{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "019df23a",
   "metadata": {},
   "source": [
    "# Clasificadores Binarios"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "363b6961",
   "metadata": {},
   "source": [
    "## Resumen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c8ae83a",
   "metadata": {},
   "source": [
    "En esta notebook se presentan ejemplos de funcionamiento de clasificadores binarios aplicados a la predicción del diagnóstico de una enfermedad.\n",
    "Para ello, se utilizan distintos algoritmos aplicados sobre algunos conjuntos de datos públicos, a los fines de analizar pros y contras de cada caso."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e97ca14",
   "metadata": {},
   "source": [
    "## 1. Inicialización"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abbdd3a4",
   "metadata": {},
   "source": [
    "### Importación de Librerías"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a9c200a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from sklearn import linear_model\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "#\n",
    "separador=os.sep"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29c9c119",
   "metadata": {},
   "source": [
    "### Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e31d712b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Para elegir el Dataset, comentar el que no desea utilizar:\n",
    "archivo='datasets'+str(separador)+'diabetes.csv';target='Diabetes';LABELS=('NEG','POS')\n",
    "#archivo='breast_cancer.csv';target='target';LABELS=('NEG','POS')\n",
    "df = pd.read_csv(archivo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f45cc3d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encabezado: primeras 5 filas\n",
      "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
      "0            6      148             72             35        0  33.6   \n",
      "1            1       85             66             29        0  26.6   \n",
      "2            8      183             64              0        0  23.3   \n",
      "3            1       89             66             23       94  28.1   \n",
      "4            0      137             40             35      168  43.1   \n",
      "\n",
      "   DiabetesPedigreeFunction  Age  Diabetes  \n",
      "0                     627.0   50         1  \n",
      "1                     351.0   31         0  \n",
      "2                     672.0   32         1  \n",
      "3                     167.0   21         0  \n",
      "4                    2288.0   33         1  \n",
      "Shape: Filas y Columnas de la Matriz (768, 9)\n",
      "Dataset balanceado: NO\n",
      "Dataset normalizado: NO\n",
      "Dataset:   datasets/diabetes.csv  Registros:  768  Balanceado= NO  Normalizado= NO\n"
     ]
    }
   ],
   "source": [
    "# Visualizacion:\n",
    "general=df.head()\n",
    "print('Encabezado: primeras 5 filas\\n'+str(general))\n",
    "# cuantas filas y columnas tiene el df\n",
    "forma=df.shape\n",
    "print('Shape: Filas y Columnas de la Matriz '+str(forma))\n",
    "# La ocurrencia de casos positivos y no positivos\n",
    "participacion=pd.value_counts(df[target], sort = True)\n",
    "vectorParticipacion=(participacion[0],participacion[1])\n",
    "proporcion=(100*participacion[1])/(participacion[0]+participacion[1])\n",
    "if(proporcion>35 and proporcion<65):\n",
    "    balanceado='SI'\n",
    "else:\n",
    "    balanceado='NO'\n",
    "print('Dataset balanceado: '+str(balanceado))\n",
    "normalizado='SI'\n",
    "for i in range(df.shape[1]):\n",
    "    for j in range(df.shape[0]):\n",
    "        if(df.iloc[i,j]>1):\n",
    "            normalizado='NO'\n",
    "            break\n",
    "print('Dataset normalizado: '+str(normalizado))\n",
    "print('Dataset:  ',archivo,' Registros: ',len(df),' Balanceado=',balanceado,' Normalizado=',normalizado)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "86c757e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_16844/2759812844.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  df.drop([target],1).hist(figsize=(10,8))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.drop([target],1).hist(figsize=(10,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "285c3aa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplots\n",
    "df.plot(kind='box', subplots=True, layout=(3,3), sharex=False, sharey=False,figsize=(10,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a22be6e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cantidad de Observaciones')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafico:\n",
    "count_classes = pd.value_counts(df[target], sort = True)\n",
    "count_classes.plot(kind = 'bar', rot=0)\n",
    "plt.xticks(range(2), LABELS)\n",
    "plt.title(\"Frequencia de Diagnosticos por cantidad de Observaciones\")\n",
    "plt.xlabel(target)\n",
    "plt.ylabel(\"Cantidad de Observaciones\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ee05815",
   "metadata": {},
   "source": [
    "## 2. Pre-Procesamiento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2dc9e67f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_16844/409321591.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  X = np.array(df.drop([target],1))\n"
     ]
    }
   ],
   "source": [
    "X = np.array(df.drop([target],1))\n",
    "y = np.array(df[target])\n",
    "# Normalizacion:\n",
    "X_Norm=preprocessing.scale(X)\n",
    "if(normalizado=='NO'):\n",
    "    X=X_Norm\n",
    "# Manejo de balanceo de datos\n",
    "balancear=None\n",
    "if(balanceado=='NO'):\n",
    "    balancear='balanced'\n",
    "# Split del Dataset en Train / Test \n",
    "validation_size = 0.20\n",
    "seed = 7\n",
    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, y, test_size=validation_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69e96bd3",
   "metadata": {},
   "source": [
