Unidad_2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## **Librerías**\n"
      ],
      "metadata": {
        "id": "gB-TNGrXrWHc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "4MpjFoHLkdk-"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, classification_report, roc_curve, roc_auc_score\n",
        "from scipy.stats import spearmanr\n",
        "from scipy.stats import kendalltau\n",
        "\n",
        "# Librerías de clasificación\n",
        "import seaborn as sns\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.feature_selection import SelectKBest, f_classif\n",
        "from sklearn.feature_selection import chi2\n",
        "from sklearn.feature_selection import RFE\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "# Librerias para SVM\n",
        "from sklearn import svm\n",
        "\n",
        "# Librerías para Multi-Class\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "# Librerías Arboles de Decisión\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# Librerúas Sub-Muestreo\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "\n",
        "# Overfitting & Underfitting\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# Cross-Validation\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Genera datos de ejemplo Regresión logística y Correlación\n",
        "np.random.seed(0)\n",
        "X = np.random.rand(100, 2)\n",
        "y = np.random.choice([0, 1], size=100, p=[0.8, 0.2])\n",
        "\n",
        "X_fitt = np.sort(5 * np.random.rand(80, 1), axis=0)\n",
        "y_fitt = np.sin(X_fitt).ravel() + np.random.normal(0, 0.1, X_fitt.shape[0])\n",
        "\n",
        "# Genera datos de ejemplo para Selección de Características (100 muestras y 10 características)\n",
        "X_feat, y_feat = make_classification(n_samples=100, n_features=10, random_state=42, flip_y=False)\n",
        "X_feat = np.abs(X_feat) # Fuerza que no existan carácterísticas negativas"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Clasificación Simple**"
      ],
      "metadata": {
        "id": "u6lTm3D2rte9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Ny3PMKpymOjP",
        "outputId": "4ca2ea82-a7c3-44cc-9472-7e6edcaf99c8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Clasificación simple\n",
        "y = (X[:, 0] + X[:, 1] > 1).astype(int)\n",
        "\n",
        "# Visualiza los datos\n",
        "plt.scatter(X[y == 0][:, 0], X[y == 0][:, 1], color='red', marker='o', label='Clase 0')\n",
        "plt.scatter(X[y == 1][:, 0], X[y == 1][:, 1], color='blue', marker='x', label='Clase 1')\n",
        "plt.xlabel('Característica 1')\n",
        "plt.ylabel('Característica 2')\n",
        "plt.legend()\n",
        "plt.title('Conjunto de datos sintético')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Regresión Logística**"
      ],
      "metadata": {
        "id": "ZmAawuRmr8Zq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xIWELIq3mg9V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48afc17a-cb3e-4a68-ae73-2b218fdfee17"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo: 95.00%\n"
          ]
        }
      ],
      "source": [
        "# Implementa la regresión logística\n",
        "class LogisticRegression:\n",
        "    def __init__(self, learning_rate=0.01, num_iterations=1000):\n",
        "        self.learning_rate = learning_rate\n",
        "        self.num_iterations = num_iterations\n",
        "\n",
        "    def sigmoid(self, z):\n",
        "        return 1 / (1 + np.exp(-z))\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        m, n = X.shape\n",
        "        self.theta = np.zeros(n)\n",
        "        for _ in range(self.num_iterations):\n",
        "            z = np.dot(X, self.theta)\n",
        "            h = self.sigmoid(z)\n",
        "            gradient = np.dot(X.T, (h - y)) / m\n",
        "            self.theta -= self.learning_rate * gradient\n",
        "\n",
        "    def predict(self, X):\n",
        "        z = np.dot(X, self.theta)\n",
        "        return np.round(self.sigmoid(z))\n",
        "\n",
        "# Añade una columna de unos a X para el término de sesgo\n",
        "X = np.hstack((np.ones((X.shape[0], 1)), X))\n",
        "# print(X)\n",
        "\n",
        "# Divide los datos en entrenamiento y prueba\n",
        "X_train, X_test = X[:80], X[80:]\n",
        "# print(X_train)\n",
        "# print(X_test)\n",
        "y_train, y_test = y[:80], y[80:]\n",
        "# print(y_train)\n",
        "# print(y_test)\n",
        "\n",
        "# Crea y entrena el modelo de regresión logística\n",
        "model = LogisticRegression(learning_rate=0.1, num_iterations=1000)\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Calcula la precisión del modelo\n",
        "accuracy = np.mean(y_pred == y_test)\n",
        "print(\"Precisión del modelo: {:.2f}%\".format(accuracy * 100))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Correlación**\n",
        "### Correlación de Pearson:\n",
        "* También conocida como \"correlación lineal\" o \"coeficiente de correlación de Pearson\".\n",
        "* Mide la relación lineal entre dos variables continuas.\n",
        "* Supone que las variables siguen una [distribución normal](https://www.matematicasonline.es/BachilleratoCCSS/segundo/archivos/distribucion_normal/DISTRIBUCION%20NORMAL.htm) y que la relación entre ellas es lineal.\n",
        "* Se utiliza comúnmente para medir la relación entre dos variables cuantitativas, como edad y ingresos.\n",
        "* Los valores van de -1 a 1, donde 1 indica una correlación positiva perfecta, -1 indica una correlación negativa perfecta, y 0 indica ausencia de correlación.\n",
        "\n",
        "###Correlación de Spearman:\n",
        "\n",
        "* También conocida como \"correlación de rango de Spearman\".\n",
        "* Mide la relación monótona entre dos variables, es decir, si el aumento en una variable se asocia con el aumento o disminución en la otra.\n",
        "* No requiere que las variables sigan una distribución normal ni que la relación sea lineal.\n",
        "* Se utiliza cuando las variables son ordinales o no siguen una distribución normal.\n",
        "* Se calcula mediante la clasificación de los valores de las variables y la comparación de los rangos de los valores.\n",
        "\n",
        "###Correlación de Kendall:\n",
        "\n",
        "* También conocida como \"tau de Kendall\".\n",
        "* Mide la relación de concordancia o discordancia entre dos variables, es decir, si los pares de observaciones mantienen el mismo orden o no.\n",
        "* No asume una distribución normal ni una relación lineal y es robusta frente a valores atípicos.\n",
        "* A menudo se utiliza cuando se tienen datos ordinales o cuando la relación no es necesariamente lineal.\n",
        "* Se calcula contando los pares de datos que son concordantes y discordantes."
      ],
      "metadata": {
        "id": "XZKoYfTesLWg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calcularemos la **Correlación de Pearson** entre dos características presentes en la matriz **X. X[:, 0]** que representa la primera característica, y **X[:, 1]** que representa la segunda característica. Usaremos la función [np.corrcoef](https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html) que calcula la matriz de correlación entre estas dos características. Se utiliza comúnmente cuando se asume una relación lineal, como en el análisis de regresión, para evaluar la relación entre dos variables cuantitativas."
      ],
      "metadata": {
        "id": "6UnI-OKeshM2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "3JyfTKNRmRH2",
        "outputId": "f99086f1-5ed4-4c52-a6b8-88b69ff74e09"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlación de Pearson entre X[:, 0] y X[:, 1]: -0.06834832770642568\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Calcula la correlación de Pearson entre las dos características\n",
        "correlation = np.corrcoef(X[:, 0], X[:, 1])[0, 1]\n",
        "\n",
        "print(\"Correlación de Pearson entre X[:, 0] y X[:, 1]:\", correlation)\n",
        "# Anota el valor de correlación y el valor p en el gráfico\n",
        "# plt.annotate(f'Correlación_X = {correlation:.2f}', xy=(0.5, 0.9))\n",
        "\n",
        "# Grafica un scatter plot de las dos características\n",
        "plt.figure(figsize=(6, 6))\n",
        "plt.scatter(X[:, 0], X[:, 1], label=f'Correlación = {correlation:.2f}')\n",
        "plt.xlabel('Característica X[:, 0]')\n",
        "plt.ylabel('Característica X[:, 1]')\n",
        "plt.title('Correlación de Pearson')\n",
        "\n",
        "# Anota el valor de correlación y el valor p en el gráfico\n",
        "# plt.annotate(f'Correlación = {correlation:.2f}', xy=(0.5, 0.9))\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calcularemos la **Correlación de Spearman** entre dos características presentes en la matriz **X. X[:, 0]** que representa la primera característica, y **X[:, 1]** que representa la segunda característica. La función spearmanr devolverá dos valores: correlation_spearmanr, que es la correlación de Spearman, y p_value, que es el valor p asociado a la prueba de correlación de Spearman. Se utiliza cuando no se asume una relación lineal, como en la comparación de calificaciones de dos pruebas, para evaluar la relación entre variables ordinales o cuando se desea ser menos sensible a valores atípicos."
