flyfir248/random-forrrest.ipynb

## random-forrrest.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNueuqyrFlIxWcd6ndBgFd3",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/flyfir248/c65a903699b46c5ecdc70f66b17672e3/random-forrrest.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4HMcVMZMLV0m",
        "outputId": "d74b922e-c680-4adf-cade-6a83b58613b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.0\n"
          ]
        }
      ],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Load the iris dataset\n",
        "iris = load_iris()\n",
        "X = iris.data\n",
        "y = iris.target\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "\n",
        "# Create an instance of the RandomForestClassifier class with 100 trees\n",
        "rfc = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "# Fit the classifier to the training data\n",
        "rfc.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the testing data\n",
        "y_pred = rfc.predict(X_test)\n",
        "\n",
        "# Evaluate the accuracy of the classifier\n",
        "acc = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", acc)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Load the Iris dataset\n",
        "iris = load_iris()\n",
        "X = iris.data\n",
        "y = iris.target\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "\n",
        "# Create an instance of the RandomForestClassifier class\n",
        "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "# Fit the classifier to the training data\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the testing data\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "# Evaluate the accuracy of the classifier\n",
        "acc = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", acc)\n",
        "\n",
        "# Plot the confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "sns.heatmap(cm, annot=True, cmap='Blues')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n",
        "\n",
        "# Plot the feature importances\n",
        "feature_importances = clf.feature_importances_\n",
        "sns.barplot(x=feature_importances, y=iris.feature_names)\n",
        "plt.xlabel('Feature Importance Score')\n",
        "plt.ylabel('Features')\n",
        "plt.title('Feature Importances')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 944
        },
        "id": "B_JbXWtPLY1z",
        "outputId": "92f84ebe-ee32-40e8-8eb2-d953a04844b2"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}