    "## 3. Entrenamiento y Evaluación de Modelos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa94f8e3",
   "metadata": {},
   "source": [
    "### 3.1. Regresión Logística:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6357144a",
   "metadata": {},
   "source": [
    "#### Entrenamiento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "510f720d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(class_weight=&#x27;balanced&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(class_weight=&#x27;balanced&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(class_weight='balanced')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = linear_model.LogisticRegression(class_weight=balancear)\n",
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ff8be84",
   "metadata": {},
   "source": [
    "#### Evaluación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1e2968b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[79 18]\n",
      " [12 45]]\n",
      "\n",
      "Reporte de las Clasificaciones\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.81      0.84        97\n",
      "           1       0.71      0.79      0.75        57\n",
      "\n",
      "    accuracy                           0.81       154\n",
      "   macro avg       0.79      0.80      0.80       154\n",
      "weighted avg       0.81      0.81      0.81       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.score(X_test,Y_test)\n",
    "predictions = model.predict(X_test)\n",
    "# Confusion Matrix\n",
    "matrizConfusion=confusion_matrix(Y_test, predictions)\n",
    "print('Confusion Matrix:\\n',matrizConfusion)\n",
    "# Evaluation Report\n",
    "print('\\nReporte de las Clasificaciones\\n',classification_report(Y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a967cdcb",
   "metadata": {},
   "source": [
    "### 3.2. Red Neuronal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db33d094",
   "metadata": {},
   "source": [
    "#### Entrenamiento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e346459d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(activation=&#x27;tanh&#x27;, alpha=0.1, batch_size=5, hidden_layer_sizes=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(activation=&#x27;tanh&#x27;, alpha=0.1, batch_size=5, hidden_layer_sizes=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(activation='tanh', alpha=0.1, batch_size=5, hidden_layer_sizes=10)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = MLPClassifier()\n",
    "params1={'activation': 'tanh', 'alpha': 0.1, 'batch_size': 5, 'hidden_layer_sizes': 10, 'max_iter': 200}\n",
    "params2={'activation': 'relu', 'alpha': 0.1, 'batch_size': 20, 'hidden_layer_sizes': 20, 'max_iter': 200}\n",
    "params=params1\n",
    "model.set_params(**params)\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4accde9b",
   "metadata": {},
   "source": [
    "#### Evaluación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f151fea2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[89  8]\n",
      " [21 36]]\n",
      "\n",
      "Reporte de las Clasificaciones\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.92      0.86        97\n",
      "           1       0.82      0.63      0.71        57\n",
      "\n",
      "    accuracy                           0.81       154\n",
      "   macro avg       0.81      0.77      0.79       154\n",
      "weighted avg       0.81      0.81      0.81       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.score(X_test,Y_test)\n",
    "predictions = model.predict(X_test)\n",
    "# Confusion Matrix\n",
    "matrizConfusion=confusion_matrix(Y_test, predictions)\n",
    "print('Confusion Matrix:\\n',matrizConfusion)\n",
    "# Evaluation Report\n",
    "print('\\nReporte de las Clasificaciones\\n',classification_report(Y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e4da24f",
   "metadata": {},
   "source": [
    "### 3.3. Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "30bbf596",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest - Confusion Matrix:\n",
      " [[88  9]\n",
      " [20 37]]\n",
      "\n",
      "Random Forest - Reporte de las Clasificaciones\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.91      0.86        97\n",
      "           1       0.80      0.65      0.72        57\n",
      "\n",
      "    accuracy                           0.81       154\n",
      "   macro avg       0.81      0.78      0.79       154\n",
      "weighted avg       0.81      0.81      0.81       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Crear el modelo Random Forest\n",
    "rf_model = RandomForestClassifier()\n",
    "\n",
    "# Entrenar el modelo\n",
    "rf_model.fit(X_train, Y_train)\n",
    "\n",
    "# Hacer predicciones\n",
    "rf_predictions = rf_model.predict(X_test)\n",
    "\n",
    "# Evaluar el modelo\n",
    "rf_matrizConfusion = confusion_matrix(Y_test, rf_predictions)\n",
    "print('Random Forest - Confusion Matrix:\\n', rf_matrizConfusion)\n",
    "print('\\nRandom Forest - Reporte de las Clasificaciones\\n', classification_report(Y_test, rf_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "326082e8",
   "metadata": {},
   "source": [
    "# 4. Conclusiones:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05284697",
   "metadata": {},
   "source": [
    "Se generaron tres modelos clasificadores binarios para predecir el diagnóstico positivo o no de Diabetes. <br>\n",
    "Si bien los modelos poseen niveles de confianza similares, aunque la red neuronal es algo mejor, se observa que el regresor logístico presenta mejor recall para el diagnóstico positivo: 0.77. Esto nos indica que el clasificador creado por regresión logística detecta mejor a los diagnósticos positivos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c812cd9b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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