      ],
      "metadata": {
        "id": "S69Vw6vQt0zb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "-lD8gFhRnr_b",
        "outputId": "22a46265-b6ac-447a-d16c-a40c09832686"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlación de Searmanr entre X[:, 0] y X[:, 1]: -0.08118811881188119\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "correlation_spearmanr, p_value = spearmanr(X[:, 0], X[:, 1])\n",
        "\n",
        "print(\"Correlación de Searmanr entre X[:, 0] y X[:, 1]:\", correlation_spearmanr)\n",
        "\n",
        "# Grafica un scatter plot de las dos características\n",
        "plt.figure(figsize=(6, 6))\n",
        "plt.scatter(X[:, 0], X[:, 1])\n",
        "plt.xlabel('Característica X[:, 0]')\n",
        "plt.ylabel('Característica X[:, 1]')\n",
        "plt.title('Correlación de Spearman')\n",
        "\n",
        "# Anota el valor de correlación y el valor p en el gráfico\n",
        "# plt.annotate(f'Correlación = {correlation_spearmanr:.2f}', xy=(0.5, 0.9))\n",
        "# plt.annotate(f'Valor p = {p_value:.4f}', xy=(0.5, 0.85))\n",
        "\n",
        "plt.grid()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calcularemos la **Correlación de Kendalltau** entre dos características presentes en la matriz **X. X[:, 0]** que representa la primera característica, y **X[:, 1]** que representa la segunda característica. La función kendalltau devuelve dos valores: correlation_kendalltau, que es la correlación de Kendall Tau, y p_value, que es el valor p asociado a la prueba de correlación de Kendall. Se utiliza en situaciones similares a la de Spearman, pero es especialmente útil cuando se trabaja con datos ordinales o categóricos y cuando se desea una medida robusta ante valores atípicos."
      ],
      "metadata": {
        "id": "b-T44Kqbwytq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "MX3UkDfCnt-X",
        "outputId": "df3c8cd0-c512-4cfd-c7b9-0324ecdf7373"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlación Tau de Kendall = -0.05\n",
            "Valor p = 0.4602\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "correlation_kendalltau, p_value = kendalltau(X[:, 0], X[:, 1])\n",
        "# print(\"Correlación de Kendalltau entre X[:, 0] y X[:, 1]:\", correlation_kendalltau)\n",
        "\n",
        "# Grafica un scatter plot de las dos características\n",
        "# plt.figure(figsize=(6, 6))\n",
        "# plt.scatter(X[:, 0], X[:, 1])\n",
        "# plt.xlabel('Característica X[:, 0]')\n",
        "# plt.ylabel('Característica X[:, 1]')\n",
        "# plt.title('Correlación de Kendall Tau')\n",
        "\n",
        "# Crea un mapa de calor\n",
        "labels = ['X[:, 0]', 'X[:, 1]']\n",
        "correlation_matrix = np.array([[1, correlation_kendalltau],[correlation_kendalltau, 1]])\n",
        "\n",
        "# Configura el mapa de calor usando seaborn\n",
        "sns.set(font_scale=1.2)\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', xticklabels=labels, yticklabels=labels)\n",
        "\n",
        "# Mostrar el valor de correlación y el valor p\n",
        "print(f'Correlación Tau de Kendall = {correlation_kendalltau:.2f}')\n",
        "print(f'Valor p = {p_value:.4f}')\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aplicación de carácterísticas, mediante **SelectKBest** que se utiliza para seleccionar las mejores características de acuerdo con una métrica específica. En este caso, se está utilizando la métrica [chi-cuadrado](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html). **Chi-cuadrado** se utiliza para determinar si existe una relación estadística entre dos variables categóricas en un conjunto de datos. Esta métrica es útil para medir la dependencia entre dos variables y se utiliza comúnmente en el análisis de tablas de contingencia\n"
      ],
      "metadata": {
        "id": "aly4CnTM0Oei"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJPcWvQoqgsi",
        "outputId": "3739b9c7-fbe6-4abc-a42e-2cef223c025c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[4.76898369e-01 5.21303748e-01 1.25198379e-01 1.48449581e-01\n",
            "  6.11927193e-01]\n",
            " [4.51303037e-01 7.07505698e-01 7.31712082e-02 2.65687975e-01\n",
            "  3.97667346e-02]\n",
            " [5.21064876e-01 1.07961859e+00 1.16940705e+00 5.75787970e-01\n",
            "  8.13364259e-01]\n",
            " [1.51999486e+00 6.84501107e-01 5.54893707e-02 1.71958931e+00\n",
            "  3.70825001e-01]\n",
            " [9.44446256e-01 3.17218215e-01 8.49123046e-01 4.10049693e-01\n",
            "  7.86327962e-01]\n",
            " [6.58552967e-01 1.29802197e+00 3.01549448e-02 5.14233966e-01\n",
            "  2.69622405e+00]\n",
            " [3.09114445e-01 8.07648488e-01 5.77822712e-01 2.33466662e-01\n",
            "  1.44653470e+00]\n",
            " [5.64008535e-01 1.05188010e+00 3.21693913e-01 1.28455230e+00\n",
            "  2.49720039e+00]\n",
            " [1.74235620e+00 1.66069981e+00 4.69537098e-01 1.30324275e+00\n",
            "  1.15147873e-01]\n",
            " [4.26258731e-01 7.81198102e-01 1.94473774e+00 6.76908035e-01\n",
            "  1.49448454e+00]\n",
            " [1.33701560e-01 1.34671751e+00 1.43043530e-01 1.07774381e+00\n",
            "  6.93773153e-01]\n",
            " [2.22605681e-01 1.04797216e+00 1.09140294e-01 9.13079218e-01\n",
            "  2.84279671e-01]\n",
            " [2.65917224e+00 1.18894496e+00 7.65997056e-01 6.06319524e-01\n",
            "  7.73252977e-01]\n",
            " [2.24258934e-01 1.50699840e+00 3.00495195e-01 3.02249730e-01\n",
            "  6.76460732e-01]\n",
            " [8.59307670e-02 7.82601752e-02 3.65380582e-01 1.94292938e-01\n",
            "  3.95228983e-01]\n",
            " [1.56506538e-01 4.81481474e-01 5.11600970e-01 2.32181036e-01\n",
            "  1.54079701e+00]\n",
            " [1.09634685e+00 7.78547559e-02 8.50891944e-01 2.34215801e-01\n",
            "  3.82732430e-01]\n",
            " [3.17160626e-01 1.55224318e+00 3.30687508e-01 5.20040615e-01\n",
            "  2.86904488e-01]\n",
            " [6.43314465e-01 2.49458580e-01 1.32411247e-02 1.57062341e+00\n",
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            " [5.61330204e-02 1.22619619e+00 1.06126205e+00 1.38504274e-03\n",
            "  1.83339199e-01]\n",
            " [8.62789892e-03 1.96557401e-01 6.15748704e-01 5.27004208e-01\n",
            "  3.54757693e-01]\n",
            " [2.36417382e+00 2.67594746e-01 2.65030274e-01 2.03341817e-02\n",
            "  6.78025782e-01]\n",
            " [2.91837363e-01 2.22675101e-01 2.93988382e-01 7.61492212e-01\n",
            "  3.53431749e-01]\n",
            " [1.95180410e+00 1.11831192e+00 8.48214735e-01 6.59891730e-01\n",
            "  1.31054012e-01]\n",
            " [6.66383082e-01 1.65813068e+00 1.76399739e+00 4.60719787e-01\n",
            "  1.18164045e-01]\n",
            " [1.60183605e+00 5.10292740e-01 2.78561851e+00 8.87180942e-01\n",
            "  1.83925494e-01]\n",
            " [7.55395696e-01 3.96086585e-01 2.68913472e+00 8.67407411e-01\n",
            "  5.91402668e-01]\n",
            " [4.48165363e-01 1.26306835e+00 1.85583449e+00 6.49337928e-01\n",
            "  4.52489093e-01]\n",
            " [1.12859406e+00 9.94394391e-01 7.61804328e-01 4.96000946e-01\n",
            "  1.31913688e+00]\n",
            " [6.96326654e-01 2.11497013e-01 2.16052537e-01 2.90397101e-01\n",
            "  7.04921353e-01]\n",
            " [4.21714513e-02 7.30677753e-01 1.84696289e+00 2.86887192e-01\n",
            "  3.84879809e-01]\n",
            " [1.30106954e-01 5.82224591e-01 4.75683472e-01 9.39532294e-02\n",
            "  2.09460307e+00]\n",
            " [8.62596011e-01 1.02943883e+00 6.33420331e-01 2.65561909e+00\n",
            "  9.08763246e-01]\n",
            " [9.36742464e-01 8.88971358e-01 9.17515596e-01 1.41232771e+00\n",
            "  2.42117961e-01]\n",
            " [2.25556423e+00 8.80178912e-01 1.28252298e-01 1.02250684e+00\n",
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            " [7.25597378e-01 3.19328417e-01 1.55399781e+00 1.38336396e+00\n",
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            " [1.75498615e+00 9.88001942e-01 2.65199620e-01 1.32988422e-01\n",
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            " [1.56821116e-02 3.77088909e-03 2.67892177e-01 1.60928168e-01\n",
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            " [2.19541028e-01 3.04614305e+00 1.05512600e+00 1.08403662e+00\n",
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            " [2.74516358e-01 1.07305276e-01 1.91692582e+00 8.90915083e-01\n",
