006.ipynb

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  "metadata": {
    "colab": {
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      "authorship_tag": "ABX9TyNjZyhqz6qyOb1fwb9yB2W0",
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      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/johnsanterre/santerreAI/blob/main/006.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tutorial 6: RF versus XGBoost\n"
      ],
      "metadata": {
        "id": "bxKxFQEXlLUN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jkk8cn-vlIvD"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.datasets import load_diabetes\n",
        "\n",
        "\n",
        "X, y = load_diabetes(return_X_y=True)\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Train a random forest classifier\n",
        "rf = RandomForestRegressor()\n",
        "rf.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "score_rf = rf.score(X_test, y_test)\n",
        "\n",
        "# Train an XGBoost classifier\n",
        "xgb = XGBRegressor()\n",
        "xgb.fit(X_train, y_train)\n",
        "score_xgb = xgb.score(X_test, y_test)\n",
        "\n",
        "\n",
        "# Print results\n",
        "print(\"Accuracy of Random Forest:\", score_rf)\n",
        "print(\"Accuracy of XGBoost:\", score_xgb)\n",
        "\n"
      ]
    }
  ]
}