-d318-3947cb44144c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distribución de 'class' para cada valor en 'cap-shape':\n",
            "class      edible  poisonous\n",
            "cap-shape                   \n",
            "bell          404         48\n",
            "conical         0          4\n",
            "convex       1948       1708\n",
            "flat         1596       1556\n",
            "knobbed       228        600\n",
            "sunken         32          0\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'cap-surface':\n",
            "class        edible  poisonous\n",
            "cap-surface                   \n",
            "fibrous        1560        760\n",
            "grooves           0          4\n",
            "scaly          1504       1740\n",
            "smooth         1144       1412\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'cap-color':\n",
            "class      edible  poisonous\n",
            "cap-color                   \n",
            "brown        1264       1020\n",
            "buff           48        120\n",
            "cinnamon       32         12\n",
            "gray         1032        808\n",
            "green          16          0\n",
            "pink           56         88\n",
            "purple         16          0\n",
            "red           624        876\n",
            "white         720        320\n",
            "yellow        400        672\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'bruises':\n",
            "class    edible  poisonous\n",
            "bruises                   \n",
            "bruises    2752        624\n",
            "no         1456       3292\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'odor':\n",
            "class     edible  poisonous\n",
            "odor                       \n",
            "almond       400          0\n",
            "anise        400          0\n",
            "creosote       0        192\n",
            "fishy          0        576\n",
            "foul           0       2160\n",
            "musty          0         36\n",
            "none        3408        120\n",
            "pungent        0        256\n",
            "spicy          0        576\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'gill-attachment':\n",
            "class            edible  poisonous\n",
            "gill-attachment                   \n",
            "attached            192         18\n",
            "free               4016       3898\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'gill-spacing':\n",
            "class         edible  poisonous\n",
            "gill-spacing                   \n",
            "close           3008       3804\n",
            "crowded         1200        112\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'gill-size':\n",
            "class      edible  poisonous\n",
            "gill-size                   \n",
            "broad        3920       1692\n",
            "narrow        288       2224\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'gill-color':\n",
            "class       edible  poisonous\n",
            "gill-color                   \n",
            "black          344         64\n",
            "brown          936        112\n",
            "buff             0       1728\n",
            "chocolate      204        528\n",
            "gray           248        504\n",
            "green            0         24\n",
            "orange          64          0\n",
            "pink           852        640\n",
            "purple         444         48\n",
            "red             96          0\n",
            "white          956        246\n",
            "yellow          64         22\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'stalk-root':\n",
            "class       edible  poisonous\n",
            "stalk-root                   \n",
            "bulbous       1920       1856\n",
            "club           512         44\n",
            "equal          864        256\n",
            "missing        720       1760\n",
            "rooted         192          0\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'stalk-surface-above-ring':\n",
            "class                     edible  poisonous\n",
            "stalk-surface-above-ring                   \n",
            "fibrous                      408        144\n",
            "scaly                         16          8\n",
            "silky                        144       2228\n",
            "smooth                      3640       1536\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'stalk-surface-below-ring':\n",
            "class                     edible  poisonous\n",
            "stalk-surface-below-ring                   \n",
            "fibrous                      456        144\n",
            "scaly                        208         76\n",
            "silky                        144       2160\n",
            "smooth                      3400       1536\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'stalk-color-above-ring':\n",
            "class                   edible  poisonous\n",
            "stalk-color-above-ring                   \n",
            "brown                       16        432\n",
            "buff                         0        432\n",
            "cinnamon                     0         36\n",
            "gray                       576          0\n",
            "orange                     192          0\n",
            "pink                       576       1296\n",
            "red                         96          0\n",
            "white                     2752       1712\n",
            "yellow                       0          8\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'stalk-color-below-ring':\n",
            "class                   edible  poisonous\n",
            "stalk-color-below-ring                   \n",
            "brown                       64        448\n",
            "buff                         0        432\n",
            "cinnamon                     0         36\n",
            "gray                       576          0\n",
            "orange                     192          0\n",
            "pink                       576       1296\n",
            "red                         96          0\n",
            "white                     2704       1680\n",
            "yellow                       0         24\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'veil-color':\n",
            "class       edible  poisonous\n",
            "veil-color                   \n",
            "brown           96          0\n",
            "orange          96          0\n",
            "white         4016       3908\n",
            "yellow           0          8\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'ring-number':\n",
            "class        edible  poisonous\n",
            "ring-number                   \n",
            "none              0         36\n",
            "one            3680       3808\n",
            "two             528         72\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'ring-type':\n",
            "class       edible  poisonous\n",
            "ring-type                    \n",
            "evanescent    1008       1768\n",
            "flaring         48          0\n",
            "large            0       1296\n",
            "none             0         36\n",
            "pendant       3152        816\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'spore-print-color':\n",
            "class              edible  poisonous\n",
            "spore-print-color                   \n",
            "black                1648        224\n",
            "brown                1744        224\n",
            "buff                   48          0\n",
            "chocolate              48       1584\n",
            "green                   0         72\n",
            "orange                 48          0\n",
            "purple                 48          0\n",
            "white                 576       1812\n",
            "yellow                 48          0\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'population':\n",
            "class       edible  poisonous\n",
            "population                   \n",
            "abundant       384          0\n",
            "clustered      288         52\n",
            "numerous       400          0\n",
            "scattered      880        368\n",
            "several       1192       2848\n",
            "solitary      1064        648\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'habitat':\n",
            "class    edible  poisonous\n",
            "habitat                   \n",
            "grasses    1408        740\n",
            "leaves      240        592\n",
            "meadows     256         36\n",
            "paths       136       1008\n",
            "urban        96        272\n",
            "waste       192          0\n",
            "woods      1880       1268\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Datos que no aportan valor\n",
        "cap-shape->\n",
        "    convex;flat\n",
        "\n",
        "cap-surface->\n",
        "    scaly;smooth\n",
        "\n",
        "cap-color->\n",
        "    brown;red;yellow\n",
        "\n",
        "gill-attachment->\n",
        "    free\n",
        "\n",
        "gill-color->\n",
        "    chocolate;gray;pink\n",
        "\n",
        "gill-attachment->\n",
        "    close\n",
        "\n",
        "stalk-root->\n",
        "    bulbous\n",
        "\n",
        "stalk-color-below-ring->\n",
        "    white\n",
        "\n",
        "veil-color->\n",
        "    white\n",
        "\n",
        "ring-number->\n",
        "    one\n",
        "\n",
        "ring-type->\n",
        "    evanescent\n",
        "\n",
        "habitat->\n",
        "    woods\n",
        "\n"
      ],
      "metadata": {
        "id": "zzSLDg-R0kge"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Reemplazar los valores especificados\n",
        "values_to_replace = {\n",
        "    'cap-shape': ['convex', 'flat'],\n",
        "    'cap-surface': ['scaly', 'smooth'],\n",
        "    'cap-color': ['brown', 'red', 'yellow'],\n",
        "    'gill-attachment': ['free'],\n",
        "    'gill-color': ['chocolate', 'gray', 'pink'],\n",
        "    'gill-spacing': ['close'],\n",
        "    'stalk-root': ['bulbous'],\n",
        "    'stalk-color-below-ring': ['white'],\n",
        "    'veil-color': ['white'],\n",
        "    'ring-number': ['one'],\n",
        "    'ring-type': ['evanescent'],\n",
        "    'habitat': ['woods']\n",
        "}\n",
        "#Nota: son reemplazados por NaN\n",
        "for column, values in values_to_replace.items():\n",
        "    mushroom_data[column] = mushroom_data[column].replace(values, np.nan)\n",
        "\n",
        "# Verificar el resultado\n",
        "print(mushroom_data.head())\n",
        "\n",
        "# Verificar el número de valores nulos\n",
        "missing_values = mushroom_data.isnull().sum()\n",
        "print(\"Valores faltantes en cada columna después de la sustitución:\")\n",
        "print(missing_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "NHRCdUs60hj5",
        "outputId": "48a3a4e7-a62e-4b7b-d03c-20e43d67cf2f"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       class cap-shape cap-surface cap-color  bruises     odor  \\\n",
            "0  poisonous       NaN         NaN       NaN  bruises  pungent   \n",
            "1     edible       NaN         NaN       NaN  bruises   almond   \n",
            "2     edible      bell         NaN     white  bruises    anise   \n",
            "3  poisonous       NaN         NaN     white  bruises  pungent   \n",
            "4     edible       NaN         NaN      gray       no     none   \n",
            "\n",
            "  gill-attachment gill-spacing gill-size gill-color  ...  \\\n",
            "0             NaN          NaN    narrow      black  ...   \n",
            "1             NaN          NaN     broad      black  ...   \n",
            "2             NaN          NaN     broad      brown  ...   \n",
            "3             NaN          NaN    narrow      brown  ...   \n",
            "4             NaN      crowded     broad      black  ...   \n",
            "\n",
            "  stalk-surface-above-ring stalk-surface-below-ring stalk-color-above-ring  \\\n",
            "0                   smooth                   smooth                  white   \n",
            "1                   smooth                   smooth                  white   \n",
            "2                   smooth                   smooth                  white   \n",
            "3                   smooth                   smooth                  white   \n",
            "4                   smooth                   smooth                  white   \n",
            "\n",
            "  stalk-color-below-ring veil-color ring-number ring-type spore-print-color  \\\n",
            "0                    NaN        NaN         NaN   pendant             black   \n",
            "1                    NaN        NaN         NaN   pendant             brown   \n",
            "2                    NaN        NaN         NaN   pendant             brown   \n",
            "3                    NaN        NaN         NaN   pendant             black   \n",
            "4                    NaN        NaN         NaN       NaN             brown   \n",
            "\n",
            "  population  habitat  \n",
            "0  scattered    urban  \n",
            "1   numerous  grasses  \n",
            "2   numerous  meadows  \n",
            "3  scattered    urban  \n",
            "4   abundant  grasses  \n",
            "\n",
            "[5 rows x 21 columns]\n",
            "Valores faltantes en cada columna después de la sustitución:\n",
            "class                          0\n",
            "cap-shape                   6808\n",
            "cap-surface                 5800\n",
            "cap-color                   4856\n",
            "bruises                        0\n",
            "odor                           0\n",
            "gill-attachment             7914\n",
            "gill-spacing                6812\n",
            "gill-size                      0\n",
            "gill-color                  2976\n",
            "stalk-root                  3776\n",
            "stalk-surface-above-ring       0\n",
            "stalk-surface-below-ring       0\n",
            "stalk-color-above-ring         0\n",
            "stalk-color-below-ring      4384\n",
            "veil-color                  7924\n",
            "ring-number                 7488\n",
            "ring-type                   2776\n",
            "spore-print-color              0\n",
            "population                     0\n",
            "habitat                     3148\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Gráfico de Barras\n",
        "# Datos proporcionados\n",
        "data_ambiguas = {\n",
        "    'Feature': ['cap-shape', 'cap-shape', 'cap-surface', 'cap-surface', 'cap-color', 'cap-color', 'cap-color', 'gill-attachment', 'gill-color', 'gill-color', 'gill-color', 'gill-spacing', 'stalk-root', 'stalk-color-below-ring', 'veil-color', 'ring-number', 'ring-type', 'habitat'],\n",
        "    'Value': ['convex', 'flat', 'scaly', 'smooth', 'brown', 'red', 'yellow', 'free', 'chocolate', 'gray', 'pink', 'close', 'bulbous', 'white', 'white', 'one', 'evanescent', 'woods'],\n",
        "    'Edible': [1948, 1596, 1504, 1144, 1264, 624, 400, 4016, 204, 248, 852, 3008, 1920, 2704, 4016, 3680, 1008, 1880],\n",
        "    'Poisonous': [1708, 1556, 1740, 1412, 1020, 876, 672, 3898, 528, 504, 640, 3804, 1856, 1680, 3908, 3808, 1768, 1268]\n",
        "}\n",
        "\n",
        "df_ambiguas = pd.DataFrame(data_ambiguas)\n",
        "\n",
        "# Ordenar los datos\n",
        "df_ambiguas['Total'] = df_ambiguas['Edible'] + df_ambiguas['Poisonous']\n",
        "df_ambiguas = df_ambiguas.sort_values(by='Total', ascending=False)\n",
        "\n",
        "# Graficar los datos\n",
        "fig, ax = plt.subplots(figsize=(16, 10))\n",
        "bar_width = 0.35\n",
        "index = np.arange(len(df_ambiguas))\n",
        "\n",
        "bar1 = ax.bar(index, df_ambiguas['Edible'], bar_width, label='Comestible')\n",
        "bar2 = ax.bar(index + bar_width, df_ambiguas['Poisonous'], bar_width, label='Venenoso')\n",
        "\n",
        "ax.set_xlabel('Características')\n",
        "ax.set_ylabel('Muestras')\n",
        "ax.set_title('Distribución de Hongos Comestibles y Venenosos según su Característica')\n",
        "ax.set_xticks(index + bar_width / 2)\n",
        "ax.set_xticklabels(df_ambiguas['Feature'] + \" - \" + df_ambiguas['Value'], rotation=90)\n",
        "ax.legend()\n",
        "\n",
        "# Agregar porcentajes sobre las barras\n",
        "for i in range(len(df_ambiguas)):\n",
        "    total = df_ambiguas['Total'].iloc[i]\n",
        "    edible = df_ambiguas['Edible'].iloc[i]\n",
        "    poisonous = df_ambiguas['Poisonous'].iloc[i]\n",
        "    edible_pct = (edible / total) * 100\n",
        "    poisonous_pct = (poisonous / total) * 100\n",
        "\n",
        "    ax.text(index[i] - bar_width / 2 + 0.2, edible + 50, f'{edible_pct:.0f}%', ha='center', va='bottom', fontsize=10, color='blue')\n",
        "    ax.text(index[i] + bar_width / 2 + 0.2, poisonous + 50, f'{poisonous_pct:.0f}%', ha='center', va='bottom', fontsize=10, color='red')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "DXLPd2aK38d-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 998
        },
        "outputId": "2759bd11-c522-4c2e-add6-b8301e1f47d9"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Chequeo de resutlados\n",
        "# Lista de columnas a verificar\n",
        "columns_to_check = [\n",
        "    'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',\n",
        "    'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',\n",
        "    'stalk-root', 'stalk-surface-above-ring',\n",
        "    'stalk-surface-below-ring', 'stalk-color-above-ring',\n",
        "    'stalk-color-below-ring', 'veil-color', 'ring-number',\n",
        "    'ring-type', 'spore-print-color', 'population',\n",
        "    'habitat'\n",
        "]\n",
        "\n",