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            " [1.06850940e+00 4.50934462e-01 4.33105451e+00 4.53385804e-01\n",
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            " [9.38981573e-01 9.12783494e-01 1.19466313e+00 1.01702099e+00\n",
            "  2.19509556e-01]\n",
            " [5.45711974e-01 1.07919473e+00 3.06165066e-01 1.88458584e+00\n",
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            " [1.63928572e-01 2.55918467e+00 9.80739928e-01 9.63213559e-02\n",
            "  1.18137860e+00]\n",
            " [1.17915793e+00 5.81268477e-01 8.53410903e-02 1.30142807e+00\n",
            "  1.63263453e+00]\n",
            " [9.60504382e-01 1.81338429e-01 3.97369747e-01 7.93117363e-01\n",
            "  5.02816701e-01]\n",
            " [1.36453185e+00 3.49654457e-01 2.95958826e+00 6.89449185e-01\n",
            "  7.64143924e-01]\n",
            " [1.68823003e+00 4.05204080e-01 4.67783070e+00 1.12465983e-01\n",
            "  4.98052405e-01]\n",
            " [5.32702792e-01 9.49246474e-01 1.34986807e+00 1.09074973e+00\n",
            "  1.50239657e+00]\n",
            " [2.30391670e+00 1.30184623e+00 1.29370156e+00 1.06001582e+00\n",
            "  6.28087560e-01]\n",
            " [2.75670535e-01 2.51484415e-01 8.35101353e-01 7.09727966e-01\n",
            "  2.01640663e+00]\n",
            " [4.79655814e-01 5.21189312e-01 2.23460699e+00 6.20358298e-01\n",
            "  1.84306955e+00]\n",
            " [3.70704003e-01 4.49712100e-01 1.05004467e+00 9.45615796e-01\n",
            "  6.72756089e-02]\n",
            " [6.52408582e-01 2.77259276e+00 3.59865169e+00 3.90953375e-01\n",
            "  1.95591231e+00]\n",
            " [3.84645423e-01 1.20857365e+00 9.64902950e-01 4.43836093e-01\n",
            "  2.42019830e-01]\n",
            " [1.02993528e+00 5.53132064e-01 2.57374908e-01 3.49943365e-01\n",
            "  4.57039607e-02]\n",
            " [9.55945000e-01 3.79151736e-01 3.04373307e+00 3.45981776e-01\n",
            "  2.25930895e+00]\n",
            " [1.02893549e+00 9.96212640e-01 1.14462475e+00 7.22870076e-02\n",
            "  1.21793851e+00]\n",
            " [9.36445726e-01 1.09463837e+00 1.14032083e-01 3.50951769e-02\n",
            "  2.34821526e-01]\n",
            " [2.80441705e-01 3.94849514e-01 8.82342712e-01 9.93123611e-01\n",
            "  2.67733537e-01]\n",
            " [1.10540657e-01 1.16103939e-01 2.93668633e-01 1.02017271e+00\n",
            "  2.03068447e+00]\n",
            " [3.10886172e-01 1.88778597e-01 5.08589018e-01 9.74001663e-02\n",
            "  5.23891024e-01]\n",
            " [1.64813493e+00 1.65671510e+00 3.09590732e-01 1.64227755e-01\n",
            "  9.85510738e-01]\n",
            " [2.16949570e-01 7.84957521e-01 6.20716743e-01 4.45393251e-01\n",
            "  5.54309627e-01]\n",
            " [7.04700276e-01 3.12292251e-01 1.43683401e+00 9.43260725e-01\n",
            "  1.57667016e-01]\n",
            " [1.01697275e-01 2.46121252e-02 1.89634239e+00 1.92793845e-02\n",
            "  7.19983730e-01]\n",
            " [1.63242330e+00 1.79132755e+00 9.32664558e-01 6.71341546e-02\n",
            "  5.85865511e-02]\n",
            " [3.64693544e-01 1.21407740e+00 1.37977014e+00 1.56703855e-01\n",
            "  4.40922632e-01]\n",
            " [1.82425666e+00 5.55786964e-01 9.05415370e-01 3.03603904e-01\n",
            "  1.03493146e-02]\n",
            " [5.17519043e-01 2.20144129e+00 1.45899162e+00 9.78829859e-01\n",
            "  1.99300197e-01]\n",
            " [7.45864065e-02 1.31247037e+00 2.13703895e+00 1.07709907e+00\n",
            "  8.58972388e-01]\n",
            " [3.57680719e-02 2.83455451e+00 1.19266118e-01 2.38074535e+00\n",
            "  2.11679102e+00]\n",
            " [7.82629156e-01 1.15107468e-01 1.53297707e-01 1.10389299e-01\n",
            "  4.57415606e-01]\n",
            " [3.09012969e-01 3.97271814e-01 2.78637404e+00 1.67600381e+00\n",
            "  1.32880578e-01]]\n",
            "Características originales: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
            "Características seleccionadas: [0 2 3 4 5]\n"
          ]
        }
      ],
      "source": [
        "# Aplica SelectKBest con la métrica chi-cuadrado para seleccionar las mejores 5 características\n",
        "k_best = SelectKBest(score_func=chi2, k=5)\n",
        "X_new = k_best.fit_transform(X_feat, y_feat)\n",
        "# print(X_new)\n",
        "\n",
        "# Obtén las características seleccionadas\n",
        "selected_features = np.where(k_best.get_support())[0]\n",
        "# print(selected_features)\n",
        "\n",
        "print(\"Características originales:\", list(range(X_feat.shape[1])))\n",
        "print(\"Características seleccionadas:\", selected_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 738
        },
        "id": "PPAcGfvR8Znt",
        "outputId": "a307cf27-d94e-4889-f676-f16d6da43745"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Puntuaciones de características:\n",
            "Característica 1: 0.59\n",
            "Característica 2: 0.00\n",
            "Característica 3: 0.68\n",
            "Característica 4: 0.74\n",
            "Característica 5: 1.14\n",
            "Característica 6: 0.85\n",
            "Característica 7: 0.00\n",
            "Característica 8: 0.09\n",
            "Característica 9: 0.02\n",
            "Característica 10: 0.09\n",
            "\n",
            "Características seleccionadas (índices): [0 2 3 4 5]\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Aplica el método de filtro utilizando ANOVA Ref: https://towardsdatascience.com/anova-for-feature-selection-in-machine-learning-d9305e228476\n",
        "num_features_to_select = 5\n",
        "feature_selector = SelectKBest(score_func=f_classif, k=num_features_to_select)\n",
        "X_filter = feature_selector.fit_transform(X_feat, y_feat)\n",
        "\n",
        "# Obtener las puntuaciones de características\n",
        "feature_scores = feature_selector.scores_\n",
        "\n",
        "# Obtener las características seleccionadas\n",
        "selected_feature_indices = feature_selector.get_support(True)\n",
        "\n",
        "print(\"Puntuaciones de características:\")\n",
        "for feature, score in enumerate(feature_scores):\n",
        "    print(f\"Característica {feature + 1}: {score:.2f}\")\n",
        "\n",
        "print(\"\\nCaracterísticas seleccionadas (índices):\", selected_feature_indices)\n",
        "\n",
        "# Graficar las puntuaciones de características\n",
        "plt.figure(figsize=(18, 6))\n",
        "plt.bar(range(len(feature_scores)), feature_scores, tick_label=[f'Feature {i+1}' for i in range(len(feature_scores))])\n",
        "plt.xlabel('Características')\n",
        "plt.ylabel('Puntuaciones de características')\n",
        "plt.title('Puntuaciones de características utilizando ANOVA')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J94O6Do490DZ",
        "outputId": "09ab7868-f656-4ba9-b605-321b9b3a2953"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Características seleccionadas:\n",
            "Feature 1\n",
            "Feature 3\n",
            "Feature 4\n",
            "Feature 5\n",
            "Feature 6\n"
          ]
        }
      ],
      "source": [
        "# Inicializa el clasificador (puedes usar otros clasificadores según tus necesidades)\n",
        "classifier = LogisticRegression(solver='liblinear')\n",
        "\n",
        "# Inicializa RFE para seleccionar 5 características\n",
        "num_features_to_select = 5\n",
        "rfe = RFE(estimator=classifier, n_features_to_select=num_features_to_select)\n",
        "\n",
        "# Ajusta RFE al conjunto de datos\n",
        "rfe.fit(X_feat, y_feat)\n",
        "\n",
        "# Obtén las características seleccionadas\n",
        "selected_feature_indices = rfe.support_\n",
        "selected_features = [f'Feature {i + 1}' for i, selected in enumerate(selected_feature_indices) if selected]\n",
        "\n",
        "print(\"Características seleccionadas:\")\n",
        "for feature in selected_features:\n",
        "    print(feature)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Hzm3rmXBSXZ",
        "outputId": "2253e060-eb4e-4e42-a302-1a39017efca0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Características seleccionadas con Forward Selection:\n",
            "Feature 3\n",
            "Feature 9\n",
            "Feature 5\n",
            "Feature 8\n",
            "Feature 7\n",
            "Feature 2\n",
            "Feature 1\n",
            "Feature 4\n",
