Naive_Bayes_iris.ipynb

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  "metadata": {
    "colab": {
      "name": "Naive_Bayes_iris.ipynb",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/PauloAAlmeida/Python/blob/master/Naive_Bayes_iris.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2r8uNeTlxVSN"
      },
      "source": [
        "# Naïve Bayes\n",
        "\n",
        "\n",
        "https://archive.ics.uci.edu/ml/datasets/Iris/\n",
        "\n",
        "3 classes (setosa, virginica, versicolor)\n",
        "\n",
        "50 amostras por classe\n",
        "\n",
        "4 atributos reais positivos (comp. pétala, comp. sépala, larg. pétala, larg. sépala)\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jdme4B9eNngv"
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "\n",
        "\n",
        "iris = load_iris()\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "                                iris['data'], iris['target'], random_state=0)\n",
        "\n",
        "\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mUiiCok3xulx",
        "outputId": "87a5c616-a944-47e1-c491-a1965eeb171c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        }
      },
      "source": [
        "gnb = GaussianNB()\n",
        "y_pred = gnb.fit(X_train, y_train).predict(X_test)\n",
        "\n",
        "print(\"Número de erros de classificação {0} de {1}\"\n",
        "      .format((y_test != y_pred).sum(), iris.data.shape[0]))\n",
        "\n",
        "ac = gnb.score(X_test, y_test)\n",
        "\n",
        "print(\"\\nAcurácia do modelo: {0:.2f}%\\n\".format(100*ac))\n",
        "\n",
        "print(classification_report(y_test, y_pred, target_names=iris.target_names))\n",
        "\n",
        "cnf_matrix = confusion_matrix(y_test, y_pred)\n",
        "cnf_table = pd.DataFrame(data=cnf_matrix, index=iris.target_names, columns=[x + \"(prev)\" for x in iris.target_names])\n",
        "print(cnf_table)\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Número de erros de classificação 0 de 150\n",
            "\n",
            "Acurácia do modelo: 100.00%\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      setosa       1.00      1.00      1.00        13\n",
            "  versicolor       1.00      1.00      1.00        16\n",
            "   virginica       1.00      1.00      1.00         9\n",
            "\n",
            "    accuracy                           1.00        38\n",
            "   macro avg       1.00      1.00      1.00        38\n",
            "weighted avg       1.00      1.00      1.00        38\n",
            "\n",
            "            setosa(prev)  versicolor(prev)  virginica(prev)\n",
            "setosa                13                 0                0\n",
            "versicolor             0                16                0\n",
            "virginica              0                 0                9\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}