        "# Verificar la distribución de \"edible\" y \"poisonous\" para cada categoría en cada columna\n",
        "for column in columns_to_check:\n",
        "    print(f\"Distribución de 'class' para cada valor en '{column}':\")\n",
        "    distribution = mushroom_data.groupby([column, 'class']).size().unstack(fill_value=0)\n",
        "    print(distribution)\n",
        "    print(\"\\n\")\n",
        "\n",
        "# Cheqeuar la distribución de clases\n",
        "class_distribution = mushroom_data['class'].value_counts()\n",
        "print(class_distribution)"
      ],
      "metadata": {
        "collapsed": true,
        "id": "OyftsVmv2YiA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ea47306-1472-4255-8405-20d44c37d660"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distribución de 'class' para cada valor en 'cap-shape':\n",
            "class      edible  poisonous\n",
            "cap-shape                   \n",
            "bell          404         48\n",
            "conical         0          4\n",
            "knobbed       228        600\n",
            "sunken         32          0\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'cap-surface':\n",
            "class        edible  poisonous\n",
            "cap-surface                   \n",
            "fibrous        1560        760\n",
            "grooves           0          4\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'cap-color':\n",
            "class      edible  poisonous\n",
            "cap-color                   \n",
            "buff           48        120\n",
            "cinnamon       32         12\n",
            "gray         1032        808\n",
            "green          16          0\n",
            "pink           56         88\n",
            "purple         16          0\n",
            "white         720        320\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'bruises':\n",
            "class    edible  poisonous\n",
            "bruises                   \n",
            "bruises    2752        624\n",
            "no         1456       3292\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'odor':\n",
            "class     edible  poisonous\n",
            "odor                       \n",
            "almond       400          0\n",
            "anise        400          0\n",
            "creosote       0        192\n",
            "fishy          0        576\n",
            "foul           0       2160\n",
            "musty          0         36\n",
            "none        3408        120\n",
            "pungent        0        256\n",
            "spicy          0        576\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'gill-attachment':\n",
            "class            edible  poisonous\n",
            "gill-attachment                   \n",
            "attached            192         18\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'gill-spacing':\n",
            "class         edible  poisonous\n",
            "gill-spacing                   \n",
            "crowded         1200        112\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'gill-size':\n",
            "class      edible  poisonous\n",
            "gill-size                   \n",
            "broad        3920       1692\n",
            "narrow        288       2224\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'gill-color':\n",
            "class       edible  poisonous\n",
            "gill-color                   \n",
            "black          344         64\n",
            "brown          936        112\n",
            "buff             0       1728\n",
            "green            0         24\n",
            "orange          64          0\n",
            "purple         444         48\n",
            "red             96          0\n",
            "white          956        246\n",
            "yellow          64         22\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'stalk-root':\n",
            "class       edible  poisonous\n",
            "stalk-root                   \n",
            "club           512         44\n",
            "equal          864        256\n",
            "missing        720       1760\n",
            "rooted         192          0\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'stalk-surface-above-ring':\n",
            "class                     edible  poisonous\n",
            "stalk-surface-above-ring                   \n",
            "fibrous                      408        144\n",
            "scaly                         16          8\n",
            "silky                        144       2228\n",
            "smooth                      3640       1536\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'stalk-surface-below-ring':\n",
            "class                     edible  poisonous\n",
            "stalk-surface-below-ring                   \n",
            "fibrous                      456        144\n",
            "scaly                        208         76\n",
            "silky                        144       2160\n",
            "smooth                      3400       1536\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'stalk-color-above-ring':\n",
            "class                   edible  poisonous\n",
            "stalk-color-above-ring                   \n",
            "brown                       16        432\n",
            "buff                         0        432\n",
            "cinnamon                     0         36\n",
            "gray                       576          0\n",
            "orange                     192          0\n",
            "pink                       576       1296\n",
            "red                         96          0\n",
            "white                     2752       1712\n",
            "yellow                       0          8\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'stalk-color-below-ring':\n",
            "class                   edible  poisonous\n",
            "stalk-color-below-ring                   \n",
            "brown                       64        448\n",
            "buff                         0        432\n",
            "cinnamon                     0         36\n",
            "gray                       576          0\n",
            "orange                     192          0\n",
            "pink                       576       1296\n",
            "red                         96          0\n",
            "yellow                       0         24\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'veil-color':\n",
            "class       edible  poisonous\n",
            "veil-color                   \n",
            "brown           96          0\n",
            "orange          96          0\n",
            "yellow           0          8\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'ring-number':\n",
            "class        edible  poisonous\n",
            "ring-number                   \n",
            "none              0         36\n",
            "two             528         72\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'ring-type':\n",
            "class      edible  poisonous\n",
            "ring-type                   \n",
            "flaring        48          0\n",
            "large           0       1296\n",
            "none            0         36\n",
            "pendant      3152        816\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'spore-print-color':\n",
            "class              edible  poisonous\n",
            "spore-print-color                   \n",
            "black                1648        224\n",
            "brown                1744        224\n",
            "buff                   48          0\n",
            "chocolate              48       1584\n",
            "green                   0         72\n",
            "orange                 48          0\n",
            "purple                 48          0\n",
            "white                 576       1812\n",
            "yellow                 48          0\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'population':\n",
            "class       edible  poisonous\n",
            "population                   \n",
            "abundant       384          0\n",
            "clustered      288         52\n",
            "numerous       400          0\n",
            "scattered      880        368\n",
            "several       1192       2848\n",
            "solitary      1064        648\n",
            "\n",
            "\n",
            "Distribución de 'class' para cada valor en 'habitat':\n",
            "class    edible  poisonous\n",
            "habitat                   \n",
            "grasses    1408        740\n",
            "leaves      240        592\n",
            "meadows     256         36\n",
            "paths       136       1008\n",
            "urban        96        272\n",
            "waste       192          0\n",
            "\n",
            "\n",
            "class\n",
            "edible       4208\n",
            "poisonous    3916\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Aplicar one-hot encoding a las características categóricas\n",
        "mushroom_data_encoded = pd.get_dummies(mushroom_data, drop_first=True)\n",
        "\n",
        "# Verificar el resultado\n",
        "print(mushroom_data_encoded.head())\n",
        "\n",
        "# Mostrar la cantidad de columnas después de One-Hot Encoding\n",
        "print(\"Cantidad de columnas después de One-Hot Encoding:\", mushroom_data_encoded.shape[1])\n"
      ],
      "metadata": {
        "id": "Neq4DazC7y5A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "outputId": "f434cb5f-2bdc-44a4-d09d-b1195ab703d3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   class_poisonous  cap-shape_conical  cap-shape_convex  cap-shape_flat  \\\n",
            "0             True              False              True           False   \n",
            "1            False              False              True           False   \n",
            "2            False              False             False           False   \n",
            "3             True              False              True           False   \n",
            "4            False              False              True           False   \n",
            "\n",
            "   cap-shape_knobbed  cap-shape_sunken  cap-surface_grooves  \\\n",
            "0              False             False                False   \n",
            "1              False             False                False   \n",
            "2              False             False                False   \n",
            "3              False             False                False   \n",
            "4              False             False                False   \n",
            "\n",
            "   cap-surface_scaly  cap-surface_smooth  cap-color_buff  ...  \\\n",
            "0              False                True           False  ...   \n",
            "1              False                True           False  ...   \n",
            "2              False                True           False  ...   \n",
            "3               True               False           False  ...   \n",
            "4              False                True           False  ...   \n",
            "\n",
            "   population_numerous  population_scattered  population_several  \\\n",
            "0                False                  True               False   \n",
            "1                 True                 False               False   \n",
            "2                 True                 False               False   \n",
            "3                False                  True               False   \n",
            "4                False                 False               False   \n",
            "\n",
            "   population_solitary  habitat_leaves  habitat_meadows  habitat_paths  \\\n",
            "0                False           False            False          False   \n",
            "1                False           False            False          False   \n",
            "2                False           False             True          False   \n",
            "3                False           False            False          False   \n",
            "4                False           False            False          False   \n",
            "\n",
            "   habitat_urban  habitat_waste  habitat_woods  \n",
            "0           True          False          False  \n",
            "1          False          False          False  \n",
            "2          False          False          False  \n",
            "3           True          False          False  \n",
            "4          False          False          False  \n",
            "\n",
            "[5 rows x 95 columns]\n",
            "Cantidad de columnas después de One-Hot Encoding: 95\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display\n",
        "\n",
        "#@title Revisar la correlación entre las características y la variable objetivo\n",
        "corr_matrix = mushroom_data_encoded.corr()\n",
        "\n",
        "# Verificar la correlación entre las características y la variable objetivo\n",
        "print(corr_matrix['class_poisonous'].sort_values(ascending=False))\n",
        "\n",
        "print(corr_matrix)\n",
        "\n",
        "display(corr_matrix)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "zNj_XhDC8HM_",
        "outputId": "2dab7137-ea19-42a2-f355-390ab046d7c9"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "class_poisonous                    1.000000\n",
            "odor_foul                          0.623842\n",
            "stalk-surface-above-ring_silky     0.587658\n",
            "stalk-surface-below-ring_silky     0.573524\n",
            "gill-size_narrow                   0.540024\n",
            "                                     ...   \n",
            "spore-print-color_brown           -0.416645\n",
            "stalk-surface-below-ring_smooth   -0.425444\n",
            "stalk-surface-above-ring_smooth   -0.491314\n",
            "ring-type_pendant                 -0.540469\n",
            "odor_none                         -0.785557\n",
            "Name: class_poisonous, Length: 95, dtype: float64\n",
            "                   class_poisonous  cap-shape_conical  cap-shape_convex  \\\n",
            "class_poisonous           1.000000           0.023007         -0.026886   \n",
            "cap-shape_conical         0.023007           1.000000         -0.020077   \n",
            "cap-shape_convex         -0.026886          -0.020077          1.000000   \n",
            "cap-shape_flat            0.018526          -0.017672         -0.720235   \n",
            "cap-shape_knobbed         0.163565          -0.007477         -0.304733   \n",
            "...                            ...                ...               ...   \n",
            "habitat_meadows          -0.138627          -0.004286         -0.004529   \n",
            "habitat_paths             0.323346          -0.008985         -0.029042   \n",
            "habitat_urban             0.112078          -0.004835          0.002845   \n",
            "habitat_waste            -0.150087          -0.003453         -0.036493   \n",
            "habitat_woods            -0.126123          -0.017653          0.071773   \n",
            "\n",
            "                   cap-shape_flat  cap-shape_knobbed  cap-shape_sunken  \\\n",
            "class_poisonous          0.018526           0.163565         -0.060664   \n",
            "cap-shape_conical       -0.017672          -0.007477         -0.001396   \n",
            "cap-shape_convex        -0.720235          -0.304733         -0.056884   \n",
            "cap-shape_flat           1.000000          -0.268226         -0.050070   \n",
            "cap-shape_knobbed       -0.268226           1.000000         -0.021185   \n",
            "...                           ...                ...               ...   \n",
            "habitat_meadows         -0.129312          -0.065047         -0.012142   \n",
            "habitat_paths            0.021891           0.090538         -0.025458   \n",
            "habitat_urban            0.030636          -0.073380          0.288697   \n",
            "habitat_waste           -0.017450           0.119003         -0.009784   \n",
            "habitat_woods            0.074986          -0.089230         -0.050018   \n",
            "\n",
            "                   cap-surface_grooves  cap-surface_scaly  cap-surface_smooth  \\\n",
            "class_poisonous               0.023007           0.088677            0.095454   \n",
            "cap-shape_conical             0.249631           0.015893           -0.015038   \n",
            "cap-shape_convex             -0.020077          -0.019137           -0.040634   \n",
            "cap-shape_flat               -0.006285           0.029076           -0.093397   \n",
            "cap-shape_knobbed             0.010863           0.015261            0.137988   \n",
            "...                                ...                ...                 ...   \n",
            "habitat_meadows              -0.004286           0.039697            0.077081   \n",
            "habitat_paths                -0.008985           0.117915           -0.039574   \n",
            "habitat_urban                -0.004835          -0.100248            0.117548   \n",
            "habitat_waste                -0.003453           0.031986            0.062109   \n",
            "habitat_woods                -0.017653           0.110902           -0.301670   \n",
            "\n",
            "                   cap-color_buff  ...  population_numerous  \\\n",
            "class_poisonous          0.067544  ...            -0.219529   \n",
            "cap-shape_conical       -0.003225  ...            -0.005051   \n",
            "cap-shape_convex        -0.020175  ...            -0.004586   \n",
            "cap-shape_flat           0.019203  ...            -0.181191   \n",
            "cap-shape_knobbed       -0.003209  ...             0.013599   \n",
            "...                           ...  ...                  ...   \n",
            "habitat_meadows          0.027701  ...             0.347262   \n",
            "habitat_paths           -0.058829  ...            -0.092129   \n",