            "Feature 10\n",
            "Feature 6\n",
            "\n",
            "Características seleccionadas con Backward Selection:\n",
            "Feature 4\n",
            "Feature 6\n",
            "Feature 8\n",
            "Feature 10\n",
            "\n",
            "Características seleccionadas con Recursive Feature Elimination (RFE):\n",
            "Feature 1\n",
            "Feature 4\n",
            "Feature 5\n",
            "Feature 6\n",
            "Feature 10\n"
          ]
        }
      ],
      "source": [
        "# Divide los datos en conjuntos de entrenamiento y prueba\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_feat, y_feat, test_size=0.2, random_state=42)\n",
        "\n",
        "# Inicializa un clasificador (puedes usar otros clasificadores según tus necesidades)\n",
        "classifier = LogisticRegression(solver='liblinear')\n",
        "\n",
        "# Inicializa una lista para almacenar características seleccionadas\n",
        "selected_features_forward = []\n",
        "selected_features_backward = list(range(X_train.shape[1]))  # Comenzar con todas las características\n",
        "selected_features_rfe = None\n",
        "\n",
        "# Realiza la selección de características con Forward Selection\n",
        "while len(selected_features_forward) < X_train.shape[1]:\n",
        "    best_feature = None\n",
        "    best_accuracy = 0\n",
        "    for feature in range(X_train.shape[1]):\n",
        "        if feature in selected_features_forward:\n",
        "            continue\n",
        "        features_to_use = selected_features_forward + [feature]\n",
        "        X_train_subset = X_train[:, features_to_use]\n",
        "        X_test_subset = X_test[:, features_to_use]\n",
        "        classifier.fit(X_train_subset, y_train)\n",
        "        y_pred = classifier.predict(X_test_subset)\n",
        "        accuracy = accuracy_score(y_test, y_pred)\n",
        "        if accuracy > best_accuracy:\n",
        "            best_accuracy = accuracy\n",
        "            best_feature = feature\n",
        "    if best_feature is not None:\n",
        "        selected_features_forward.append(best_feature)\n",
        "    else:\n",
        "        break\n",
        "\n",
        "# Realiza la selección de características con Backward Selection\n",
        "while len(selected_features_backward) > 0:\n",
        "    worst_feature = None\n",
        "    worst_accuracy = best_accuracy\n",
        "    for feature in selected_features_backward:\n",
        "        features_to_use = selected_features_backward.copy()\n",
        "        features_to_use.remove(feature)\n",
        "        X_train_subset = X_train[:, features_to_use]\n",
        "        X_test_subset = X_test[:, features_to_use]\n",
        "        classifier.fit(X_train_subset, y_train)\n",
        "        y_pred = classifier.predict(X_test_subset)\n",
        "        accuracy = accuracy_score(y_test, y_pred)\n",
        "        if accuracy > worst_accuracy:\n",
        "            worst_accuracy = accuracy\n",
        "            worst_feature = feature\n",
        "    if worst_feature is not None:\n",
        "        selected_features_backward.remove(worst_feature)\n",
        "    else:\n",
        "        break\n",
        "\n",
        "# Realiza la selección de características con Recursive Feature Elimination (RFE)\n",
        "num_features_to_select = 5\n",
        "rfe = RFE(estimator=classifier, n_features_to_select=num_features_to_select)\n",
        "rfe.fit(X_train, y_train)\n",
        "selected_features_rfe = rfe.support_\n",
        "\n",
        "# Obtén las características seleccionadas\n",
        "selected_feature_names_forward = [f'Feature {i + 1}' for i in selected_features_forward]\n",
        "selected_feature_names_backward = [f'Feature {i + 1}' for i in selected_features_backward]\n",
        "selected_feature_names_rfe = [f'Feature {i + 1}' for i, selected in enumerate(selected_features_rfe) if selected]\n",
        "\n",
        "print(\"Características seleccionadas con Forward Selection:\")\n",
        "for feature in selected_feature_names_forward:\n",
        "    print(feature)\n",
        "\n",
        "print(\"\\nCaracterísticas seleccionadas con Backward Selection:\")\n",
        "for feature in selected_feature_names_backward:\n",
        "    print(feature)\n",
        "\n",
        "print(\"\\nCaracterísticas seleccionadas con Recursive Feature Elimination (RFE):\")\n",
        "for feature in selected_feature_names_rfe:\n",
        "    print(feature)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Codigo de KNN con Rechazo**"
      ],
      "metadata": {
        "id": "wUt6GlSpMnFw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Inicializa el clasificador KNN con un valor de k\n",
        "k = 10\n",
        "knn_classifier = KNeighborsClassifier(n_neighbors=k)\n",
        "\n",
        "# Ajusta el modelo a los datos de entrenamiento\n",
        "knn_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = knn_classifier.predict(X_test)\n",
        "\n",
        "# Calcula la precisión del modelo sin rechazo\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "# Umbral para el rechazo\n",
        "umbral_rechazo = 0.5\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba con rechazo\n",
        "y_pred_with_rechazo = []\n",
        "for i in range(len(X_test)):\n",
        "    distancias, indices = knn_classifier.kneighbors([X_test[i]], n_neighbors=k)\n",
        "    promedio_distancias = np.mean(distancias)\n",
        "    if promedio_distancias <= umbral_rechazo:\n",
        "        y_pred_with_rechazo.append(y_pred[i])\n",
        "        y_pred_with_rechazo.append(-1)\n",
        "    else:\n",
        "        y_pred_with_rechazo.append(knn_classifier.predict([X_test[i]]))\n",
        "\n",
        "# Calcula la precisión del modelo con rechazo\n",
        "y_pred_with_rechazo = np.array(y_pred_with_rechazo)\n",
        "y_pred_with_rechazo = y_pred_with_rechazo[y_pred_with_rechazo != -1]\n",
        "accuracy_with_rechazo = accuracy_score(y_test, y_pred_with_rechazo)\n",
        "\n",
        "print(f\"Precisión del modelo KNN sin rechazo: {accuracy:.2f}\")\n",
        "print(f\"Precisión del modelo KNN con rechazo (umbral {umbral_rechazo}): {accuracy_with_rechazo:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wg2P7o2QLt6W",
        "outputId": "298515e2-8908-4c79-db82-b78a8219b989"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo KNN sin rechazo: 0.35\n",
            "Precisión del modelo KNN con rechazo (umbral 0.5): 0.35\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Codigo de KNN con Distancia**"
      ],
      "metadata": {
        "id": "zzFQUizuM17-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Inicializa el clasificador KNN con un valor de k\n",
        "k = 10\n",
        "knn_classifier = KNeighborsClassifier(n_neighbors=k)\n",
        "\n",
        "# Ajusta el modelo a los datos de entrenamiento\n",
        "knn_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = knn_classifier.predict(X_test)\n",
        "\n",
        "# Calcula la precisión del modelo sin distancia media\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "# Umbral para la distancia media\n",
        "umbral_distancia_media = 0.5\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba con distancia media\n",
        "y_pred_con_distancia_media = []\n",
        "for i in range(len(X_test)):\n",
        "    distancias, indices = knn_classifier.kneighbors([X_test[i]], n_neighbors=k)\n",
        "    promedio_distancias = np.mean(distancias)\n",
        "    if promedio_distancias <= umbral_distancia_media:\n",
        "        y_pred_con_distancia_media.append(y_pred[i])\n",
        "    else:\n",
        "        y_pred_con_distancia_media.append(1 - y_pred[i])  # Invierte la etiqueta original\n",
        "\n",
        "# Calcula la precisión del modelo con distancia media\n",
        "accuracy_con_distancia_media = accuracy_score(y_test, y_pred_con_distancia_media)\n",
        "\n",
        "print(f\"Precisión del modelo KNN sin distancia media: {accuracy:.2f}\")\n",
        "print(f\"Precisión del modelo KNN con distancia media (umbral {umbral_distancia_media}): {accuracy_con_distancia_media:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gupqiE4uM4n0",
        "outputId": "3b285aaa-4509-494e-c3d4-7196bb90dade"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo KNN sin distancia media: 0.35\n",