            "habitat_urban            0.167996  ...            -0.049569   \n",
            "habitat_waste            0.250709  ...            -0.035405   \n",
            "habitat_woods           -0.115580  ...            -0.181003   \n",
            "\n",
            "                   population_scattered  population_several  \\\n",
            "class_poisonous               -0.159572            0.443722   \n",
            "cap-shape_conical             -0.009456           -0.022075   \n",
            "cap-shape_convex               0.059264           -0.060421   \n",
            "cap-shape_flat                -0.075804            0.034625   \n",
            "cap-shape_knobbed             -0.089360            0.172707   \n",
            "...                                 ...                 ...   \n",
            "habitat_meadows                0.152473           -0.144434   \n",
            "habitat_paths                 -0.125366            0.169228   \n",
            "habitat_urban                  0.130450            0.001180   \n",
            "habitat_waste                 -0.066282           -0.154742   \n",
            "habitat_woods                 -0.271589            0.171068   \n",
            "\n",
            "                   population_solitary  habitat_leaves  habitat_meadows  \\\n",
            "class_poisonous              -0.107055        0.155150        -0.138627   \n",
            "cap-shape_conical            -0.011469        0.065707        -0.004286   \n",
            "cap-shape_convex              0.044626       -0.096639        -0.004529   \n",
            "cap-shape_flat                0.111346       -0.052326        -0.129312   \n",
            "cap-shape_knobbed            -0.170081        0.235108        -0.065047   \n",
            "...                                ...             ...              ...   \n",
            "habitat_meadows              -0.099772       -0.065222         1.000000   \n",
            "habitat_paths                 0.040714       -0.136749        -0.078170   \n",
            "habitat_urban                -0.042888       -0.073577        -0.042059   \n",
            "habitat_waste                -0.080392       -0.052553        -0.030041   \n",
            "habitat_woods                 0.277929       -0.268668        -0.153579   \n",
            "\n",
            "                   habitat_paths  habitat_urban  habitat_waste  habitat_woods  \n",
            "class_poisonous         0.323346       0.112078      -0.150087      -0.126123  \n",
            "cap-shape_conical      -0.008985      -0.004835      -0.003453      -0.017653  \n",
            "cap-shape_convex       -0.029042       0.002845      -0.036493       0.071773  \n",
            "cap-shape_flat          0.021891       0.030636      -0.017450       0.074986  \n",
            "cap-shape_knobbed       0.090538      -0.073380       0.119003      -0.089230  \n",
            "...                          ...            ...            ...            ...  \n",
            "habitat_meadows        -0.078170      -0.042059      -0.030041      -0.153579  \n",
            "habitat_paths           1.000000      -0.088184      -0.062986      -0.322005  \n",
            "habitat_urban          -0.088184       1.000000      -0.033889      -0.173254  \n",
            "habitat_waste          -0.062986      -0.033889       1.000000      -0.123748  \n",
            "habitat_woods          -0.322005      -0.173254      -0.123748       1.000000  \n",
            "\n",
            "[95 rows x 95 columns]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                   class_poisonous  cap-shape_conical  cap-shape_convex  \\\n",
              "class_poisonous           1.000000           0.023007         -0.026886   \n",
              "cap-shape_conical         0.023007           1.000000         -0.020077   \n",
              "cap-shape_convex         -0.026886          -0.020077          1.000000   \n",
              "cap-shape_flat            0.018526          -0.017672         -0.720235   \n",
              "cap-shape_knobbed         0.163565          -0.007477         -0.304733   \n",
              "...                            ...                ...               ...   \n",
              "habitat_meadows          -0.138627          -0.004286         -0.004529   \n",
              "habitat_paths             0.323346          -0.008985         -0.029042   \n",
              "habitat_urban             0.112078          -0.004835          0.002845   \n",
              "habitat_waste            -0.150087          -0.003453         -0.036493   \n",
              "habitat_woods            -0.126123          -0.017653          0.071773   \n",
              "\n",
              "                   cap-shape_flat  cap-shape_knobbed  cap-shape_sunken  \\\n",
              "class_poisonous          0.018526           0.163565         -0.060664   \n",
              "cap-shape_conical       -0.017672          -0.007477         -0.001396   \n",
              "cap-shape_convex        -0.720235          -0.304733         -0.056884   \n",
              "cap-shape_flat           1.000000          -0.268226         -0.050070   \n",
              "cap-shape_knobbed       -0.268226           1.000000         -0.021185   \n",
              "...                           ...                ...               ...   \n",
              "habitat_meadows         -0.129312          -0.065047         -0.012142   \n",
              "habitat_paths            0.021891           0.090538         -0.025458   \n",
              "habitat_urban            0.030636          -0.073380          0.288697   \n",
              "habitat_waste           -0.017450           0.119003         -0.009784   \n",
              "habitat_woods            0.074986          -0.089230         -0.050018   \n",
              "\n",
              "                   cap-surface_grooves  cap-surface_scaly  cap-surface_smooth  \\\n",
              "class_poisonous               0.023007           0.088677            0.095454   \n",
              "cap-shape_conical             0.249631           0.015893           -0.015038   \n",
              "cap-shape_convex             -0.020077          -0.019137           -0.040634   \n",
              "cap-shape_flat               -0.006285           0.029076           -0.093397   \n",
              "cap-shape_knobbed             0.010863           0.015261            0.137988   \n",
              "...                                ...                ...                 ...   \n",
              "habitat_meadows              -0.004286           0.039697            0.077081   \n",
              "habitat_paths                -0.008985           0.117915           -0.039574   \n",
              "habitat_urban                -0.004835          -0.100248            0.117548   \n",
              "habitat_waste                -0.003453           0.031986            0.062109   \n",
              "habitat_woods                -0.017653           0.110902           -0.301670   \n",
              "\n",
              "                   cap-color_buff  ...  population_numerous  \\\n",
              "class_poisonous          0.067544  ...            -0.219529   \n",
              "cap-shape_conical       -0.003225  ...            -0.005051   \n",
              "cap-shape_convex        -0.020175  ...            -0.004586   \n",
              "cap-shape_flat           0.019203  ...            -0.181191   \n",
              "cap-shape_knobbed       -0.003209  ...             0.013599   \n",
              "...                           ...  ...                  ...   \n",
              "habitat_meadows          0.027701  ...             0.347262   \n",
              "habitat_paths           -0.058829  ...            -0.092129   \n",
              "habitat_urban            0.167996  ...            -0.049569   \n",
              "habitat_waste            0.250709  ...            -0.035405   \n",
              "habitat_woods           -0.115580  ...            -0.181003   \n",
              "\n",
              "                   population_scattered  population_several  \\\n",
              "class_poisonous               -0.159572            0.443722   \n",
              "cap-shape_conical             -0.009456           -0.022075   \n",
              "cap-shape_convex               0.059264           -0.060421   \n",
              "cap-shape_flat                -0.075804            0.034625   \n",
              "cap-shape_knobbed             -0.089360            0.172707   \n",
              "...                                 ...                 ...   \n",
              "habitat_meadows                0.152473           -0.144434   \n",
              "habitat_paths                 -0.125366            0.169228   \n",
              "habitat_urban                  0.130450            0.001180   \n",
              "habitat_waste                 -0.066282           -0.154742   \n",
              "habitat_woods                 -0.271589            0.171068   \n",
              "\n",
              "                   population_solitary  habitat_leaves  habitat_meadows  \\\n",
              "class_poisonous              -0.107055        0.155150        -0.138627   \n",
              "cap-shape_conical            -0.011469        0.065707        -0.004286   \n",
              "cap-shape_convex              0.044626       -0.096639        -0.004529   \n",
              "cap-shape_flat                0.111346       -0.052326        -0.129312   \n",
              "cap-shape_knobbed            -0.170081        0.235108        -0.065047   \n",
              "...                                ...             ...              ...   \n",
              "habitat_meadows              -0.099772       -0.065222         1.000000   \n",
              "habitat_paths                 0.040714       -0.136749        -0.078170   \n",
              "habitat_urban                -0.042888       -0.073577        -0.042059   \n",
              "habitat_waste                -0.080392       -0.052553        -0.030041   \n",
              "habitat_woods                 0.277929       -0.268668        -0.153579   \n",
              "\n",
              "                   habitat_paths  habitat_urban  habitat_waste  habitat_woods  \n",
              "class_poisonous         0.323346       0.112078      -0.150087      -0.126123  \n",
              "cap-shape_conical      -0.008985      -0.004835      -0.003453      -0.017653  \n",
              "cap-shape_convex       -0.029042       0.002845      -0.036493       0.071773  \n",
              "cap-shape_flat          0.021891       0.030636      -0.017450       0.074986  \n",
              "cap-shape_knobbed       0.090538      -0.073380       0.119003      -0.089230  \n",
              "...                          ...            ...            ...            ...  \n",
              "habitat_meadows        -0.078170      -0.042059      -0.030041      -0.153579  \n",
              "habitat_paths           1.000000      -0.088184      -0.062986      -0.322005  \n",
              "habitat_urban          -0.088184       1.000000      -0.033889      -0.173254  \n",
              "habitat_waste          -0.062986      -0.033889       1.000000      -0.123748  \n",
              "habitat_woods          -0.322005      -0.173254      -0.123748       1.000000  \n",
              "\n",
              "[95 rows x 95 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7ef50a73-9ba9-4988-94f9-44d7905762d2\" 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>class_poisonous</th>\n",
              "      <th>cap-shape_conical</th>\n",
              "      <th>cap-shape_convex</th>\n",
              "      <th>cap-shape_flat</th>\n",
              "      <th>cap-shape_knobbed</th>\n",
              "      <th>cap-shape_sunken</th>\n",
              "      <th>cap-surface_grooves</th>\n",
              "      <th>cap-surface_scaly</th>\n",
              "      <th>cap-surface_smooth</th>\n",
              "      <th>cap-color_buff</th>\n",
              "      <th>...</th>\n",
              "      <th>population_numerous</th>\n",
              "      <th>population_scattered</th>\n",
              "      <th>population_several</th>\n",
              "      <th>population_solitary</th>\n",
              "      <th>habitat_leaves</th>\n",
              "      <th>habitat_meadows</th>\n",
              "      <th>habitat_paths</th>\n",
              "      <th>habitat_urban</th>\n",
              "      <th>habitat_waste</th>\n",
              "      <th>habitat_woods</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>class_poisonous</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.023007</td>\n",
              "      <td>-0.026886</td>\n",
              "      <td>0.018526</td>\n",
              "      <td>0.163565</td>\n",
              "      <td>-0.060664</td>\n",
              "      <td>0.023007</td>\n",
              "      <td>0.088677</td>\n",
              "      <td>0.095454</td>\n",
              "      <td>0.067544</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.219529</td>\n",
              "      <td>-0.159572</td>\n",
              "      <td>0.443722</td>\n",
              "      <td>-0.107055</td>\n",
              "      <td>0.155150</td>\n",
              "      <td>-0.138627</td>\n",
              "      <td>0.323346</td>\n",
              "      <td>0.112078</td>\n",
              "      <td>-0.150087</td>\n",
              "      <td>-0.126123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cap-shape_conical</th>\n",
              "      <td>0.023007</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.020077</td>\n",
              "      <td>-0.017672</td>\n",
              "      <td>-0.007477</td>\n",
              "      <td>-0.001396</td>\n",
              "      <td>0.249631</td>\n",
              "      <td>0.015893</td>\n",
              "      <td>-0.015038</td>\n",
              "      <td>-0.003225</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.005051</td>\n",
              "      <td>-0.009456</td>\n",
              "      <td>-0.022075</td>\n",
              "      <td>-0.011469</td>\n",
              "      <td>0.065707</td>\n",
              "      <td>-0.004286</td>\n",
              "      <td>-0.008985</td>\n",
              "      <td>-0.004835</td>\n",
              "      <td>-0.003453</td>\n",
              "      <td>-0.017653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cap-shape_convex</th>\n",
              "      <td>-0.026886</td>\n",
              "      <td>-0.020077</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.720235</td>\n",
              "      <td>-0.304733</td>\n",
              "      <td>-0.056884</td>\n",
              "      <td>-0.020077</td>\n",
              "      <td>-0.019137</td>\n",
              "      <td>-0.040634</td>\n",
              "      <td>-0.020175</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.004586</td>\n",
              "      <td>0.059264</td>\n",
              "      <td>-0.060421</td>\n",
              "      <td>0.044626</td>\n",
              "      <td>-0.096639</td>\n",
              "      <td>-0.004529</td>\n",
              "      <td>-0.029042</td>\n",
              "      <td>0.002845</td>\n",
              "      <td>-0.036493</td>\n",
              "      <td>0.071773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cap-shape_flat</th>\n",
              "      <td>0.018526</td>\n",
              "      <td>-0.017672</td>\n",
              "      <td>-0.720235</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.268226</td>\n",
              "      <td>-0.050070</td>\n",
              "      <td>-0.006285</td>\n",
              "      <td>0.029076</td>\n",
              "      <td>-0.093397</td>\n",
              "      <td>0.019203</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.181191</td>\n",
              "      <td>-0.075804</td>\n",
              "      <td>0.034625</td>\n",
              "      <td>0.111346</td>\n",
              "      <td>-0.052326</td>\n",
              "      <td>-0.129312</td>\n",
              "      <td>0.021891</td>\n",
              "      <td>0.030636</td>\n",
              "      <td>-0.017450</td>\n",
              "      <td>0.074986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cap-shape_knobbed</th>\n",
              "      <td>0.163565</td>\n",
              "      <td>-0.007477</td>\n",
              "      <td>-0.304733</td>\n",
              "      <td>-0.268226</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.021185</td>\n",
              "      <td>0.010863</td>\n",
              "      <td>0.015261</td>\n",
              "      <td>0.137988</td>\n",
              "      <td>-0.003209</td>\n",
              "      <td>...</td>\n",
              "      <td>0.013599</td>\n",
              "      <td>-0.089360</td>\n",
              "      <td>0.172707</td>\n",
              "      <td>-0.170081</td>\n",
              "      <td>0.235108</td>\n",
              "      <td>-0.065047</td>\n",
              "      <td>0.090538</td>\n",
              "      <td>-0.073380</td>\n",
              "      <td>0.119003</td>\n",
              "      <td>-0.089230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>habitat_meadows</th>\n",
              "      <td>-0.138627</td>\n",
              "      <td>-0.004286</td>\n",
              "      <td>-0.004529</td>\n",
              "      <td>-0.129312</td>\n",
              "      <td>-0.065047</td>\n",
              "      <td>-0.012142</td>\n",
              "      <td>-0.004286</td>\n",
              "      <td>0.039697</td>\n",
              "      <td>0.077081</td>\n",
              "      <td>0.027701</td>\n",
              "      <td>...</td>\n",
              "      <td>0.347262</td>\n",
              "      <td>0.152473</td>\n",
              "      <td>-0.144434</td>\n",
              "      <td>-0.099772</td>\n",
              "      <td>-0.065222</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.078170</td>\n",
              "      <td>-0.042059</td>\n",
              "      <td>-0.030041</td>\n",
              "      <td>-0.153579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>habitat_paths</th>\n",
              "      <td>0.323346</td>\n",
              "      <td>-0.008985</td>\n",
              "      <td>-0.029042</td>\n",
              "      <td>0.021891</td>\n",