            "Precisión del modelo KNN con distancia media (umbral 0.5): 0.65\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Codigo de KNN con onderación en ejemplos y características**"
      ],
      "metadata": {
        "id": "gM_RJgJdP_Qb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Inicializa el clasificador KNN con un valor de k y peso uniforme\n",
        "k = 10\n",
        "knn_classifier = KNeighborsClassifier(n_neighbors=k, weights='uniform')\n",
        "\n",
        "# Ajusta el modelo a los datos de entrenamiento\n",
        "knn_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = knn_classifier.predict(X_test)\n",
        "\n",
        "# Calcula la precisión del modelo con peso uniforme\n",
        "accuracy_uniform = accuracy_score(y_test, y_pred)\n",
        "\n",
        "# Cambia el peso a distancia inversa (mayor peso a ejemplos cercanos)\n",
        "knn_classifier = KNeighborsClassifier(n_neighbors=k, weights='distance')\n",
        "\n",
        "# Ajusta el modelo a los datos de entrenamiento con peso de distancia inversa\n",
        "knn_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba con peso de distancia inversa\n",
        "y_pred_distance_weighted = knn_classifier.predict(X_test)\n",
        "\n",
        "# Calcula la precisión del modelo con peso de distancia inversa\n",
        "accuracy_distance_weighted = accuracy_score(y_test, y_pred_distance_weighted)\n",
        "\n",
        "print(f\"Precisión del modelo KNN con peso uniforme: {accuracy_uniform:.2f}\")\n",
        "print(f\"Precisión del modelo KNN con peso de distancia inversa: {accuracy_distance_weighted:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7n7Xu0bTP_xh",
        "outputId": "1f857e63-b8d7-41f8-d3d9-56ef9482f138"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo KNN con peso uniforme: 0.35\n",
            "Precisión del modelo KNN con peso de distancia inversa: 0.45\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Support Vector Machine: Linealmente separables**\n"
      ],
      "metadata": {
        "id": "CLtYsUzfQqV0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Inicializa el clasificador SVM lineal\n",
        "clf = svm.SVC(kernel='linear')\n",
        "\n",
        "# Ajusta el modelo a los datos de entrenamiento\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "# Calcula la precisión del modelo\n",
        "accuracy = np.mean(y_pred == y_test)\n",
        "\n",
        "# Visualiza los datos y la superficie de decisión\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "# Dibuja los puntos de datos\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired, marker='o', s=100)\n",
        "\n",
        "# Dibuja la superficie de decisión\n",
        "ax = plt.gca()\n",
        "xlim = ax.get_xlim()\n",
        "ylim = ax.get_ylim()\n",
        "\n",
        "plt.title(f\"Precisión del modelo SVM lineal: {accuracy:.2f}\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "3rGl5cQmQyw5",
        "outputId": "8a8649b4-82df-4f69-a1d9-05c170731081"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Support Vector Machine: Regularizacion**"
      ],
      "metadata": {
        "id": "a_kHfDdERd2u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Inicializa el clasificador SVM con regularización (parámetro C)\n",
        "C = 1.0  # Valor de ajuste\n",
        "clf = svm.SVC(C=C, kernel='linear')\n",
        "\n",
        "# Ajusta el modelo a los datos de entrenamiento\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "# Calcula la precisión del modelo\n",
        "accuracy = np.mean(y_pred == y_test)\n",
        "\n",
        "# Visualiza los datos y la superficie de decisión\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "# Dibuja los puntos de datos\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired, marker='o', s=100)\n",
        "\n",
        "# Dibuja la superficie de decisión\n",
        "ax = plt.gca()\n",
        "xlim = ax.get_xlim()\n",
        "ylim = ax.get_ylim()\n",
        "\n",
        "plt.title(f\"Precisión del modelo SVM con regularización (C={C}): {accuracy:.2f}\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "WDrZZcF7Rf5q",
        "outputId": "2cc8825c-fbb6-44cc-b5dd-a14b60b80fd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modelos de Clasificación Multi-Clase"
      ],
      "metadata": {
        "id": "QO06ErQ-rRrb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Un clasificador OvO (One-Versus-One), también conocido como \"uno contra uno\", es una técnica de clasificación utilizada en problemas de clasificación multiclase. En lugar de tratar de clasificar directamente múltiples clases, la técnica OvO descompone el problema en múltiples clasificadores binarios. Básicamente, se crea un clasificador binario para cada posible par de clases. Esto significa que si tienes N clases, se generarán N(N-1)/2 clasificadores binarios."
      ],
      "metadata": {
        "id": "hbiLeyWdrt-8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear un clasificador SVM OvO.\n",
        "# Se creará un clasificador binario para cada par de clases posibles y se realizará una votación para tomar la decisión final.\n",
        "# Esta es una estrategia común para abordar problemas de clasificación multiclase.\n",
        "svm_classifier_ovo = SVC(kernel='linear', decision_function_shape='ovo', random_state=42)\n",
        "\n",
        "# Entrenar el clasificador\n",
        "svm_classifier_ovo.fit(X_feat, y_feat)\n",
        "\n",
        "# Realizar predicciones en el conjunto de prueba\n",
        "y_pred = svm_classifier_ovo.predict(X_feat)\n",
        "# print(X_feat)\n",
        "print(y_pred)\n",
        "\n",
        "# Calcular la precisión\n",
        "accuracy_ovo = accuracy_score(y_feat, y_pred)\n",
        "print(f'Precisión: {accuracy_ovo}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jamHfhS0lByu",
        "outputId": "8e032404-8ebf-407e-f2c8-61e53238e858"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 1 1 1 1 0 0 1 0 1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0 1 1 0 1 0 0 1 0 0 1 1\n",
            " 0 1 0 1 0 0 0 1 1 1 0 1 0 1 1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 1 0 1 1 0 1 1 0\n",
            " 1 0 1 0 1 1 0 1 0 0 0 1 0 0 1 0 1 0 1 1 1 1 1 0 1 0]\n",
            "Precisión: 0.64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Un clasificador OvR (One-Versus-Rest), también conocido como \"uno contra el resto\" o \"uno contra todos\", es una técnica de clasificación utilizada en problemas de clasificación multiclase. En lugar de tratar de clasificar directamente múltiples clases, la técnica OvR descompone el problema en múltiples clasificadores binarios. Básicamente, se crea un clasificador binario para cada clase, y cada uno de ellos se entrena para distinguir entre una clase específica y el \"resto\" de las clases."
      ],
      "metadata": {
        "id": "kNusOR_qr-2h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear un clasificador SVM OvR\n",
        "# Se creará un clasificador binario para cada par de clases posibles y se realizará una votación para tomar la decisión final.\n",
        "# Esta es una estrategia común para abordar problemas de clasificación multiclase.\n",
        "svm_classifier_ovr = SVC(kernel='linear', decision_function_shape='ovr', random_state=42)\n",
        "\n",
        "# Entrenar el clasificador\n",
        "svm_classifier_ovr.fit(X_feat, y_feat)\n",
        "\n",
        "# Realizar predicciones en el conjunto de prueba\n",
        "y_pred = svm_classifier_ovr.predict(X_feat)\n",
        "print(y_pred)\n",
        "\n",
        "# Calcular la precisión\n",
        "accuracy_ovr = accuracy_score(y_feat, y_pred)\n",
        "print(f'Precisión: {accuracy_ovr}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GtpxhR1_lohz",
        "outputId": "39a9b746-0824-4f96-e489-e59ebd65a0ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 1 1 1 1 0 0 1 0 1 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0 1 1 0 1 0 0 1 0 0 1 1\n",
            " 0 1 0 1 0 0 0 1 1 1 0 1 0 1 1 0 0 1 1 0 0 1 0 0 1 1 1 1 1 1 0 1 1 0 1 1 0\n",
            " 1 0 1 0 1 1 0 1 0 0 0 1 0 0 1 0 1 0 1 1 1 1 1 0 1 0]\n",
            "Precisión: 0.64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "La puntuación F1 en el contexto de machine learning es una métrica de evaluación de clasificación que combina la precisión (precision) y el recall (sensibilidad) en un solo valor. Se utiliza para evaluar el rendimiento de un modelo de clasificación, especialmente en problemas donde el equilibrio entre la precisión y el recall es importante."