              "      <td>0.090538</td>\n",
              "      <td>-0.025458</td>\n",
              "      <td>-0.008985</td>\n",
              "      <td>0.117915</td>\n",
              "      <td>-0.039574</td>\n",
              "      <td>-0.058829</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.092129</td>\n",
              "      <td>-0.125366</td>\n",
              "      <td>0.169228</td>\n",
              "      <td>0.040714</td>\n",
              "      <td>-0.136749</td>\n",
              "      <td>-0.078170</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.088184</td>\n",
              "      <td>-0.062986</td>\n",
              "      <td>-0.322005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>habitat_urban</th>\n",
              "      <td>0.112078</td>\n",
              "      <td>-0.004835</td>\n",
              "      <td>0.002845</td>\n",
              "      <td>0.030636</td>\n",
              "      <td>-0.073380</td>\n",
              "      <td>0.288697</td>\n",
              "      <td>-0.004835</td>\n",
              "      <td>-0.100248</td>\n",
              "      <td>0.117548</td>\n",
              "      <td>0.167996</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.049569</td>\n",
              "      <td>0.130450</td>\n",
              "      <td>0.001180</td>\n",
              "      <td>-0.042888</td>\n",
              "      <td>-0.073577</td>\n",
              "      <td>-0.042059</td>\n",
              "      <td>-0.088184</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.033889</td>\n",
              "      <td>-0.173254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>habitat_waste</th>\n",
              "      <td>-0.150087</td>\n",
              "      <td>-0.003453</td>\n",
              "      <td>-0.036493</td>\n",
              "      <td>-0.017450</td>\n",
              "      <td>0.119003</td>\n",
              "      <td>-0.009784</td>\n",
              "      <td>-0.003453</td>\n",
              "      <td>0.031986</td>\n",
              "      <td>0.062109</td>\n",
              "      <td>0.250709</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.035405</td>\n",
              "      <td>-0.066282</td>\n",
              "      <td>-0.154742</td>\n",
              "      <td>-0.080392</td>\n",
              "      <td>-0.052553</td>\n",
              "      <td>-0.030041</td>\n",
              "      <td>-0.062986</td>\n",
              "      <td>-0.033889</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.123748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>habitat_woods</th>\n",
              "      <td>-0.126123</td>\n",
              "      <td>-0.017653</td>\n",
              "      <td>0.071773</td>\n",
              "      <td>0.074986</td>\n",
              "      <td>-0.089230</td>\n",
              "      <td>-0.050018</td>\n",
              "      <td>-0.017653</td>\n",
              "      <td>0.110902</td>\n",
              "      <td>-0.301670</td>\n",
              "      <td>-0.115580</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.181003</td>\n",
              "      <td>-0.271589</td>\n",
              "      <td>0.171068</td>\n",
              "      <td>0.277929</td>\n",
              "      <td>-0.268668</td>\n",
              "      <td>-0.153579</td>\n",
              "      <td>-0.322005</td>\n",
              "      <td>-0.173254</td>\n",
              "      <td>-0.123748</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>95 rows × 95 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    .colab-df-convert:hover {\n",
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              "                                                    [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",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('corr_matrix')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_1213c10a-0ed1-4970-ab8e-f3e1d5e7581e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('corr_matrix');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "corr_matrix"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Visualización de la Matriz de Correlación Completa\n",
        "# Visualizar la matriz de correlación como un heatmap\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.heatmap(corr_matrix, cmap='coolwarm', annot=False)\n",
        "plt.title('Heatmap de la matriz de correlación')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 913
        },
        "id": "JGFvf9gQ-stf",
        "outputId": "5e6e3665-d37e-4897-d0ac-4877dd845623"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Visualización de la Matriz de Correlación Acotada\n",
        "# Filtrar características con correlación absoluta mayor a 0.5 con la variable objetivo\n",
        "corr_target = corr_matrix['class_poisonous'].abs()\n",
        "relevant_features = corr_target[corr_target > 0.5].index\n",
        "\n",
        "# Crear una nueva matriz de correlación solo con las características relevantes\n",
        "filtered_corr_matrix = mushroom_data_encoded[relevant_features].corr()\n",
        "\n",
        "# Graficar la nueva matriz de correlación\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.heatmap(filtered_corr_matrix, annot=True, fmt='.2f', cmap='coolwarm')\n",
        "plt.title('Matriz de Correlación (Características Relevantes)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 892
        },
        "id": "h0ZFQ6FA-02g",
        "outputId": "a1dc5bc5-dc95-4e79-c926-a597faef2a82"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Selección de Características\n",
        "\n",
        "\n",
        "#Caso 1: Seleccionar las características más correlacionadas con la clase 'poisonous'\n",
        "selected_features = corr_matrix['class_poisonous'].sort_values(ascending=False).index[1:7].tolist()\n",
        "\n",
        "print(\"Caso 1: Características más Correlaciondas\")\n",
        "print(corr_matrix['class_poisonous'].sort_values(ascending=False).index[1:6])\n",
        "\n",
        "# Caso 2: Seleccionar todas las características (da 100% en los modelos)\n",
        "#selected_features = mushroom_data_encoded.columns.drop('class').tolist()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Xg-amzbjzmto",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbe46408-e412-4ec4-afb8-6ede25ffc7f4"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Caso 1: Características más Correlaciondas\n",
            "Index(['odor_foul', 'stalk-surface-above-ring_silky',\n",
            "       'stalk-surface-below-ring_silky', 'gill-size_narrow',\n",
            "       'gill-color_buff'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Separar las características y la variable objetivo\n",
        "X = mushroom_data_encoded[selected_features]\n",
        "y = mushroom_data_encoded['class_poisonous']"
      ],
      "metadata": {
        "id": "tFChQjC7tMTA"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title División I: Datos en conjuntos de entrenamiento y prueba\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n"
      ],
      "metadata": {
        "id": "4OpXHkBk60Og"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title División II: Datos en conjuntos de entrenamiento, validación y prueba\n",
        "# 70% entrenamiento y 30% el resto\n",
        "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "\n",
        "#El 30% restante en 20% pruebas y 10% validación\n",
        "# Nota: 20% pruebas es el 2/3 de 30% (lo que resulta en 20%), y 10% validación es el 1/3 de 30%\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=2/3, random_state=42)\n",
        "\n",
        "# Verificar las proporciones\n",
        "print(f\"Tamaño de X_train: {len(X_train)}\")\n",
        "print(f\"Tamaño de X_test: {len(X_test)}\")\n",
        "print(f\"Tamaño de X_val: {len(X_val)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oNSBaQBojGWo",
        "outputId": "efcd136d-5791-4ef6-f751-2865242a381f"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tamaño de X_train: 5686\n",
            "Tamaño de X_test: 1626\n",
            "Tamaño de X_val: 812\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **#Modelos que no requieren escalado de características**"
      ],
      "metadata": {
        "id": "lDOiDD2IB4DD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Naive Bayes\n",
        "\n",
        "# No es recomendable escalar las características\n",
        "# para Naive Bayes Categórico\n",
        "\n",
        "# Entrenar el modelo de Naive Bayes\n",
        "model = CategoricalNB()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "y_pred = model.predict(X_train)\n",
        "print(\"Precisión del modelo en Entr:\", accuracy_score(y_train, y_pred))\n",
        "print(\"Recall del modelo: en Entr\", recall_score(y_train, y_pred))\n",
        "print(\"F1-Score del modelo en Entr:\", f1_score(y_train, y_pred))\n",
        "print(\"Reporte de clasificación en Entr:\\n\", classification_report(y_train, y_pred))\n",
        "\n",
        "# Hacer predicciones y evaluar el modelo\n",
        "y_pred = model.predict(X_test)\n",
        "print(\"Precisión del modelo:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Recall del modelo:\", recall_score(y_test, y_pred))\n",
        "print(\"F1-Score del modelo:\", f1_score(y_test, y_pred))\n",
        "print(\"Reporte de clasificación:\\n\", classification_report(y_test, y_pred))\n",
        "\n",
        "# Evaluar el modelo en el conjunto de validación\n",
        "y_val_pred = model.predict(X_val)\n",
        "print(\"Precisión del modelo en validación:\", accuracy_score(y_val, y_val_pred))\n",
        "print(\"Recall del modelo en validación:\", recall_score(y_val, y_val_pred))\n",
        "print(\"F1-Score del modelo en validación:\", f1_score(y_val, y_val_pred))\n",
        "print(\"Reporte de clasificación en validación:\\n\", classification_report(y_val, y_val_pred))\n",
        "\n",
        "print()\n",
        "print(\"Parametros Usados\")\n",
        "print(model.get_params())\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "balTLUqZJ0Un",
        "outputId": "2be939ef-6626-4a72-efb5-01aadddfa723"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo en Entr: 0.9104818853323954\n",
            "Recall del modelo: en Entr 0.8665447897623401\n",
            "F1-Score del modelo en Entr: 0.9030291484092209\n",
            "Reporte de clasificación en Entr:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.88      0.95      0.92      2951\n",
            "        True       0.94      0.87      0.90      2735\n",
            "\n",
            "    accuracy                           0.91      5686\n",
            "   macro avg       0.91      0.91      0.91      5686\n",
            "weighted avg       0.91      0.91      0.91      5686\n",
            "\n",
            "Precisión del modelo: 0.8985239852398524\n",
            "Recall del modelo: 0.8573264781491002\n",
            "F1-Score del modelo: 0.8899266177451634\n",
            "Reporte de clasificación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.88      0.94      0.91       848\n",
            "        True       0.93      0.86      0.89       778\n",
            "\n",
            "    accuracy                           0.90      1626\n",
            "   macro avg       0.90      0.90      0.90      1626\n",
            "weighted avg       0.90      0.90      0.90      1626\n",
            "\n",
            "Precisión del modelo en validación: 0.9039408866995073\n",
            "Recall del modelo en validación: 0.8511166253101737\n",
            "F1-Score del modelo en validación: 0.8979057591623038\n",
            "Reporte de clasificación en validación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.87      0.96      0.91       409\n",
            "        True       0.95      0.85      0.90       403\n",
            "\n",
            "    accuracy                           0.90       812\n",
            "   macro avg       0.91      0.90      0.90       812\n",
            "weighted avg       0.91      0.90      0.90       812\n",
            "\n",
            "\n",
            "Parametros Usados\n",
            "{'alpha': 1.0, 'class_prior': None, 'fit_prior': True, 'force_alpha': 'warn', 'min_categories': None}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Random Forest\n",
        "\n",
        "\n",
        "# No es recomendable escalar las características\n",
        "# para Random Forest\n",
        "\n",
        "# Entrenar el modelo de Random Forest\n",
        "model = RandomForestClassifier(random_state=42)\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = model.predict(X_train)\n",
        "print(\"Precisión del modelo en Entr:\", accuracy_score(y_train, y_pred))\n",
        "print(\"Recall del modelo: en Entr\", recall_score(y_train, y_pred))\n",
        "print(\"F1-Score del modelo en Entr:\", f1_score(y_train, y_pred))\n",
        "print(\"Reporte de clasificación en Entr:\\n\", classification_report(y_train, y_pred))\n",
        "\n",
        "\n",
        "# Hacer predicciones y evaluar el modelo\n",
        "y_pred = model.predict(X_test)\n",
        "print(\"Precisión del modelo en Test:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Recall del modelo: en Test\", recall_score(y_test, y_pred))\n",
        "print(\"F1-Score del modelo en Test:\", f1_score(y_test, y_pred))\n",
        "print(\"Reporte de clasificación en Test:\\n\", classification_report(y_test, y_pred))\n",
        "\n",
        "# Evaluar el modelo en el conjunto de validación\n",
        "y_val_pred = model.predict(X_val)\n",
        "print(\"Precisión del modelo en validación:\", accuracy_score(y_val, y_val_pred))\n",
        "print(\"Recall del modelo en validación:\", recall_score(y_val, y_val_pred))\n",
        "print(\"F1-Score del modelo en validación:\", f1_score(y_val, y_val_pred))\n",
        "print(\"Reporte de clasificación en validación:\\n\", classification_report(y_val, y_val_pred))\n",
        "\n",
        "\n",
        "print()\n",
        "print(\"Parametros Usados\")\n",
        "print(model.get_params())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "khJYyTe-Kkkf",
        "outputId": "f07dd106-1e84-4178-a04b-77794c3986d6"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo en Entr: 0.9523390784382695\n",
            "Recall del modelo: en Entr 0.9714808043875686\n",
            "F1-Score del modelo en Entr: 0.9514771709937332\n",
            "Reporte de clasificación en Entr:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.97      0.93      0.95      2951\n",
            "        True       0.93      0.97      0.95      2735\n",
            "\n",
            "    accuracy                           0.95      5686\n",
            "   macro avg       0.95      0.95      0.95      5686\n",
            "weighted avg       0.95      0.95      0.95      5686\n",
            "\n",
            "Precisión del modelo en Test: 0.9544895448954489\n",
            "Recall del modelo: en Test 0.9768637532133676\n",
            "F1-Score del modelo en Test: 0.9535759096612295\n",
            "Reporte de clasificación en Test:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.98      0.93      0.96       848\n",
            "        True       0.93      0.98      0.95       778\n",
            "\n",
            "    accuracy                           0.95      1626\n",
            "   macro avg       0.95      0.96      0.95      1626\n",
            "weighted avg       0.96      0.95      0.95      1626\n",
            "\n",
            "Precisión del modelo en validación: 0.937192118226601\n",
            "Recall del modelo en validación: 0.9702233250620348\n",
            "F1-Score del modelo en validación: 0.9387755102040817\n",
            "Reporte de clasificación en validación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.97      0.90      0.94       409\n",
            "        True       0.91      0.97      0.94       403\n",
            "\n",
            "    accuracy                           0.94       812\n",
            "   macro avg       0.94      0.94      0.94       812\n",
            "weighted avg       0.94      0.94      0.94       812\n",
            "\n",
            "\n",
            "Parametros Usados\n",
            "{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **#Modelos que requieren escalado de características**"
      ],
      "metadata": {
        "id": "f4ZBsI_WCV76"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Escalado de las características\n",
        "# Guardar los nombres de las columnas antes de escalar\n",
        "feature_names = X_train.columns\n",
        "\n",
        "# Escalar las características\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "#Para validacion\n",
        "X_val_scaled = scaler.transform(X_val)\n",
        "\n"
      ],
      "metadata": {
        "id": "1aljGFWJ9KXa"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Regresion Logística\n",
        "\n",
        "#EDIT si selecciono todas las carac da 100%\n",
        "# Entrenar el modelo de regresión logística\n",
        "model = LogisticRegression(random_state=42)\n",
        "model.fit(X_train_scaled, y_train)\n",
        "\n",
        "\n",
        "y_pred = model.predict(X_train_scaled)\n",
        "print(\"Precisión del modelo en Entr:\", accuracy_score(y_train, y_pred))\n",
        "print(\"Recall del modelo: en Entr\", recall_score(y_train, y_pred))\n",
        "print(\"F1-Score del modelo en Entr:\", f1_score(y_train, y_pred))\n",
        "print(\"Reporte de clasificación en Entr:\\n\", classification_report(y_train, y_pred))\n",
        "\n",
        "# Hacer predicciones y evaluar el modelo\n",
        "y_pred = model.predict(X_test_scaled)\n",
        "print(\"Precisión del modelo:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Recall del modelo:\", recall_score(y_test, y_pred))\n",
        "print(\"F1-Score del modelo:\", f1_score(y_test, y_pred))\n",
        "print(\"Reporte de clasificación:\\n\", classification_report(y_test, y_pred))\n",
        "\n",
        "# Evaluar el modelo en el conjunto de validación\n",
        "y_val_pred = model.predict(X_val_scaled)\n",
        "print(\"Precisión del modelo en validación:\", accuracy_score(y_val, y_val_pred))\n",
        "print(\"Recall del modelo en validación:\", recall_score(y_val, y_val_pred))\n",
        "print(\"F1-Score del modelo en validación:\", f1_score(y_val, y_val_pred))\n",
        "print(\"Reporte de clasificación en validación:\\n\", classification_report(y_val, y_val_pred))\n",
        "\n",
        "print()\n",
        "print(\"Parametros Usados\")\n",
        "print(model.get_params())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RgfTrkBJ1m6B",
        "outputId": "8e1e7a88-68d0-47f6-a35c-bbd39fa62597"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo en Entr: 0.9523390784382695\n",
            "Recall del modelo: en Entr 0.9714808043875686\n",
            "F1-Score del modelo en Entr: 0.9514771709937332\n",
            "Reporte de clasificación en Entr:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.97      0.93      0.95      2951\n",
            "        True       0.93      0.97      0.95      2735\n",
            "\n",
            "    accuracy                           0.95      5686\n",
            "   macro avg       0.95      0.95      0.95      5686\n",
            "weighted avg       0.95      0.95      0.95      5686\n",
            "\n",
            "Precisión del modelo: 0.9544895448954489\n",
            "Recall del modelo: 0.9768637532133676\n",
            "F1-Score del modelo: 0.9535759096612295\n",
            "Reporte de clasificación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.98      0.93      0.96       848\n",
            "        True       0.93      0.98      0.95       778\n",
            "\n",
            "    accuracy                           0.95      1626\n",
            "   macro avg       0.95      0.96      0.95      1626\n",
            "weighted avg       0.96      0.95      0.95      1626\n",
            "\n",
            "Precisión del modelo en validación: 0.937192118226601\n",
            "Recall del modelo en validación: 0.9702233250620348\n",
            "F1-Score del modelo en validación: 0.9387755102040817\n",
            "Reporte de clasificación en validación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.97      0.90      0.94       409\n",
            "        True       0.91      0.97      0.94       403\n",
            "\n",
            "    accuracy                           0.94       812\n",
            "   macro avg       0.94      0.94      0.94       812\n",
            "weighted avg       0.94      0.94      0.94       812\n",
            "\n",
            "\n",
            "Parametros Usados\n",
            "{'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 42, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title KNN\n",
        "\n",
        "# Entrenar el modelo de KNN\n",
        "model = KNeighborsClassifier()  # Puedes ajustar n_neighbors según sea necesario\n",
        "model.fit(X_train_scaled, y_train)\n",
        "\n",
        "y_pred = model.predict(X_train_scaled)\n",
        "print(\"Precisión del modelo en Entr:\", accuracy_score(y_train, y_pred))\n",
        "print(\"Recall del modelo: en Entr\", recall_score(y_train, y_pred))\n",
        "print(\"F1-Score del modelo en Entr:\", f1_score(y_train, y_pred))\n",
        "print(\"Reporte de clasificación en Entr:\\n\", classification_report(y_train, y_pred))\n",
        "\n",
        "\n",
        "# Hacer predicciones y evaluar el modelo\n",
        "y_pred = model.predict(X_test_scaled)\n",
        "print(\"Precisión del modelo:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Recall del modelo:\", recall_score(y_test, y_pred))\n",
        "print(\"F1-Score del modelo:\", f1_score(y_test, y_pred))\n",
        "print(\"Reporte de clasificación:\\n\", classification_report(y_test, y_pred))\n",
        "\n",
        "\n",
        "# Evaluar el modelo en el conjunto de validación\n",
        "y_val_pred = model.predict(X_val_scaled)\n",
        "print(\"Precisión del modelo en validación:\", accuracy_score(y_val, y_val_pred))\n",
        "print(\"Recall del modelo en validación:\", recall_score(y_val, y_val_pred))\n",
        "print(\"F1-Score del modelo en validación:\", f1_score(y_val, y_val_pred))\n",
        "print(\"Reporte de clasificación en validación:\\n\", classification_report(y_val, y_val_pred))\n",
        "\n",
        "print()\n",
        "print(\"Parametros Usados\")\n",
        "print(model.get_params())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yZIStD00r_lg",
        "outputId": "9acbcb45-ae55-4abd-8ec3-9dbdb27f2099"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo en Entr: 0.9474147027787548\n",
            "Recall del modelo: en Entr 0.9297989031078611\n",
            "F1-Score del modelo en Entr: 0.9444753946146704\n",
            "Reporte de clasificación en Entr:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.94      0.96      0.95      2951\n",
            "        True       0.96      0.93      0.94      2735\n",
            "\n",
            "    accuracy                           0.95      5686\n",
            "   macro avg       0.95      0.95      0.95      5686\n",
            "weighted avg       0.95      0.95      0.95      5686\n",
            "\n",
            "Precisión del modelo: 0.942189421894219\n",
            "Recall del modelo: 0.9383033419023136\n",
            "F1-Score del modelo: 0.9395109395109394\n",
            "Reporte de clasificación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.94      0.95      0.94       848\n",
            "        True       0.94      0.94      0.94       778\n",
            "\n",
            "    accuracy                           0.94      1626\n",
            "   macro avg       0.94      0.94      0.94      1626\n",
            "weighted avg       0.94      0.94      0.94      1626\n",
            "\n",
            "Precisión del modelo en validación: 0.9421182266009852\n",
            "Recall del modelo en validación: 0.9205955334987593\n",
            "F1-Score del modelo en validación: 0.9404309252217997\n",
            "Reporte de clasificación en validación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.92      0.96      0.94       409\n",
            "        True       0.96      0.92      0.94       403\n",
            "\n",
            "    accuracy                           0.94       812\n",
            "   macro avg       0.94      0.94      0.94       812\n",
            "weighted avg       0.94      0.94      0.94       812\n",
            "\n",
            "\n",
            "Parametros Usados\n",
            "{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': None, 'n_neighbors': 5, 'p': 2, 'weights': 'uniform'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Optimización mediante RandomizedSearchCV"
      ],
      "metadata": {
        "id": "XeamKqDSmcu2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title OPTIMIZACION: Random Forest\n",
        "\n",
        "param_distribs = {\n",
        "        'n_estimators': randint(low=50, high=500),\n",
        "        'max_depth': randint(low=8, high=50),\n",
        "        'min_samples_split':randint(low=2, high=10),\n",
        "        'min_samples_leaf': randint(low=1, high=4)\n",
        "    }\n",
        "\n",
        "rnd_clf = RandomForestClassifier(n_jobs=-1)\n",
        "\n",
        "rnd_search = RandomizedSearchCV(rnd_clf, param_distributions=param_distribs,\n",
        "                                n_iter=4, cv=30, scoring='accuracy')\n",
        "\n",
        "rnd_search.fit(X_train, y_train)\n",
        "\n",
        "#Almacenar los resultados en variables para posterior analisis\n",
        "best_params_rf = rnd_search.best_params_\n",
        "best_score_rf = rnd_search.best_score_\n",
        "best_estimator_rf = rnd_search.best_estimator_\n",
        "\n"
      ],
      "metadata": {
        "id": "aJEAIVBmmh62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "63d9cf26-0347-4869-964a-37cfcb65df2c"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-71-c4435813695d>\u001b[0m in \u001b[0;36m<cell line: 15>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m                                 n_iter=4, cv=30, scoring='accuracy')\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mrnd_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;31m#Almacenar los resultados en variables para posterior analisis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    872\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 874\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    875\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    876\u001b[0m             \u001b[0;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1766\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1767\u001b[0m         \u001b[0;34m\"\"\"Search n_iter candidates from param_distributions\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1768\u001b[0;31m         evaluate_candidates(\n\u001b[0m\u001b[1;32m   1769\u001b[0m             ParameterSampler(\n\u001b[1;32m   1770\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_distributions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[1;32m    819\u001b[0m                     )\n\u001b[1;32m    820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 821\u001b[0;31m                 out = parallel(\n\u001b[0m\u001b[1;32m    822\u001b[0m                     delayed(_fit_and_score)(\n\u001b[1;32m    823\u001b[0m                         \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_estimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mdelayed_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         )\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable_with_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1916\u001b[0m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_sequential_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1917\u001b[0m             \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1918\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_generator\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1919\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1920\u001b[0m         \u001b[0;31m# Let's create an ID that uniquely identifies the current call. If the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_get_sequential_output\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1845\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_dispatched_batches\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1846\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_dispatched_tasks\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1847\u001b[0;31m                 \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1848\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_completed_tasks\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1849\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_progress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[1;32m    684\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    685\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 686\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    471\u001b[0m             \u001b[0;31m# parallel_backend contexts set at a higher level,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    472\u001b[0m             \u001b[0;31m# since correctness does not rely on using threads.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 473\u001b[0;31m             trees = Parallel(\n\u001b[0m\u001b[1;32m    474\u001b[0m                 \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    475\u001b[0m                 \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mdelayed_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         )\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable_with_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   2005\u001b[0m         \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2007\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_generator\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2008\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2009\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_get_outputs\u001b[0;34m(self, iterator, pre_dispatch)\u001b[0m\n\u001b[1;32m   1648\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1649\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1650\u001b[0;31m                 \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_retrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1651\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1652\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mGeneratorExit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1760\u001b[0m                 (self._jobs[0].get_status(\n\u001b[1;32m   1761\u001b[0m                     timeout=self.timeout) == TASK_PENDING)):\n\u001b[0;32m-> 1762\u001b[0;31m                 \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1763\u001b[0m                 \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1764\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title OPTIMIZACION: CategoricalNB\n",
        "\n",
        "param_distribs = {\n",
        "    'alpha': [0.01, 0.1, 1.0, 10.0],\n",
        "}\n",
        "\n",
        "# Inicializa modelo CategoricalNB\n",
        "cat_nb = CategoricalNB()\n",
        "\n",
        "# Realiza Randomized Search\n",
        "rnd_search = RandomizedSearchCV(cat_nb, param_distributions=param_distribs,\n",
        "                                n_iter=4, cv=5, scoring='accuracy',\n",
        "                                random_state=42, n_jobs=-1)\n",
        "\n",
        "rnd_search.fit(X_train, y_train)\n",
        "\n",
        "#Almacenar los resultados en variables para posterior analisis\n",
        "best_params_nb = rnd_search.best_params_\n",
        "best_score_nb = rnd_search.best_score_\n",
        "best_estimator_nb = rnd_search.best_estimator_"
      ],
      "metadata": {
        "id": "nv0mV9iw-XH-"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title OPTIMIZACION: KNN\n",
        "\n",
        "# Parámetros ajustables de KNeighborsClassifier\n",
        "param_distribs = {\n",
        "    'n_neighbors': randint(low=3, high=31),\n",
        "    'weights': ['uniform', 'distance'],\n",
        "    'p': [1, 2]                             # Minkowski distance\n",
        "}\n",
        "\n",
        "\n",
        "knn = KNeighborsClassifier()\n",
        "\n",
        "\n",
        "rnd_search = RandomizedSearchCV(knn, param_distributions=param_distribs,\n",
        "                                n_iter=10, cv=5, scoring='accuracy',\n",
        "                                random_state=42, n_jobs=-1)\n",
        "\n",
        "\n",
        "rnd_search.fit(X_train_scaled, y_train)\n",
        "\n",
        "#Almacenar los resultados en variables para posterior analisis\n",
        "best_params_knn = rnd_search.best_params_\n",
        "best_score_knn = rnd_search.best_score_\n",
        "best_estimator_knn = rnd_search.best_estimator_"
      ],
      "metadata": {
        "id": "kY5inHK-HIel"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title OPTIMIZACION: Reg Log\n",
        "\n",
        "from scipy.stats import uniform\n",
        "# Parameteros\n",
        "param_distribs = {\n",
        "    'C': uniform(loc=0.01, scale=99.99),  # Distribución uniforme entre 0.01 y 100\n",
        "    'penalty': ['l1', 'l2'],\n",
        "    'solver': ['liblinear', 'saga']\n",
        "    }\n",
        "\n",
        "\n",
        "logreg = LogisticRegression(solver='saga', max_iter=1000, random_state=42)\n",
        "# Nota: 'saga' solver soporta  l1 y l2\n",
        "\n",
        "rnd_search = RandomizedSearchCV(logreg, param_distributions=param_distribs,\n",
        "                                n_iter=10, cv=5, scoring='accuracy',\n",
        "                                random_state=42, n_jobs=-1)\n",
        "\n",
        "\n",
        "rnd_search.fit(X_train_scaled, y_train)\n",
        "\n",
        "#Almacenar los resultados en variables para posterior analisis\n",
        "best_params_reg = rnd_search.best_params_\n",
        "best_score_reg = rnd_search.best_score_\n",
        "best_estimator_reg = rnd_search.best_estimator_"
      ],
      "metadata": {
        "id": "1XRBkxUI_Gkl"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Acceso a los resultados\n",
        "\n",
        "#Nota, usando más cv se mejora marginalmente los decimales\n",
        "print(\"Random Forest - Resultados de Optimización:\")\n",
        "print(best_params_rf)\n",
        "print(\"Random Forest - Accuracy:\")\n",
        "print(best_score_rf)\n",
        "print(\"Random Forest - Mejores hiperparámetros:\")\n",
        "print(best_estimator_rf)\n",
        "\n",
        "print(\"\")\n",
        "print(\"CategoricalNB - Resultados de Optimización:\")\n",
        "print(best_params_nb)\n",
        "print(\"CategoricalNB - Accuracy:\")\n",
        "print(best_score_nb)\n",
        "print(\"CategoricalNB - Mejor hiperparámetros:\")\n",
        "print(best_estimator_nb)\n",
        "\n",
        "print(\"\")\n",
        "print(\"KNN - Resultados de Optimización:\")\n",
        "print(best_params_knn)\n",
        "print(\"KNN - Accuracy:\")\n",
        "print(best_score_knn)\n",
        "print(\"KNN - Mejor hiperparámetros:\")\n",
        "print(best_estimator_knn)\n",
        "\n",
        "print(\"\")\n",
        "print(\"Reg Log - Resultados de Optimización:\")\n",
        "print(best_params_reg)\n",
        "print(\"Reg Log - Accuracy:\")\n",
        "print(best_score_reg)\n",
        "print(\"Reg Log - Mejor hiperparámetros:\")\n",
        "print(best_estimator_reg)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "zIumOFrHphs6",
        "outputId": "028f3d40-01f4-42ab-ca4a-603e53153374"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest - Resultados de Optimización:\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'best_params_rf' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-75-d9c8f9f4e19a>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m#Nota, usando más cv se mejora marginalmente los decimales\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Random Forest - Resultados de Optimización:\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_params_rf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Random Forest - Accuracy:\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_score_rf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'best_params_rf' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Optimización Usando GridSearchCV"
      ],
      "metadata": {
        "id": "Htgbo3DdNCHc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Naive Bayes con GridSearchCV y y Stratified Cross-Validation\n",