      ],
      "metadata": {
        "id": "xPYqp_XtshXB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear un clasificador de regresión logística para calcular Puntuación F1\n",
        "logreg_classifier = LogisticRegression(solver='liblinear', random_state=42)\n",
        "\n",
        "# Entrenar el clasificador\n",
        "logreg_classifier.fit(X_feat, y_feat)\n",
        "\n",
        "# Realizar predicciones en el conjunto de prueba\n",
        "y_pred = logreg_classifier.predict(X_feat)\n",
        "\n",
        "# Calcular el puntaje F1\n",
        "f1 = f1_score(y_feat, y_pred)\n",
        "print(f'Puntaje F1: {f1}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uX33k_0-o4Jv",
        "outputId": "ba097188-57ea-4ebb-dcb8-d2a12de0b459"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Puntaje F1: 0.5858585858585857\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "La matriz de confusión es una herramienta esencial en el campo de la evaluación de modelos de clasificación en machine learning. Se utiliza para visualizar el rendimiento de un modelo de clasificación al comparar las predicciones del modelo con los valores reales del conjunto de datos. La matriz de confusión es especialmente útil cuando se trata de problemas de clasificación binaria o multiclase.\n",
        "\n",
        "La matriz de confusión es una tabla que organiza las predicciones del modelo en cuatro categorías:\n",
        "\n",
        "1.   **Verdaderos positivos (True Positives, TP)**: Estos son los casos en los que el modelo predice correctamente una instancia como positiva cuando realmente es positiva.\n",
        "2.   **Falsos positivos (False Positives, FP)**: Estos son los casos en los que el modelo predice incorrectamente una instancia como positiva cuando en realidad es negativa (un error de tipo I).\n",
        "3.   **Verdaderos negativos (True Negatives, TN)**: Estos son los casos en los que el modelo predice correctamente una instancia como negativa cuando realmente es negativa.\n",
        "4.  **Falsos negativos (False Negatives, FN)**: Estos son los casos en los que el modelo predice incorrectamente una instancia como negativa cuando en realidad es positiva (un error de tipo II).\n",
        "\n",
        "\n",
        "|       Clase real      |     Positiva     |     Negativa     |\n",
        "|-----------------------|------------------|------------------|\n",
        "| Clase predicha        |        TP        |        FP        |\n",
        "|-----------------------|------------------|------------------|\n",
        "|                       |        FN        |        TN        |"
      ],
      "metadata": {
        "id": "tnhNyQDasvWK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear un clasificador de regresión logística\n",
        "logreg_classifier = LogisticRegression(solver='liblinear', random_state=42)\n",
        "\n",
        "# Entrenar el clasificador\n",
        "logreg_classifier.fit(X_feat, y_feat)\n",
        "\n",
        "# Realizar predicciones en el conjunto de prueba\n",
        "y_pred = logreg_classifier.predict(X_feat)\n",
        "\n",
        "# Calcular la matriz de confusión\n",
        "confusion = confusion_matrix(y_feat, y_pred)\n",
        "print(\"Matriz de Confusión:\")\n",
        "print(confusion)\n",
        "\n",
        "# Calcular otras métricas de evaluación\n",
        "accuracy = accuracy_score(y_feat, y_pred)\n",
        "precision = precision_score(y_feat, y_pred)\n",
        "recall = recall_score(y_feat, y_pred)\n",
        "f1 = f1_score(y_feat, y_pred)\n",
        "\n",
        "# Calcular el F1-score macro\n",
        "f1_macro = f1_score(y_feat, y_pred, average='macro')\n",
        "\n",
        "# Calcular el F1-score ponderado\n",
        "f1_weighted = f1_score(y_feat, y_pred, average='weighted')\n",
        "\n",
        "# Calcular el F1-score micro\n",
        "f1_micro = f1_score(y_feat, y_pred, average='micro')\n",
        "\n",
        "\n",
        "print(f'Accuracy Score: {accuracy}')\n",
        "print(f'Precision Score: {precision}')\n",
        "print(f'Recall Score: {recall}')\n",
        "print(f'Puntaje F1: {f1}')\n",
        "print(f'F1-score Macro: {f1_macro}')\n",
        "print(f'F1-score Ponderado: {f1_weighted}')\n",
        "print(f'F1-score Micro: {f1_micro}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HIf7s2HkpqPr",
        "outputId": "4301f856-3539-40a3-e55c-5a0fd91c0d9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Matriz de Confusión:\n",
            "[[30 20]\n",
            " [21 29]]\n",
            "Accuracy Score: 0.59\n",
            "Precision Score: 0.5918367346938775\n",
            "Recall Score: 0.58\n",
            "Puntaje F1: 0.5858585858585857\n",
            "F1-score Macro: 0.5899589958995899\n",
            "F1-score Ponderado: 0.58995899589959\n",
            "F1-score Micro: 0.59\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**F1-score Macro**\n",
        "\n",
        "- **Fórmula:** Las métricas macro calculan la precisión, el recuerdo y el F1 por separado para cada clase y luego toman el promedio sin ponderar de estas métricas. En otras palabras, calcula las métricas por clase y las promedia.\n",
        "- **Uso:** Las métricas macro son adecuadas cuando se desea evaluar el rendimiento del modelo en cada clase por separado y se considera que todas las clases son igualmente importantes. Es especialmente útil cuando no hay un desequilibrio significativo entre las clases y se desea una evaluación imparcial de cada clase\n",
        "\n",
        "**F1-score Ponderado**\n",
        "- **Fórmula:** Las métricas de peso son una variante de las métricas macro que tienen en cuenta el desequilibrio de clases. Calculan la precisión, el recuerdo y el F1 por separado para cada clase y luego toman un promedio ponderado de estas métricas, donde el peso se basa en la proporción de muestras en cada clase.\n",
        "- **Uso:** Las métricas de peso son adecuadas cuando el desequilibrio de clases es significativo, es decir, cuando algunas clases tienen muchas más muestras que otras. Estas métricas proporcionan una evaluación más precisa del rendimiento del modelo, ya que tienen en cuenta la importancia relativa de cada clase en función de su tamaño.\n",
        "\n",
        "**F1-score Micro**\n",
        "- **Fórmula:** El F1 Micro es una métrica que calcula la precisión y el recuerdo (recall) a nivel de instancia y luego calcula la puntuación F1 usando estas métricas. La precisión mide la proporción de verdaderos positivos sobre el número de predicciones positivas, mientras que el recuerdo mide la proporción de verdaderos positivos sobre el número de ejemplos verdaderamente positivos.\n",
        "- **Uso:** El F1 Micro es útil cuando se quiere evaluar el rendimiento de un modelo en un conjunto de datos desequilibrado, donde una clase tiene muchas más muestras que las demás. Pondera todas las instancias por igual, lo que significa que no importa si una clase es más pequeña o más grande en términos de muestras."
      ],
      "metadata": {
        "id": "z97TuwXsJ1BE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Arboles de Decisión"
      ],
      "metadata": {
        "id": "o7mXspfOW9Ne"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Crea el modelo del árbol de decisión\n",
        "clf = DecisionTreeClassifier()\n",
        "\n",
        "# Entrena el modelo en el conjunto de entrenamiento\n",
        "clf.fit(X_feat, y_feat)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = clf.predict(X_feat)\n",
        "\n",
        "# Calcula la precisión del modelo\n",
        "accuracy = accuracy_score(y_feat, y_pred)\n",
        "print(f\"Precisión del árbol de decisión: {accuracy}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QTmDnpP9XBWC",
        "outputId": "36909b3e-1bca-4867-f7ed-99030f802141"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del árbol de decisión: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Crea un modelo de árbol de decisión CART\n",
        "clf = DecisionTreeClassifier(criterion='log_loss')  # Usamos el criterio 'gini' para problemas de clasificación https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\n",
        "\n",
        "# Entrena el modelo en el conjunto de entrenamiento\n",
        "clf.fit(X_feat, y_feat)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = clf.predict(X_feat)\n",
        "\n",
        "# Calcula la precisión del modelo\n",
        "accuracy = accuracy_score(y_feat, y_pred)\n",
        "print(f\"Precisión del árbol de decisión CART: {accuracy}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wuL9A02QX69Z",
        "outputId": "baf4d13c-1214-4cc8-fdf8-0a92484d735b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del árbol de decisión CART: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Crea un modelo de árbol de decisión Splitter\n",
        "clf = DecisionTreeClassifier(splitter='random')  # Usamos el criterio splitter 'best' para problemas de clasificación https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\n",
        "\n",
        "# Entrena el modelo en el conjunto de entrenamiento\n",
        "clf.fit(X_feat, y_feat)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = clf.predict(X_feat)\n",
        "\n",
        "# Calcula la precisión del modelo\n",
        "accuracy = accuracy_score(y_feat, y_pred)\n",
        "print(f\"Precisión del árbol de decisión : {accuracy}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j9UXAL5gYMB1",
        "outputId": "b9a44510-2780-4c0c-b528-c84bec9498e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del árbol de decisión : 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Crea un modelo de árbol con poda\n",
        "clf = DecisionTreeClassifier(\n",
        "    criterion         = 'gini',  # Criterio de división ('gini' o 'entropy')\n",
        "    splitter          = 'best',  # 'best' para la mejor división, o 'random' para división aleatoria\n",
        "    max_depth         = None,    # Profundidad máxima del árbol (ajusta según tus necesidades)\n",
        "    min_samples_split = 2,       # Número mínimo de muestras requeridas para dividir un nodo\n",
        "    min_samples_leaf  = 1        # Número mínimo de muestras requeridas en un nodo hoja\n",
        ")\n",
        "\n",
        "# Entrena el modelo en el conjunto de entrenamiento\n",
        "clf.fit(X_feat, y_feat)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = clf.predict(X_feat)\n",
        "\n",
        "# Calcula la precisión del modelo\n",
        "accuracy = accuracy_score(y_feat, y_pred)\n",
        "recall   = recall_score(y_feat, y_pred)\n",
        "print(f\"Precisión del árbol de decisión : {accuracy}\")\n",
        "print(f\"Sensibilidad del árbol de decisión : {recall}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zsY10MbRYt34",
        "outputId": "a84d9a08-deab-4390-bef1-47a40fd77907"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del árbol de decisión : 1.0\n",
            "Sensibilidad del árbol de decisión : 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Gini (Índice Gini):**\n",
        "\n",
        "- **Definición:** El índice Gini se utiliza comúnmente en árboles de decisión y se refiere a la impureza de un conjunto de datos. Cuanto menor es el valor de Gini, más \"puro\" es el conjunto de datos, lo que significa que está compuesto principalmente por una sola clase. La fórmula del índice Gini es una medida de cuánto se mezclan las clases en un conjunto de datos.\n",
        "- **Uso:** El índice Gini es útil en la construcción de árboles de decisión, donde se busca dividir un conjunto de datos en subconjuntos más puros en función de características particulares. Se utiliza para evaluar qué división de datos es la más informativa y reduce mejor la impureza del conjunto de datos.\n",
        "\n",
        "**Entropía (Entropy):**\n",
        "\n",
        "- **Definición:** La entropía mide la incertidumbre o la impureza en un conjunto de datos. Cuanto mayor es la entropía, más incierto o impuro es el conjunto de datos. La fórmula de la entropía se basa en la probabilidad de cada clase en el conjunto de datos y se utiliza para evaluar cuán bien un modelo de clasificación realiza la separación de clases.\n",
        "- **Uso:** La entropía se usa comúnmente en árboles de decisión y otros algoritmos de clasificación. Se utiliza para evaluar la calidad de una división de datos y decidir qué división reduce la incertidumbre o entropía en mayor medida.\n",
        "\n",
        "**Log Loss (Cross-Entropy):**\n",
        "\n",
        "- **Definición:** Log Loss (pérdida logarítmica) o Cross-Entropy es una medida de qué tan bien un modelo de clasificación asigna probabilidades a las clases. Cuanto menor es el valor de Log Loss, mejor es el modelo en términos de calibración de probabilidades y precisión.\n",
        "- **Uso:** Log Loss se utiliza comúnmente en problemas de clasificación multiclase y es una métrica de evaluación popular en tareas de clasificación en las que se requiere una buena estimación de las probabilidades. También es útil en problemas de clasificación binaria. Es importante destacar que Log Loss penaliza de manera significativa las predicciones incorrectas con alta confianza (asignación incorrecta de probabilidades altas a una clase)."
      ],
      "metadata": {
        "id": "qt3IjcTRKr0H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Estrategias"
      ],
      "metadata": {
        "id": "ipC1cbzlkdYt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tipos\n",
        "\n",
        "* class_weight='balanced':\n",
        "\n",
        ">> **Caso de uso:** Útil cuando tienes un desequilibrio de clases significativo. El modelo ajustará automáticamente los pesos de clase de manera inversamente proporcional a la frecuencia de las clases en los datos, lo que penalizará la clasificación incorrecta de la clase mayoritaria y ayudará a equilibrar la clasificación.\n",
        "\n",
        "* class_weight=dict:\n",
        "\n",
        ">> **Caso de uso:** Te permite personalizar los pesos de clase de acuerdo a tus necesidades. Puedes asignar pesos específicos a cada clase en función de la importancia relativa que le asignes. Por ejemplo, si una clase minoritaria es más importante, puedes darle un peso más alto.\n",
        "\n"
      ],
      "metadata": {
        "id": "OJNrsXRjltUC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Divide los 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=0)\n",
        "\n",
        "# Define el modelo de Regresión Logística con ajuste de pesos de clase\n",
        "class_dict = {0: 1, 1: 3}\n",
        "model = LogisticRegression(class_weight=class_dict)  # 'balanced' ajusta automáticamente los pesos\n",
        "\n",
        "# Entrena el modelo\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Calcula métricas de clasificación\n",
        "report = classification_report(y_test, y_pred)\n",
        "\n",
        "# Imprime el informe de clasificación\n",
        "print(\"Informe de Clasificación:\\n\", report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z0_4c8Lmkg8p",
        "outputId": "07e90426-9628-4e5a-cb13-ce592f4fc9d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Informe de Clasificación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.86      0.79        22\n",
            "           1       0.25      0.12      0.17         8\n",
            "\n",
            "    accuracy                           0.67        30\n",
            "   macro avg       0.49      0.49      0.48        30\n",
            "weighted avg       0.60      0.67      0.62        30\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **sampling_strategy='auto'(predeterminado):**\n",
        "\n",
        "> Con sampling_strategy='auto', el submuestreo se realiza automáticamente de manera que la clase mayoritaria se reduce al tamaño de la clase minoritaria. Esta es una forma sencilla de equilibrar las clases.\n",
        "\n",
        "* **sampling_strategy=float:**\n",
        "\n",
        "> Puedes proporcionar un valor entre 0 y 1 como sampling_strategy, que representa la proporción de la clase mayoritaria que deseas mantener después del submuestreo. Por ejemplo, si estableces sampling_strategy=0.5, se mantendría la mitad de la clase mayoritaria.\n",
        "\n",
        "* **sampling_strategy=dict:**\n",
        "\n",
        "> Puedes proporcionar un diccionario personalizado que especifique la cantidad de ejemplos que deseas mantener para cada clase. Por ejemplo, {0: 100, 1: 200} significaría que se mantendrían 100 ejemplos de la clase mayoritaria (etiqueta 0) y 200 ejemplos de la clase minoritaria (etiqueta 1) después del submuestreo.\n",
        "\n",
        "* **sampling_strategy='majority':**\n",
        "\n",
        "> Esta opción es útil si deseas reducir la clase mayoritaria al tamaño de la clase minoritaria.\n",
        "\n",
        "* **sampling_strategy='not minority':**\n",
        "\n",
        "> Mantendrá todos los ejemplos de la clase minoritaria y submuestre la clase mayoritaria.\n",
        "\n",
        "* **sampling_strategy='all':**\n",
        "\n",
        "> Mantendrá todos los ejemplos de ambas clases, lo que en realidad no realizará el submuestreo.\n",
        "\n"
      ],
      "metadata": {
        "id": "7_e0YcyHo4xB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Aplica submuestreo para equilibrar las clases en el conjunto de entrenamiento\n",
        "undersampler = RandomUnderSampler(sampling_strategy='majority', random_state=0)\n",
        "X_train_resampled, y_train_resampled = undersampler.fit_resample(X_feat, y_feat)\n",
        "\n",
        "# Define el modelo de Regresión Logística\n",
        "model = LogisticRegression()\n",
        "\n",
        "# Entrena el modelo en el conjunto de datos equilibrado\n",
        "model.fit(X_train_resampled, y_train_resampled)\n",
        "\n",
        "# Realiza predicciones en el conjunto de prueba\n",
        "y_pred = model.predict(X_feat)\n",
        "\n",
        "# Calcula métricas de clasificación\n",
        "report = classification_report(y_feat, y_pred)\n",
        "\n",
        "# Imprime el informe de clasificación\n",
        "print(\"Informe de Clasificación:\\n\", report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BfKGfgcToRf1",
        "outputId": "0fb6eb67-0d72-4e99-f3e0-64854916126b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Informe de Clasificación:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.59      0.60      0.59        50\n",
            "           1       0.59      0.58      0.59        50\n",
            "\n",
            "    accuracy                           0.59       100\n",
            "   macro avg       0.59      0.59      0.59       100\n",
            "weighted avg       0.59      0.59      0.59       100\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Análisis ROC"
      ],
      "metadata": {
        "id": "Kjyj3b4jqsN2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "La curva ROC (Receiver Operating Characteristic) es una gráfica que muestra la relación entre la tasa de verdaderos positivos (True Positive Rate o TPR) y la tasa de falsos positivos (False Positive Rate o FPR) en función de diferentes umbrales de decisión.\n",
        "* El eje X representa el FPR, que es la proporción de falsos positivos en relación con el total de negativos reales. Se calcula como FPR = FP / (FP + TN), donde FP son falsos positivos y TN son verdaderos negativos.\n",
        "* El eje Y representa el TPR, que es la proporción de verdaderos positivos en relación con el total de positivos reales. Se calcula como TPR = TP / (TP + FN), donde TP son verdaderos positivos y FN son falsos negativos.\n",
        "* La curva ROC muestra cómo cambia la TPR en función del FPR a medida que se ajusta el umbral de decisión del modelo. Cuanto más alejada esté la curva ROC de la línea de referencia diagonal (conocida como \"línea aleatoria\"), mejor es el rendimiento del modelo."