        "\n",
        "# No es recomendable escalar las características\n",
        "# para Naive Bayes Categórico\n",
        "\n",
        "# Parámetros a ajustar\n",
        "param_grid = {\n",
        "    'alpha': [0.01, 0.1, 1.0, 10.0],  # Smoothing parameter\n",
        "}\n",
        "\n",
        "# Inicializar CategoricalNB\n",
        "cat_nb = CategoricalNB()\n",
        "\n",
        "# Inicializar GridSearchCV con validación cruzada estratificada\n",
        "grid_search = GridSearchCV(cat_nb, param_grid, cv=StratifiedKFold(n_splits=5, shuffle=True, random_state=42),\n",
        "                           scoring='accuracy', n_jobs=-1)\n",
        "\n",
        "# Ajustar el modelo a los datos de entrenamiento\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Obtener los mejores parámetros y el mejor estimador\n",
        "best_params = grid_search.best_params_\n",
        "best_model = grid_search.best_estimator_\n",
        "\n",
        "print(\"Mejores parámetros:\", best_params)\n",
        "\n",
        "# Evaluar el mejor modelo en el conjunto de prueba (si tienes uno separado)\n",
        "y_pred = best_model.predict(X_test)\n",
        "print(\"Precisión del modelo:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Recall del modelo:\", recall_score(y_test, y_pred))\n",
        "print(\"F1-Score del modelo:\", f1_score(y_test, y_pred))\n",
        "print(\"Reporte de clasificación:\\n\", classification_report(y_test, y_pred))\n",
        "\n",
        "# Evaluar el modelo en el conjunto de validación\n",
        "y_val_pred = best_model.predict(X_val)\n",
        "print(\"Precisión del modelo en validación:\", accuracy_score(y_val, y_val_pred))\n",
        "print(\"Recall del modelo en validación:\", recall_score(y_val, y_val_pred))\n",
        "print(\"F1-Score del modelo en validación:\", f1_score(y_val, y_val_pred))\n",
        "print(\"Reporte de clasificación en validación:\\n\", classification_report(y_val, y_val_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4cRXieKWK7my",
        "outputId": "43b61083-0968-477c-c56d-a159d8caa50c"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mejores parámetros: {'alpha': 0.01}\n",
            "Precisión del modelo: 0.8985239852398524\n",
            "Recall del modelo: 0.8573264781491002\n",
            "F1-Score del modelo: 0.8899266177451634\n",
            "Reporte de clasificación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.88      0.94      0.91       848\n",
            "        True       0.93      0.86      0.89       778\n",
            "\n",
            "    accuracy                           0.90      1626\n",
            "   macro avg       0.90      0.90      0.90      1626\n",
            "weighted avg       0.90      0.90      0.90      1626\n",
            "\n",
            "Precisión del modelo en validación: 0.9039408866995073\n",
            "Recall del modelo en validación: 0.8511166253101737\n",
            "F1-Score del modelo en validación: 0.8979057591623038\n",
            "Reporte de clasificación en validación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.87      0.96      0.91       409\n",
            "        True       0.95      0.85      0.90       403\n",
            "\n",
            "    accuracy                           0.90       812\n",
            "   macro avg       0.91      0.90      0.90       812\n",
            "weighted avg       0.91      0.90      0.90       812\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Random Forest con GridSearchCV y Stratified Cross-Validation\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Parámetros a ajustar para RandomForest\n",
        "param_grid = {\n",
        "    'n_estimators': [50, 100, 200, 250, 300, 350, 400, 450, 500],\n",
        "    'max_depth': [None, 10, 20, 30],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4]\n",
        "}\n",
        "\n",
        "# Inicializar RandomForestClassifier\n",
        "rf_classifier = RandomForestClassifier(random_state=42)\n",
        "\n",
        "# Inicializar GridSearchCV con validación cruzada estratificada\n",
        "grid_search = GridSearchCV(rf_classifier, param_grid, cv=StratifiedKFold(n_splits=5, shuffle=True, random_state=42),\n",
        "                           scoring='accuracy', n_jobs=-1)\n",
        "\n",
        "# Ajustar el modelo a los datos de entrenamiento\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Obtener los mejores parámetros y el mejor estimador\n",
        "best_params = grid_search.best_params_\n",
        "best_model = grid_search.best_estimator_\n",
        "\n",
        "print(\"Mejores parámetros:\", best_params)\n",
        "\n",
        "# Evaluar el mejor modelo en el conjunto de prueba\n",
        "y_pred = best_model.predict(X_test)\n",
        "print(\"Precisión del modelo:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Reporte de clasificación:\\n\", classification_report(y_test, y_pred))\n",
        "\n",
        "# Evaluar el modelo en el conjunto de validación\n",
        "y_val_pred = best_model.predict(X_val)\n",
        "print(\"Precisión del modelo en validación:\", accuracy_score(y_val, y_val_pred))\n",
        "print(\"Reporte de clasificación en validación:\\n\", classification_report(y_val, y_val_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "ZePWCJrFN4_6",
        "outputId": "8e8c9375-4623-46e9-8bb9-277d272d9282"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-78-5a7e0b3aef5f>\u001b[0m in \u001b[0;36m<cell line: 23>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;31m# Ajustar el modelo a los datos de entrenamiento\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;31m# Obtener los mejores parámetros y el mejor estimador\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    872\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 874\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    875\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    876\u001b[0m             \u001b[0;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1386\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1387\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1388\u001b[0;31m         \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[1;32m    819\u001b[0m                     )\n\u001b[1;32m    820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 821\u001b[0;31m                 out = parallel(\n\u001b[0m\u001b[1;32m    822\u001b[0m                     delayed(_fit_and_score)(\n\u001b[1;32m    823\u001b[0m                         \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_estimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mdelayed_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         )\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable_with_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   2005\u001b[0m         \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2007\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_generator\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2008\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2009\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_get_outputs\u001b[0;34m(self, iterator, pre_dispatch)\u001b[0m\n\u001b[1;32m   1648\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1649\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1650\u001b[0;31m                 \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_retrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1651\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1652\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mGeneratorExit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1760\u001b[0m                 (self._jobs[0].get_status(\n\u001b[1;32m   1761\u001b[0m                     timeout=self.timeout) == TASK_PENDING)):\n\u001b[0;32m-> 1762\u001b[0;31m                 \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1763\u001b[0m                 \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1764\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title KNN con GridSearchCV y Stratified Cross-Validation\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Parámetros a ajustar para KNN\n",
        "param_grid = {\n",
        "    'n_neighbors': [3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31],\n",
        "    'weights': ['uniform', 'distance'],\n",
        "    'p': [1, 2]  # 1: Manhattan distance, 2: Euclidean distance\n",
        "}\n",
        "\n",
        "# Inicializar KNeighborsClassifier\n",
        "knn_classifier = KNeighborsClassifier()\n",
        "\n",
        "# Inicializar StratifiedKFold para validación cruzada estratificada\n",
        "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
        "\n",
        "# Inicializar GridSearchCV con StratifiedKFold\n",
        "grid_search = GridSearchCV(knn_classifier, param_grid, cv=skf,\n",
        "                           scoring='accuracy', n_jobs=-1)\n",
        "\n",
        "# Ajustar el modelo a los datos de entrenamiento escalados\n",
        "grid_search.fit(X_train_scaled, y_train)  # Usar X_train_scaled\n",
        "\n",
        "# Obtener los mejores parámetros y el mejor estimador\n",
        "best_params = grid_search.best_params_\n",
        "best_model = grid_search.best_estimator_\n",
        "\n",
        "print(\"Mejores parámetros:\", best_params)\n",
        "\n",
        "# Evaluar el mejor modelo en el conjunto de prueba (asumiendo que X_test_scaled también está disponible)\n",
        "y_pred = best_model.predict(X_test_scaled)  # Usar X_test_scaled\n",
        "print(\"Precisión del modelo:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Reporte de clasificación:\\n\", classification_report(y_test, y_pred))\n",
        "\n",
        "# Evaluar el modelo en el conjunto de validación\n",
        "y_val_pred = best_model.predict(X_val_scaled)\n",
        "print(\"Precisión del modelo en validación:\", accuracy_score(y_val, y_val_pred))\n",
        "print(\"Reporte de clasificación en validación:\\n\", classification_report(y_val, y_val_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4coKvergSpm9",
        "outputId": "f854f892-7d49-45e3-8c5d-70971920b9ae"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mejores parámetros: {'n_neighbors': 23, 'p': 1, 'weights': 'uniform'}\n",
            "Precisión del modelo: 0.9526445264452644\n",
            "Reporte de clasificación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.94      0.97      0.96       848\n",
            "        True       0.97      0.93      0.95       778\n",
            "\n",
            "    accuracy                           0.95      1626\n",
            "   macro avg       0.95      0.95      0.95      1626\n",
            "weighted avg       0.95      0.95      0.95      1626\n",
            "\n",
            "Precisión del modelo en validación: 0.9433497536945813\n",
            "Reporte de clasificación en validación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.92      0.97      0.95       409\n",
            "        True       0.97      0.91      0.94       403\n",
            "\n",
            "    accuracy                           0.94       812\n",
            "   macro avg       0.95      0.94      0.94       812\n",
            "weighted avg       0.94      0.94      0.94       812\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Regresión Logística con GridSearchCV y Stratified Cross-Validation\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Parámetros a ajustar para Regresión Logística\n",
        "param_grid = {\n",
        "    'penalty': ['l1', 'l2'],\n",
        "    'C': [0.01, 0.1, 1, 10, 100],\n",
        "    'solver': ['liblinear', 'saga']  # Solvers que soportan ambas penalizaciones\n",
        "}\n",
        "\n",
        "# Inicializar LogisticRegression\n",
        "logreg_classifier = LogisticRegression(random_state=42, max_iter=1000)  # Aumentar max_iter si es necesario\n",
        "\n",
        "# Inicializar StratifiedKFold para validación cruzada estratificada\n",
        "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
        "\n",
        "# Inicializar GridSearchCV con StratifiedKFold\n",
        "grid_search = GridSearchCV(logreg_classifier, param_grid, cv=skf,\n",
        "                           scoring='accuracy', n_jobs=-1)\n",
        "\n",
        "# Ajustar el modelo a los datos de entrenamiento escalados\n",
        "grid_search.fit(X_train_scaled, y_train)\n",
        "\n",
        "# Obtener los mejores parámetros y el mejor estimador\n",
        "best_params = grid_search.best_params_\n",
        "best_model = grid_search.best_estimator_\n",
        "\n",
        "print(\"Mejores parámetros:\", best_params)\n",
        "\n",
        "# Evaluar el mejor modelo en el conjunto de prueba (usando X_test escalado)\n",
        "y_pred = best_model.predict(X_test_scaled)\n",
        "\n",
        "# Crear un DataFrame con los resultados de la predicción\n",
        "y_pred_df = pd.DataFrame(y_pred, columns=['prediction'])\n",
        "\n",
        "print(\"Precisión del modelo:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Reporte de clasificación:\\n\", classification_report(y_test, y_pred))\n",
        "print(\"Precisión del modelo:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Recall del modelo:\", recall_score(y_test, y_pred))\n",
        "print(\"F1-Score del modelo:\", f1_score(y_test, y_pred))\n",
        "\n",
        "# Evaluar el modelo en el conjunto de validación\n",
        "y_val_pred = best_model.predict(X_val_scaled)\n",
        "print(\"Precisión del modelo en validación:\", accuracy_score(y_val, y_val_pred))\n",
        "print(\"Recall del modelo en validación:\", recall_score(y_val, y_val_pred))\n",
        "print(\"F1-Score del modelo en validación:\", f1_score(y_val, y_val_pred))\n",
        "print(\"Reporte de clasificación en validación:\\n\", classification_report(y_val, y_val_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G6VFDi0FZxBo",
        "outputId": "2242ed1b-8fcb-43cd-fdb3-c3396fe2b0ed"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mejores parámetros: {'C': 0.01, 'penalty': 'l1', 'solver': 'liblinear'}\n",
            "Precisión del modelo: 0.9544895448954489\n",
            "Reporte de clasificación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.98      0.93      0.96       848\n",
            "        True       0.93      0.98      0.95       778\n",
            "\n",
            "    accuracy                           0.95      1626\n",
            "   macro avg       0.95      0.96      0.95      1626\n",
            "weighted avg       0.96      0.95      0.95      1626\n",
            "\n",
            "Precisión del modelo: 0.9544895448954489\n",
            "Recall del modelo: 0.9768637532133676\n",
            "F1-Score del modelo: 0.9535759096612295\n",
            "Precisión del modelo en validación: 0.937192118226601\n",
            "Recall del modelo en validación: 0.9702233250620348\n",
            "F1-Score del modelo en validación: 0.9387755102040817\n",
            "Reporte de clasificación en validación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.97      0.90      0.94       409\n",
            "        True       0.91      0.97      0.94       403\n",
            "\n",
            "    accuracy                           0.94       812\n",
            "   macro avg       0.94      0.94      0.94       812\n",
            "weighted avg       0.94      0.94      0.94       812\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Métricas Usando Hiperparámetros Optimizados**"
      ],
      "metadata": {
        "id": "OSTh8okp1XQC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Naive Bayes Optimizado\n",
        "\n",
        "# Definir los mejores parámetros\n",
        "#best_params = {'alpha': 10}  #RSCV\n",
        "best_params = {'alpha': 0.1}  #GridSearchCV\n",
        "\n",
        "# Crear el modelo de Naive Bayes\n",
        "model = CategoricalNB(**best_params)\n",
        "\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "y_pred = model.predict(X_train)\n",
        "print(\"Precisión del modelo en Entr:\", accuracy_score(y_train, y_pred))\n",
        "print(\"Recall del modelo: en Entr\", recall_score(y_train, y_pred))\n",
        "print(\"F1-Score del modelo en Entr:\", f1_score(y_train, y_pred))\n",
        "print(\"Reporte de clasificación en Entr:\\n\", classification_report(y_train, y_pred))\n",
        "\n",
        "# Hacer predicciones y evaluar el modelo\n",
        "y_pred = model.predict(X_test)\n",
        "print(\"Precisión del modelo:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Recall del modelo:\", recall_score(y_test, y_pred))\n",
        "print(\"F1-Score del modelo:\", f1_score(y_test, y_pred))\n",
        "print(\"Reporte de clasificación:\\n\", classification_report(y_test, y_pred))\n",
        "\n",
        "# Evaluar el modelo en el conjunto de validación\n",
        "y_val_pred = model.predict(X_val)\n",
        "print(\"Precisión del modelo en validación:\", accuracy_score(y_val, y_val_pred))\n",
        "print(\"Recall del modelo en validación:\", recall_score(y_val, y_val_pred))\n",
        "print(\"F1-Score del modelo en validación:\", f1_score(y_val, y_val_pred))\n",
        "print(\"Reporte de clasificación en validación:\\n\", classification_report(y_val, y_val_pred))\n",
        "\n",
        "print()\n",
        "print(\"Parametros Usados\")\n",
        "print(model.get_params())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bo9rt3FLQ_GZ",
        "outputId": "b92de7ad-5f5c-45b5-f7a5-9f2ad1969840"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo en Entr: 0.9104818853323954\n",
            "Recall del modelo: en Entr 0.8665447897623401\n",
            "F1-Score del modelo en Entr: 0.9030291484092209\n",
            "Reporte de clasificación en Entr:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.88      0.95      0.92      2951\n",
            "        True       0.94      0.87      0.90      2735\n",
            "\n",
            "    accuracy                           0.91      5686\n",
            "   macro avg       0.91      0.91      0.91      5686\n",
            "weighted avg       0.91      0.91      0.91      5686\n",
            "\n",
            "Precisión del modelo: 0.8985239852398524\n",
            "Recall del modelo: 0.8573264781491002\n",