      ],
      "metadata": {
        "id": "BZHWc-y9rKXZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define el modelo de Regresión Logística\n",
        "model = LogisticRegression()\n",
        "\n",
        "# Entrena el modelo\n",
        "model.fit(X_feat, y_feat)\n",
        "\n",
        "# Realiza predicciones de probabilidad en el conjunto de prueba\n",
        "y_prob = model.predict_proba(X_feat)[:, 1]\n",
        "\n",
        "# Calcula la curva ROC\n",
        "fpr, tpr, thresholds = roc_curve(y_feat, y_prob)\n",
        "\n",
        "# Calcula el AUC (Área bajo la curva ROC)\n",
        "auc = roc_auc_score(y_feat, y_prob)\n",
        "\n",
        "# Grafica la curva ROC\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, label=f'ROC Curve (AUC = {auc:.2f})')\n",
        "plt.plot([0, 1], [0, 1], 'k--', color='gray', label='Random')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Curva ROC')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "Yl-JSH3ZqujF",
        "outputId": "abe3ca47-63f4-4c7f-f5eb-97ba000fc14d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-edf330719cba>:19: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"k--\" (-> color='k'). The keyword argument will take precedence.\n",
            "  plt.plot([0, 1], [0, 1], 'k--', color='gray', label='Random')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "El Área bajo la Curva ROC (AUC) es una métrica que cuantifica la capacidad del modelo para distinguir entre clases positivas y negativas. Representa el área debajo de la curva ROC.\n",
        "El valor de AUC varía entre 0 y 1, donde un valor de 1 indica un modelo perfecto (puede distinguir perfectamente entre clases), y un valor de 0.5 indica que el modelo no es mejor que una elección aleatoria.\n",
        "En general, cuanto mayor sea el valor de AUC, mejor será el rendimiento del modelo. Un AUC de 0.5 significa que el modelo no tiene habilidad de clasificación, mientras que un AUC de 1.0 indica un rendimiento perfecto."
      ],
      "metadata": {
        "id": "qUj7Sy9ZrbKL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Over/Under Fitting & CrossValidation"
      ],
      "metadata": {
        "id": "krMdef_wz2eH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dividir los datos en conjuntos de entrenamiento y prueba\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_fitt, y_fitt, test_size=0.5, random_state=0)\n",
        "\n",
        "# Crear un modelo de regresión lineal\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Crear un modelo de regresión polinómica de grado 2\n",
        "poly = PolynomialFeatures(degree=2)\n",
        "X_poly = poly.fit_transform(X_train)\n",
        "model_poly = LinearRegression()\n",
        "model_poly.fit(X_poly, y_train)\n",
        "y_poly_pred = model_poly.predict(poly.transform(X_test))\n",
        "\n",
        "# Crear un modelo de regresión polinómica de grado 20\n",
        "poly = PolynomialFeatures(degree=20)\n",
        "X_poly = poly.fit_transform(X_train)\n",
        "model_poly_high_degree = LinearRegression()\n",
        "model_poly_high_degree.fit(X_poly, y_train)\n",
        "y_poly_high_degree_pred = model_poly_high_degree.predict(poly.transform(X_test))\n",
        "\n",
        "# Visualizar los resultados\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "plt.subplot(131)\n",
        "plt.scatter(X_fitt, y_fitt, s=20, label=\"Datos reales\")\n",
        "plt.plot(X_test, y_pred, color='r', label=\"Regresión Lineal\")\n",
        "plt.title(\"Regresión Lineal\")\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(132)\n",
        "plt.scatter(X_fitt, y_fitt, s=20, label=\"Datos reales\")\n",
        "plt.plot(X_test, y_poly_pred, color='r', label=\"Polinómica (grado 2)\")\n",
        "plt.title(\"Regresión Polinómica (grado 2)\")\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(133)\n",
        "plt.scatter(X_fitt, y_fitt, s=20, label=\"Datos reales\")\n",
        "plt.plot(X_test, y_poly_high_degree_pred, color='r', label=\"Polinómica (grado 20)\")\n",
        "plt.title(\"Regresión Polinómica (grado 20)\")\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "RV0BTfQyz3rJ",
        "outputId": "ffd7347d-86d4-47ac-93a5-68f0b048ad35"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear un modelo de regresión lineal\n",
        "model = LinearRegression()\n",
        "\n",
        "# Realizar validación cruzada (cross-validation)\n",
        "# Aquí utilizamos la validación cruzada con k=5 (5-fold cross-validation)\n",
        "# Esto divide los datos en 5 conjuntos de entrenamiento y prueba diferentes, ajusta el modelo en cada uno de ellos y calcula las puntuaciones de rendimiento (en este caso, el coeficiente de determinación R^2).\n",
        "# Cuanto más cercano a 1 esté R^2, mejor se ajusta el modelo a los datos\n",
        "# La validación cruzada de 5 pliegues es una elección común porque proporciona un equilibrio entre la exhaustividad (probar el modelo en varios subconjuntos) y la eficiencia computaciona\n",
        "scores = cross_val_score(model, X_fitt, y_fitt, cv=5, scoring='r2')\n",
        "\n",
        "# Calcular la puntuación media y la desviación estándar de las puntuaciones\n",
        "mean_score = np.mean(scores)\n",
        "std_score = np.std(scores)\n",
        "# Calcular la media de y_fitt\n",
        "mean_y = np.mean(y_fitt)\n",
        "\n",
        "# Agregar la línea de la media al gráfico\n",
        "plt.axhline(y=mean_score, color='r', linestyle='--', label=\"Media de y\")\n",
        "\n",
        "# Crear un gráfico de dispersión de los datos\n",
        "plt.scatter(X_fitt, y_fitt, label=\"Datos reales\", s=20)\n",
        "plt.xlabel(\"Variable X\")\n",
        "plt.ylabel(\"Variable y\")\n",
        "plt.title(\"Gráfico de Dispersión de Datos\")\n",
        "\n",
        "# Mostrar el gráfico\n",
        "plt.show()\n",
        "\n",
        "# Finalmente, imprimimos las puntuaciones individuales de cada iteración de validación cruzada, la puntuación media y la desviación estándar de las puntuaciones. La puntuación media proporciona una estimación general del rendimiento del modelo, y la desviación estándar indica la variabilidad de las puntuaciones en las iteraciones de la validación cruzada.\n",
        "print(\"Puntuaciones individuales:\", scores)\n",
        "print(\"Puntuación media:\", mean_score)\n",
        "print(\"Desviación estándar de las puntuaciones:\", std_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "NstS_O2h16Yc",
        "outputId": "fe4fc940-e3f4-4e96-bff8-bed3c3200dfe"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Puntuaciones individuales: [-37.95451531 -64.69472581  -8.22939935   0.5324471  -15.9986848 ]\n",
            "Puntuación media: -25.26897563534909\n",
            "Desviación estándar de las puntuaciones: 23.48323470442906\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Desviación Estándar Pequeña:** Si la desviación estándar es pequeña, significa que la mayoría de los valores en el conjunto de datos están cerca de la media. En otras palabras, los datos son relativamente homogéneos y no varían mucho. Esto puede indicar una mayor consistencia o estabilidad en los datos.\n",
        "\n",
        "**Desviación Estándar Grande:** Si la desviación estándar es grande, indica que los valores en el conjunto de datos están más dispersos en relación con la media. Los datos son heterogéneos y varían ampliamente. Puede sugerir una mayor variabilidad en los datos.\n",
        "\n",
        "**Comparación con la Media:** Puedes interpretar la desviación estándar en relación con la media. Si la media es 0 (o muy cercana a 0), entonces la desviación estándar muestra cuán dispersos están los datos en relación con ese punto. Si la media no es 0, la desviación estándar te muestra cuánto los valores se alejan de la media."
      ],
      "metadata": {
        "id": "swirs1GX3DVm"
      }
    }
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