            "F1-Score del modelo: 0.8899266177451634\n",
            "Reporte de clasificación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.88      0.94      0.91       848\n",
            "        True       0.93      0.86      0.89       778\n",
            "\n",
            "    accuracy                           0.90      1626\n",
            "   macro avg       0.90      0.90      0.90      1626\n",
            "weighted avg       0.90      0.90      0.90      1626\n",
            "\n",
            "Precisión del modelo en validación: 0.9039408866995073\n",
            "Recall del modelo en validación: 0.8511166253101737\n",
            "F1-Score del modelo en validación: 0.8979057591623038\n",
            "Reporte de clasificación en validación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.87      0.96      0.91       409\n",
            "        True       0.95      0.85      0.90       403\n",
            "\n",
            "    accuracy                           0.90       812\n",
            "   macro avg       0.91      0.90      0.90       812\n",
            "weighted avg       0.91      0.90      0.90       812\n",
            "\n",
            "\n",
            "Parametros Usados\n",
            "{'alpha': 0.1, 'class_prior': None, 'fit_prior': True, 'force_alpha': 'warn', 'min_categories': None}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Random Forest Optimizado\n",
        "\n",
        "# Definir los mejores parámetros\n",
        "best_params_rf = {\n",
        "    'n_estimators': 409,\n",
        "    'max_depth': 14,\n",
        "    'min_samples_split': 7,\n",
        "    'min_samples_leaf': 2\n",
        "}\n",
        "\n",
        "#GridSearchCV\n",
        "#best_params_rf = { 'n_estimators': 50, 'max_depth': None , 'min_samples_split': 2,'min_samples_leaf': 1 }\n",
        "\n",
        "# Crear el modelo de Random Forest\n",
        "model = RandomForestClassifier(**best_params_rf, random_state=42)\n",
        "\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "y_pred = model.predict(X_train)\n",
        "print(\"Precisión del modelo en Entr:\", accuracy_score(y_train, y_pred))\n",
        "print(\"Recall del modelo: en Entr\", recall_score(y_train, y_pred))\n",
        "print(\"F1-Score del modelo en Entr:\", f1_score(y_train, y_pred))\n",
        "print(\"Reporte de clasificación en Entr:\\n\", classification_report(y_train, y_pred))\n",
        "\n",
        "# Hacer predicciones y evaluar el modelo\n",
        "y_pred = model.predict(X_test)\n",
        "print(\"Precisión del modelo:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Recall del modelo:\", recall_score(y_test, y_pred))\n",
        "print(\"F1-Score del modelo:\", f1_score(y_test, y_pred))\n",
        "print(\"Reporte de clasificación:\\n\", classification_report(y_test, y_pred))\n",
        "\n",
        "# Evaluar el modelo en el conjunto de validación\n",
        "y_val_pred = model.predict(X_val)\n",
        "print(\"Precisión del modelo en validación:\", accuracy_score(y_val, y_val_pred))\n",
        "print(\"Recall del modelo en validación:\", recall_score(y_val, y_val_pred))\n",
        "print(\"F1-Score del modelo en validación:\", f1_score(y_val, y_val_pred))\n",
        "print(\"Reporte de clasificación en validación:\\n\", classification_report(y_val, y_val_pred))\n",
        "\n",
        "print()\n",
        "print(\"Parametros Usados\")\n",
        "print(model.get_params())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "urcUGFKeSRPG",
        "outputId": "85021d5d-a0d7-44d1-e792-d3fc2f0ddc67"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo en Entr: 0.9523390784382695\n",
            "Recall del modelo: en Entr 0.9714808043875686\n",
            "F1-Score del modelo en Entr: 0.9514771709937332\n",
            "Reporte de clasificación en Entr:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.97      0.93      0.95      2951\n",
            "        True       0.93      0.97      0.95      2735\n",
            "\n",
            "    accuracy                           0.95      5686\n",
            "   macro avg       0.95      0.95      0.95      5686\n",
            "weighted avg       0.95      0.95      0.95      5686\n",
            "\n",
            "Precisión del modelo: 0.9544895448954489\n",
            "Recall del modelo: 0.9768637532133676\n",
            "F1-Score del modelo: 0.9535759096612295\n",
            "Reporte de clasificación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.98      0.93      0.96       848\n",
            "        True       0.93      0.98      0.95       778\n",
            "\n",
            "    accuracy                           0.95      1626\n",
            "   macro avg       0.95      0.96      0.95      1626\n",
            "weighted avg       0.96      0.95      0.95      1626\n",
            "\n",
            "Precisión del modelo en validación: 0.937192118226601\n",
            "Recall del modelo en validación: 0.9702233250620348\n",
            "F1-Score del modelo en validación: 0.9387755102040817\n",
            "Reporte de clasificación en validación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.97      0.90      0.94       409\n",
            "        True       0.91      0.97      0.94       403\n",
            "\n",
            "    accuracy                           0.94       812\n",
            "   macro avg       0.94      0.94      0.94       812\n",
            "weighted avg       0.94      0.94      0.94       812\n",
            "\n",
            "\n",
            "Parametros Usados\n",
            "{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 14, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 2, 'min_samples_split': 7, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 409, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title KNN Optimizado\n",
        "\n",
        "# Definir los mejores parámetros\n",
        "best_params_knn = {\n",
        "    'n_neighbors': 23,\n",
        "    'weights': 'uniform',\n",
        "    'p': 1\n",
        "}\n",
        "#GridSCV (edit, encontraron los mismos parametros)\n",
        "#best_params_knn = {    'n_neighbors': 3,    'weights': 'uniform',    'p': 1}\n",
        "# Crear el modelo de KNN\n",
        "model = KNeighborsClassifier(**best_params_knn)\n",
        "\n",
        "model.fit(X_train_scaled, y_train)\n",
        "\n",
        "\n",
        "y_pred = model.predict(X_train_scaled)\n",
        "print(\"Precisión del modelo en Entr:\", accuracy_score(y_train, y_pred))\n",
        "print(\"Recall del modelo: en Entr\", recall_score(y_train, y_pred))\n",
        "print(\"F1-Score del modelo en Entr:\", f1_score(y_train, y_pred))\n",
        "print(\"Reporte de clasificación en Entr:\\n\", classification_report(y_train, y_pred))\n",
        "\n",
        "# Hacer predicciones y evaluar el modelo\n",
        "y_pred = model.predict(X_test_scaled)\n",
        "print(\"Precisión del modelo:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Recall del modelo:\", recall_score(y_test, y_pred))\n",
        "print(\"F1-Score del modelo:\", f1_score(y_test, y_pred))\n",
        "print(\"Reporte de clasificación:\\n\", classification_report(y_test, y_pred))\n",
        "\n",
        "# Evaluar el modelo en el conjunto de validación\n",
        "y_val_pred = model.predict(X_val_scaled)\n",
        "print(\"Precisión del modelo en validación:\", accuracy_score(y_val, y_val_pred))\n",
        "print(\"Recall del modelo en validación:\", recall_score(y_val, y_val_pred))\n",
        "print(\"F1-Score del modelo en validación:\", f1_score(y_val, y_val_pred))\n",
        "print(\"Reporte de clasificación en validación:\\n\", classification_report(y_val, y_val_pred))\n",
        "\n",
        "print()\n",
        "print(\"Parametros Usados\")\n",
        "print(model.get_params())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ui6PHI54SWT-",
        "outputId": "1f036b0f-018c-4795-81f8-133f87242869"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo en Entr: 0.9505803728455856\n",
            "Recall del modelo: en Entr 0.9191956124314442\n",
            "F1-Score del modelo en Entr: 0.9470710114899228\n",
            "Reporte de clasificación en Entr:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.93      0.98      0.95      2951\n",
            "        True       0.98      0.92      0.95      2735\n",
            "\n",
            "    accuracy                           0.95      5686\n",
            "   macro avg       0.95      0.95      0.95      5686\n",
            "weighted avg       0.95      0.95      0.95      5686\n",
            "\n",
            "Precisión del modelo: 0.9526445264452644\n",
            "Recall del modelo: 0.9331619537275064\n",
            "F1-Score del modelo: 0.9496402877697843\n",
            "Reporte de clasificación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.94      0.97      0.96       848\n",
            "        True       0.97      0.93      0.95       778\n",
            "\n",
            "    accuracy                           0.95      1626\n",
            "   macro avg       0.95      0.95      0.95      1626\n",
            "weighted avg       0.95      0.95      0.95      1626\n",
            "\n",
            "Precisión del modelo en validación: 0.9433497536945813\n",
            "Recall del modelo en validación: 0.913151364764268\n",
            "F1-Score del modelo en validación: 0.9411764705882352\n",
            "Reporte de clasificación en validación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.92      0.97      0.95       409\n",
            "        True       0.97      0.91      0.94       403\n",
            "\n",
            "    accuracy                           0.94       812\n",
            "   macro avg       0.95      0.94      0.94       812\n",
            "weighted avg       0.94      0.94      0.94       812\n",
            "\n",
            "\n",
            "Parametros Usados\n",
            "{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': None, 'n_neighbors': 23, 'p': 1, 'weights': 'uniform'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Regresión Logística Optimizada\n",
        "\n",
        "# Definir los mejores parámetros\n",
        "#best_params_lr = {\n",
        " #   'C': 37.46026648354777,\n",
        "  #  'penalty': 'l2',\n",
        "   # 'solver': 'liblinear'\n",
        "#}  # Reemplaza con los parámetros que hayas optimizado\n",
        "\n",
        "#GridSCV\n",
        "best_params_lr = {    'C': 0.01,    'penalty': 'l1',    'solver': 'saga'}\n",
        "\n",
        "\n",
        "# Crear el modelo de Regresión Logística\n",
        "model = LogisticRegression(**best_params_lr, random_state=42)\n",
        "\n",
        "model.fit(X_train_scaled, y_train)\n",
        "\n",
        "\n",
        "y_pred = model.predict(X_train_scaled)\n",
        "print(\"Precisión del modelo en Entr:\", accuracy_score(y_train, y_pred))\n",
        "print(\"Recall del modelo: en Entr\", recall_score(y_train, y_pred))\n",
        "print(\"F1-Score del modelo en Entr:\", f1_score(y_train, y_pred))\n",
        "print(\"Reporte de clasificación en Entr:\\n\", classification_report(y_train, y_pred))\n",
        "\n",
        "# Hacer predicciones y evaluar el modelo\n",
        "y_pred = model.predict(X_test_scaled)\n",
        "print(\"Precisión del modelo:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Recall del modelo:\", recall_score(y_test, y_pred))\n",
        "print(\"F1-Score del modelo:\", f1_score(y_test, y_pred))\n",
        "print(\"Reporte de clasificación:\\n\", classification_report(y_test, y_pred))\n",
        "\n",
        "# Evaluar el modelo en el conjunto de validación\n",
        "y_val_pred = model.predict(X_val_scaled)\n",
        "print(\"Precisión del modelo en validación:\", accuracy_score(y_val, y_val_pred))\n",
        "print(\"Recall del modelo en validación:\", recall_score(y_val, y_val_pred))\n",
        "print(\"F1-Score del modelo en validación:\", f1_score(y_val, y_val_pred))\n",
        "print(\"Reporte de clasificación en validación:\\n\", classification_report(y_val, y_val_pred))\n",
        "\n",
        "print()\n",
        "print(\"Parametros Usados\")\n",
        "print(model.get_params())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gc-mclNsSgIr",
        "outputId": "d314ca19-60dc-4f3c-ce21-d07ef1818a15"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo en Entr: 0.9523390784382695\n",
            "Recall del modelo: en Entr 0.9714808043875686\n",
            "F1-Score del modelo en Entr: 0.9514771709937332\n",
            "Reporte de clasificación en Entr:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.97      0.93      0.95      2951\n",
            "        True       0.93      0.97      0.95      2735\n",
            "\n",
            "    accuracy                           0.95      5686\n",
            "   macro avg       0.95      0.95      0.95      5686\n",
            "weighted avg       0.95      0.95      0.95      5686\n",
            "\n",
            "Precisión del modelo: 0.9544895448954489\n",
            "Recall del modelo: 0.9768637532133676\n",
            "F1-Score del modelo: 0.9535759096612295\n",
            "Reporte de clasificación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.98      0.93      0.96       848\n",
            "        True       0.93      0.98      0.95       778\n",
            "\n",
            "    accuracy                           0.95      1626\n",
            "   macro avg       0.95      0.96      0.95      1626\n",
            "weighted avg       0.96      0.95      0.95      1626\n",
            "\n",
            "Precisión del modelo en validación: 0.937192118226601\n",
            "Recall del modelo en validación: 0.9702233250620348\n",
            "F1-Score del modelo en validación: 0.9387755102040817\n",
            "Reporte de clasificación en validación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "       False       0.97      0.90      0.94       409\n",
            "        True       0.91      0.97      0.94       403\n",
            "\n",
            "    accuracy                           0.94       812\n",
            "   macro avg       0.94      0.94      0.94       812\n",
            "weighted avg       0.94      0.94      0.94       812\n",
            "\n",
            "\n",
            "Parametros Usados\n",
            "{'C': 0.01, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l1', 'random_state': 42, 'solver': 'saga', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Validación Cruzada Estratificada: StratifiedKFold**"
      ],
      "metadata": {
        "id": "WFFdqD0YNwaR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Validación Cruzadad Estratificada: Naive Bayes opt vs normal\n",
        "# Definir los mejores parámetros\n",
        "\n",
        "#best_params = {'alpha': 10}  # RSCV\n",
        "best_params = {'alpha': 0.1}  # GSCV\n",
        "\n",
        "# Crear el modelo de Naive Bayes\n",
        "model_nb_opt = CategoricalNB(**best_params)\n",
        "\n",
        "# Validación cruzada estratificada\n",
        "skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
        "cv_results_nb_opt = cross_validate(model_nb_opt, X_train, y_train, cv=skf, scoring=['accuracy', 'recall', 'f1'], return_train_score=False)\n",
        "\n",
        "print(\"***************************OPTIMIZADO***************************\")\n",
        "print(\"Puntuaciones de validación cruzada:\")\n",
        "print(\"Precisión:\", cv_results_nb_opt['test_accuracy'])\n",
        "print(\"Recall:\", cv_results_nb_opt['test_recall'])\n",
        "print(\"F1 Score:\", cv_results_nb_opt['test_f1'])\n",
        "print(\"\")\n",
        "print(\"Precisión media de validación cruzada:\", cv_results_nb_opt['test_accuracy'].mean())\n",
        "print(\"Recall medio de validación cruzada:\", cv_results_nb_opt['test_recall'].mean())\n",
        "print(\"F1 Score medio de validación cruzada:\", cv_results_nb_opt['test_f1'].mean())\n",
        "print(\"\")\n",
        "print(model_nb_opt.get_params())\n",
        "print(\"\")\n",
        "\n",
        "print(\"***************************SIN OPTIMIZAR***************************\")\n",
        "# Crear el modelo de Naive Bayes\n",
        "model_nb = CategoricalNB()\n",
        "\n",
        "# Validación cruzada estratificada\n",
        "cv_results_nb = cross_validate(model_nb, X_train, y_train, cv=skf, scoring=['accuracy', 'recall', 'f1'], return_train_score=False)\n",
        "\n",
        "print(\"Puntuaciones de validación cruzada:\")\n",
        "print(\"Precisión:\", cv_results_nb['test_accuracy'])\n",
        "print(\"Recall:\", cv_results_nb['test_recall'])\n",
        "print(\"F1 Score:\", cv_results_nb['test_f1'])\n",
        "print(\"\")\n",
        "print(\"Precisión media de validación cruzada:\", cv_results_nb['test_accuracy'].mean())\n",
        "print(\"Recall medio de validación cruzada:\", cv_results_nb['test_recall'].mean())\n",
        "print(\"F1 Score medio de validación cruzada:\", cv_results_nb['test_f1'].mean())\n",
        "print(\"\")\n",
        "print(model_nb.get_params())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "13aDeQFJC9GX",
        "outputId": "845838da-74b0-47ca-908e-9e9b16610318"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "***************************OPTIMIZADO***************************\n",
            "Puntuaciones de validación cruzada:\n",
            "Precisión: [0.91036907 0.91388401 0.89279438 0.90509666 0.90685413 0.91036907\n",
            " 0.9084507  0.90316901 0.92605634 0.92077465]\n",
            "Recall: [0.87591241 0.87956204 0.88686131 0.84306569 0.92335766 0.91208791\n",
            " 0.92673993 0.86446886 0.93040293 0.93406593]\n",
            "F1 Score: [0.9039548  0.90772128 0.88848263 0.89534884 0.90518784 0.90710383\n",
            " 0.90681004 0.89563567 0.92363636 0.91891892]\n",
            "\n",
            "Precisión media de validación cruzada: 0.909781801529741\n",
            "Recall medio de validación cruzada: 0.8976524691853159\n",
            "F1 Score medio de validación cruzada: 0.9052800205191829\n",
            "\n",
            "{'alpha': 0.1, 'class_prior': None, 'fit_prior': True, 'force_alpha': 'warn', 'min_categories': None}\n",
            "\n",
            "***************************SIN OPTIMIZAR***************************\n",
            "Puntuaciones de validación cruzada:\n",
            "Precisión: [0.91036907 0.91388401 0.90158172 0.90509666 0.90685413 0.91036907\n",
            " 0.9084507  0.90316901 0.92605634 0.92077465]\n",
            "Recall: [0.87591241 0.87956204 0.85036496 0.84306569 0.92335766 0.91208791\n",
            " 0.92673993 0.86446886 0.93040293 0.93406593]\n",
            "F1 Score: [0.9039548  0.90772128 0.89272031 0.89534884 0.90518784 0.90710383\n",
            " 0.90681004 0.89563567 0.92363636 0.91891892]\n",
            "\n",
            "Precisión media de validación cruzada: 0.9106605361518849\n",
            "Recall medio de validación cruzada: 0.8940028341488195\n",
            "F1 Score medio de validación cruzada: 0.9057037879164106\n",
            "\n",
            "{'alpha': 1.0, 'class_prior': None, 'fit_prior': True, 'force_alpha': 'warn', 'min_categories': None}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Validación Cruzadad Estratificada: Random Forest opt vs normal\n",
        "# Definir los mejores parámetros\n",
        "#best_params_rf = {'n_estimators': 409, 'max_depth': 14, 'min_samples_split': 7, 'min_samples_leaf': 2, 'random_state': 42}\n",
        "best_params_rf = {'n_estimators': 50, 'max_depth': None, 'min_samples_split': 2, 'min_samples_leaf': 1, 'random_state': 42}\n",
        "\n",
        "\n",
        "# Crear el modelo de Random Forest\n",
        "model_rf_opt = RandomForestClassifier(**best_params_rf)\n",
        "\n",
        "# Validación cruzada estratificada\n",
        "skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
        "cv_results_rf_opt = cross_validate(model_rf_opt, X_train, y_train, cv=skf, scoring=['accuracy', 'recall', 'f1'], return_train_score=False)\n",
        "\n",
        "print(\"***************************OPTIMIZADO***************************\")\n",
        "print(\"Puntuaciones de validación cruzada:\")\n",
        "print(\"Precisión:\", cv_results_rf_opt['test_accuracy'])\n",
        "print(\"Recall:\", cv_results_rf_opt['test_recall'])\n",
        "print(\"F1 Score:\", cv_results_rf_opt['test_f1'])\n",
        "print(\"\")\n",
        "print(\"Precisión media de validación cruzada:\", cv_results_rf_opt['test_accuracy'].mean())\n",
        "print(\"Recall medio de validación cruzada:\", cv_results_rf_opt['test_recall'].mean())\n",
        "print(\"F1 Score medio de validación cruzada:\", cv_results_rf_opt['test_f1'].mean())\n",
        "print(\"\")\n",
        "print(model_rf_opt.get_params())\n",
        "print(\"\")\n",
        "\n",
        "print(\"***************************SIN OPTIMIZAR***************************\")\n",
        "# Crear el modelo de Random Forest\n",
        "model_rf = RandomForestClassifier(random_state=42)\n",
        "\n",
        "# Validación cruzada estratificada\n",
        "cv_results_rf = cross_validate(model_rf, X_train, y_train, cv=skf, scoring=['accuracy', 'recall', 'f1'], return_train_score=False)\n",
        "\n",
        "print(\"Puntuaciones de validación cruzada:\")\n",
        "print(\"Precisión:\", cv_results_rf['test_accuracy'])\n",
        "print(\"Recall:\", cv_results_rf['test_recall'])\n",
        "print(\"F1 Score:\", cv_results_rf['test_f1'])\n",
        "print(\"\")\n",
        "print(\"Precisión media de validación cruzada:\", cv_results_rf['test_accuracy'].mean())\n",
        "print(\"Recall medio de validación cruzada:\", cv_results_rf['test_recall'].mean())\n",
        "print(\"F1 Score medio de validación cruzada:\", cv_results_rf['test_f1'].mean())\n",
        "print(\"\")\n",
        "print(model_rf.get_params())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wTdz1WhBOCqO",
        "outputId": "11001a2e-75a6-4cf9-8196-09b500f3ea07"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "***************************OPTIMIZADO***************************\n",
            "Puntuaciones de validación cruzada:\n",
            "Precisión: [0.95957821 0.95957821 0.94551845 0.95957821 0.94727592 0.94903339\n",
            " 0.94014085 0.96126761 0.9471831  0.95422535]\n",
            "Recall: [0.97810219 0.97080292 0.95620438 0.97810219 0.97445255 0.96336996\n",
            " 0.96703297 0.97069597 0.97435897 0.98168498]\n",
            "F1 Score: [0.9588551  0.95855856 0.94414414 0.9588551  0.94680851 0.94774775\n",
            " 0.93950178 0.96014493 0.94661922 0.95373665]\n",
            "\n",
            "Precisión media de validación cruzada: 0.9523379291566624\n",
            "Recall medio de validación cruzada: 0.9714807090719499\n",
            "F1 Score medio de validación cruzada: 0.9514971736650496\n",
            "\n",
            "{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}\n",
            "\n",
            "***************************SIN OPTIMIZAR***************************\n",
            "Puntuaciones de validación cruzada:\n",
            "Precisión: [0.95957821 0.95957821 0.94551845 0.95957821 0.94727592 0.94903339\n",
            " 0.94014085 0.96126761 0.9471831  0.95422535]\n",
            "Recall: [0.97810219 0.97080292 0.95620438 0.97810219 0.97445255 0.96336996\n",
            " 0.96703297 0.97069597 0.97435897 0.98168498]\n",
            "F1 Score: [0.9588551  0.95855856 0.94414414 0.9588551  0.94680851 0.94774775\n",
            " 0.93950178 0.96014493 0.94661922 0.95373665]\n",
            "\n",
            "Precisión media de validación cruzada: 0.9523379291566624\n",
            "Recall medio de validación cruzada: 0.9714807090719499\n",
            "F1 Score medio de validación cruzada: 0.9514971736650496\n",
            "\n",
            "{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 42, 'verbose': 0, 'warm_start': False}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Validación Cruzada Estratificada: Regresión Logística opt vs normal\n",
        "# Definir los mejores parámetros\n",
        "#best_params_lr = {'C': 37.46026648354777, 'penalty': 'l2', 'solver': 'liblinear', 'random_state': 42}\n",
        "best_params_lr = {'C': 0.01, 'penalty': 'l1', 'solver': 'liblinear', 'random_state': 42}\n",
        "\n",
        "\n",
        "# Crear el modelo de Regresión Logística\n",
        "model_lr_opt = LogisticRegression(**best_params_lr)\n",
        "\n",
        "# Validación cruzada estratificada\n",
        "skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
        "cv_results_lr_opt = cross_validate(model_lr_opt, X_train_scaled, y_train, cv=skf, scoring=['accuracy', 'recall', 'f1'], return_train_score=False)\n",
        "\n",
        "print(\"***************************OPTIMIZADO***************************\")\n",
        "print(\"Puntuaciones de validación cruzada:\")\n",
        "print(\"Precisión:\", cv_results_lr_opt['test_accuracy'])\n",
        "print(\"Recall:\", cv_results_lr_opt['test_recall'])\n",
        "print(\"F1 Score:\", cv_results_lr_opt['test_f1'])\n",
        "print(\"\")\n",
        "print(\"Precisión media de validación cruzada:\", cv_results_lr_opt['test_accuracy'].mean())\n",
        "print(\"Recall medio de validación cruzada:\", cv_results_lr_opt['test_recall'].mean())\n",
        "print(\"F1 Score medio de validación cruzada:\", cv_results_lr_opt['test_f1'].mean())\n",
        "print(\"\")\n",
        "print(model_lr_opt.get_params())\n",
        "print(\"\")\n",
        "\n",
        "print(\"***************************SIN OPTIMIZAR***************************\")\n",
        "# Crear el modelo de Regresión Logística\n",
        "model_lr = LogisticRegression()\n",
        "\n",
        "# Validación cruzada estratificada\n",
        "cv_results_lr = cross_validate(model_lr, X_train_scaled, y_train, cv=skf, scoring=['accuracy', 'recall', 'f1'], return_train_score=False)\n",
        "\n",
        "print(\"Puntuaciones de validación cruzada:\")\n",
        "print(\"Precisión:\", cv_results_lr['test_accuracy'])\n",
        "print(\"Recall:\", cv_results_lr['test_recall'])\n",
        "print(\"F1 Score:\", cv_results_lr['test_f1'])\n",
        "print(\"\")\n",
        "print(\"Precisión media de validación cruzada:\", cv_results_lr['test_accuracy'].mean())\n",
        "print(\"Recall medio de validación cruzada:\", cv_results_lr['test_recall'].mean())\n",
        "print(\"F1 Score medio de validación cruzada:\", cv_results_lr['test_f1'].mean())\n",
        "print(\"\")\n",
        "print(model_lr.get_params())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DhGQ2VR4O1_9",
        "outputId": "cd5cf693-75bc-41e1-cef6-11df758d0c14"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "***************************OPTIMIZADO***************************\n",
            "Puntuaciones de validación cruzada:\n",
            "Precisión: [0.95957821 0.95957821 0.94551845 0.95957821 0.94727592 0.94903339\n",
            " 0.94014085 0.96126761 0.9471831  0.95422535]\n",
            "Recall: [0.97810219 0.97080292 0.95620438 0.97810219 0.97445255 0.96336996\n",
            " 0.96703297 0.97069597 0.97435897 0.98168498]\n",
            "F1 Score: [0.9588551  0.95855856 0.94414414 0.9588551  0.94680851 0.94774775\n",
            " 0.93950178 0.96014493 0.94661922 0.95373665]\n",
            "\n",
            "Precisión media de validación cruzada: 0.9523379291566624\n",
            "Recall medio de validación cruzada: 0.9714807090719499\n",
            "F1 Score medio de validación cruzada: 0.9514971736650496\n",
            "\n",
            "{'C': 0.01, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l1', 'random_state': 42, 'solver': 'liblinear', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}\n",
            "\n",
            "***************************SIN OPTIMIZAR***************************\n",
            "Puntuaciones de validación cruzada:\n",
            "Precisión: [0.95957821 0.95079086 0.94551845 0.94551845 0.94727592 0.94903339\n",
            " 0.94014085 0.96126761 0.9471831  0.95422535]\n",
            "Recall: [0.97810219 0.90875912 0.95620438 0.90145985 0.97445255 0.96336996\n",
            " 0.96703297 0.97069597 0.97435897 0.98168498]\n",
            "F1 Score: [0.9588551  0.94676806 0.94414414 0.94095238 0.94680851 0.94774775\n",
            " 0.93950178 0.96014493 0.94661922 0.95373665]\n",
            "\n",
            "Precisión media de validación cruzada: 0.9500532191390876\n",
            "Recall medio de validación cruzada: 0.9576120959332638\n",
            "F1 Score medio de validación cruzada: 0.9485278521490839\n",
            "\n",
            "{'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Validación Cruzada Estratificada: KNN opt vs normal\n",
        "# Definir los mejores parámetros\n",
        "\n",
        "best_params_knn = {'n_neighbors': 23, 'p': 1, 'weights': 'uniform'}\n",
        "\n",
        "#ambos algoritmos encontraron los mismos hiperparámetros\n",
        "\n",
        "\n",
        "# Crear el modelo de KNN\n",
        "model_knn_opt = KNeighborsClassifier(**best_params_knn)\n",
        "\n",
        "# Validación cruzada estratificada\n",
        "skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
        "cv_results_knn_opt = cross_validate(model_knn_opt, X_train_scaled, y_train, cv=skf, scoring=['accuracy', 'recall', 'f1'], return_train_score=False)\n",
        "\n",
        "print(\"***************************OPTIMIZADO***************************\")\n",
        "print(\"Puntuaciones de validación cruzada:\")\n",
        "print(\"Precisión:\", cv_results_knn_opt['test_accuracy'])\n",
        "print(\"Recall:\", cv_results_knn_opt['test_recall'])\n",
        "print(\"F1 Score:\", cv_results_knn_opt['test_f1'])\n",
        "print(\"\")\n",
        "print(\"Precisión media de validación cruzada:\", cv_results_knn_opt['test_accuracy'].mean())\n",
        "print(\"Recall medio de validación cruzada:\", cv_results_knn_opt['test_recall'].mean())\n",
        "print(\"F1 Score medio de validación cruzada:\", cv_results_knn_opt['test_f1'].mean())\n",
        "print(\"\")\n",
        "print(model_knn_opt.get_params())\n",
        "print(\"\")\n",
        "\n",
        "print(\"***************************SIN OPTIMIZAR***************************\")\n",
        "# Crear el modelo de KNN\n",
        "model_knn = KNeighborsClassifier()\n",
        "\n",
        "# Validación cruzada estratificada\n",
        "cv_results_knn = cross_validate(model_knn, X_train_scaled, y_train, cv=skf, scoring=['accuracy', 'recall', 'f1'], return_train_score=False)\n",
        "\n",
        "print(\"Puntuaciones de validación cruzada:\")\n",
        "print(\"Precisión:\", cv_results_knn['test_accuracy'])\n",
        "print(\"Recall:\", cv_results_knn['test_recall'])\n",
        "print(\"F1 Score:\", cv_results_knn['test_f1'])\n",
        "print(\"\")\n",
        "print(\"Precisión media de validación cruzada:\", cv_results_knn['test_accuracy'].mean())\n",
        "print(\"Recall medio de validación cruzada:\", cv_results_knn['test_recall'].mean())\n",
        "print(\"F1 Score medio de validación cruzada:\", cv_results_knn['test_f1'].mean())\n",
        "print(\"\")\n",
        "print(model_knn.get_params())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1FOXh_HYOXnY",
        "outputId": "98c9739f-1e0d-41e0-9e71-1b566c431ce4"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "***************************OPTIMIZADO***************************\n",
            "Puntuaciones de validación cruzada:\n",
            "Precisión: [0.95957821 0.95079086 0.9543058  0.94551845 0.94727592 0.94376098\n",
            " 0.94014085 0.96126761 0.9471831  0.95422535]\n",
            "Recall: [0.97810219 0.90875912 0.91970803 0.90145985 0.97445255 0.91575092\n",
            " 0.96703297 0.97069597 0.97435897 0.98168498]\n",
            "F1 Score: [0.9588551  0.94676806 0.9509434  0.94095238 0.94680851 0.93984962\n",
            " 0.93950178 0.96014493 0.94661922 0.95373665]\n",
            "\n",
            "Precisión media de validación cruzada: 0.9504047129879453\n",
            "Recall medio de validación cruzada: 0.9492005561348626\n",
            "F1 Score medio de validación cruzada: 0.9484179649885511\n",
            "\n",
            "{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': None, 'n_neighbors': 23, 'p': 1, 'weights': 'uniform'}\n",
            "\n",
            "***************************SIN OPTIMIZAR***************************\n",
            "Puntuaciones de validación cruzada:\n",
            "Precisión: [0.95079086 0.95079086 0.9543058  0.94551845 0.94727592 0.94376098\n",
            " 0.94014085 0.96126761 0.94014085 0.96126761]\n",
            "Recall: [0.97810219 0.90875912 0.91970803 0.90875912 0.97445255 0.91575092\n",
            " 0.96703297 0.93406593 0.91208791 0.94139194]\n",
            "F1 Score: [0.95035461 0.94676806 0.9509434  0.94139887 0.94680851 0.93984962\n",
            " 0.93950178 0.95864662 0.93609023 0.95895522]\n",
            "\n",
            "Precisión media de validación cruzada: 0.949525978365801\n",
            "Recall medio de validación cruzada: 0.9360110692227481\n",
            "F1 Score medio de validación cruzada: 0.9469316912820233\n",
            "\n",
            "{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': None, 'n_neighbors': 5, 'p': 2, 'weights': 'uniform'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "FFTH3o3iXD_6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title (REVISAR) Codificar la columna objetivo\n",
        "label_encoder = LabelEncoder()\n",
        "mushroom_data_cp=mushroom_data.copy()\n",
        "\n",
        "#Transorma las categorías de la columna objetivo en valores numéricos\n",
        "mushroom_data_cp['class'] = label_encoder.fit_transform(mushroom_data['class'])\n",
        "\n",
        "# Visualizar las primeras filas después de codificar la columna objetivo\n",
        "print(\"\\nDespués de codificar la columna objetivo:\")\n",
        "print(mushroom_data_cp.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "-17TMZZa7HrG",
        "outputId": "0917d87e-d06c-4c62-d7fb-75e3a40878b9"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Después de codificar la columna objetivo:\n",
            "   class cap-shape cap-surface cap-color  bruises     odor gill-attachment  \\\n",
            "0      1    convex      smooth     brown  bruises  pungent            free   \n",
            "1      0    convex      smooth    yellow  bruises   almond            free   \n",
            "2      0      bell      smooth     white  bruises    anise            free   \n",
            "3      1    convex       scaly     white  bruises  pungent            free   \n",
            "4      0    convex      smooth      gray       no     none            free   \n",
            "\n",
            "  gill-spacing gill-size gill-color  ... stalk-surface-above-ring  \\\n",
            "0        close    narrow      black  ...                   smooth   \n",
            "1        close     broad      black  ...                   smooth   \n",
            "2        close     broad      brown  ...                   smooth   \n",
            "3        close    narrow      brown  ...                   smooth   \n",
            "4      crowded     broad      black  ...                   smooth   \n",
            "\n",
            "  stalk-surface-below-ring stalk-color-above-ring stalk-color-below-ring  \\\n",
            "0                   smooth                  white                  white   \n",
            "1                   smooth                  white                  white   \n",
            "2                   smooth                  white                  white   \n",
            "3                   smooth                  white                  white   \n",
            "4                   smooth                  white                  white   \n",
            "\n",
            "  veil-color ring-number   ring-type spore-print-color population  habitat  \n",
            "0      white         one     pendant             black  scattered    urban  \n",
            "1      white         one     pendant             brown   numerous  grasses  \n",
            "2      white         one     pendant             brown   numerous  meadows  \n",
            "3      white         one     pendant             black  scattered    urban  \n",
            "4      white         one  evanescent             brown   abundant  grasses  \n",
            "\n",
            "[5 rows x 21 columns]\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
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      "codemirror_mode": {
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