Bagging_Boosting.ipynb

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        "<a href=\"https://colab.research.google.com/github/iremgngr/Machine_Learning/blob/main/Bagging_boosting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import cross_val_score"
      ],
      "metadata": {
        "id": "aUparaBYBiIo"
      },
      "execution_count": 33,
      "outputs": []
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      "execution_count": 34,
      "metadata": {
        "id": "nTBNtpiirDOa"
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      "source": [
        "df=pd.read_csv('iris.csv')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.variety=df.variety.astype('category')\n",
        "df.variety=df.variety.cat.codes\n",
        "df"
      ],
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      },
      "execution_count": 35,
      "outputs": [
        {
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            "text/plain": [
              "     sepal.length  sepal.width  petal.length  petal.width  variety\n",
              "0             5.1          3.5           1.4          0.2        0\n",
              "1             4.9          3.0           1.4          0.2        0\n",
              "2             4.7          3.2           1.3          0.2        0\n",
              "3             4.6          3.1           1.5          0.2        0\n",
              "4             5.0          3.6           1.4          0.2        0\n",
              "..            ...          ...           ...          ...      ...\n",
              "145           6.7          3.0           5.2          2.3        2\n",
              "146           6.3          2.5           5.0          1.9        2\n",
              "147           6.5          3.0           5.2          2.0        2\n",
              "148           6.2          3.4           5.4          2.3        2\n",
              "149           5.9          3.0           5.1          1.8        2\n",
              "\n",
              "[150 rows x 5 columns]"
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal.length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal.width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4358662849366982,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal.length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594662,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal.width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"variety\",\n      \"properties\": {\n        \"dtype\": \"int8\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 35
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      "cell_type": "code",
      "source": [
        "X = df.drop('variety', axis=1).values  # sepal.length, sepal.width, petal.length, petal.width\n",
        "y = df['variety'].values"
      ],
      "metadata": {
        "id": "u2r4IewmBgy7"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "VDiovB2PBsKa"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "cv_scores = cross_val_score(rf, X, y, cv=5, scoring='accuracy')\n",
        "\n",
        "# Sonuçları yazdır\n",
        "print(f\"Cross-Validation Scores: {cv_scores}\")\n",
        "print(f\"Average CV Accuracy: {cv_scores.mean()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v8ma3IZ0gg0n",
        "outputId": "e574f43f-988d-4607-c3df-698fb5fb7dd3"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-Validation Scores: [0.96666667 0.96666667 0.93333333 0.96666667 1.        ]\n",
            "Average CV Accuracy: 0.9666666666666668\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#RANDOM FOREST\n",
        "rf = RandomForestClassifier(n_estimators=100, random_state=42, max_depth=2)\n",
        "model = rf.fit(X_train, y_train)\n",
        "\n",
        "y_pred = rf.predict(X_test)\n",
        "rf_score = accuracy_score(y_test, y_pred)\n",
        "print(f\"Random Forest Accuracy: {rf_score}\")\n",
        "model.score(X_test, y_test)\n",
        "print(model.score(X_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q8QygLPlec5p",
        "outputId": "a75b0350-9fc9-4077-f729-cfdda91ecceb"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Accuracy: 1.0\n",
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ADABOOST\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "ada = AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=1), n_estimators=50, random_state=42)\n",
        "ada.fit(X_train, y_train)\n",
        "y_pred = ada.predict(X_test)\n",
        "print(f\"AdaBoost Accuracy: {accuracy_score(y_test, y_pred)}\")\n",
        "\n",
        "cv_scores = cross_val_score(ada, X, y, cv=5, scoring='accuracy')\n",
        "print(f\"Cross-Validation Scores: {cv_scores}\")\n",
        "print(f\"Average CV Accuracy: {cv_scores.mean()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4rM1uqkbCC4D",
        "outputId": "fb502678-0adf-4be1-a64a-41d4686d7303"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AdaBoost Accuracy: 1.0\n",
            "Cross-Validation Scores: [0.96666667 0.93333333 0.9        0.93333333 1.        ]\n",
            "Average CV Accuracy: 0.9466666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#XGBOOST\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "xgb = XGBClassifier(n_estimators=100, random_state=42)\n",
        "xgb.fit(X_train, y_train)\n",
        "\n",
        "y_pred = xgb.predict(X_test)\n",
        "print(f\"XGBoost Accuracy: {accuracy_score(y_test, y_pred)}\")\n",
        "\n",
        "cv_scores = cross_val_score(xgb, X, y, cv=5, scoring='accuracy')\n",
        "print(f\"Cross-Validation Scores: {cv_scores}\")\n",
        "print(f\"Average CV Accuracy: {cv_scores.mean()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZNI3qRuZCIFA",
        "outputId": "16a046d3-6b12-4944-a179-53b3891f2517"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost Accuracy: 1.0\n",
            "Cross-Validation Scores: [0.96666667 0.96666667 0.93333333 0.93333333 0.96666667]\n",
            "Average CV Accuracy: 0.9533333333333334\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Gradient Boosting\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "gb = GradientBoostingClassifier(n_estimators=100, random_state=42)\n",
        "gb.fit(X_train, y_train)\n",
        "\n",
        "y_pred = gb.predict(X_test)\n",
        "print(f\"Gradient Boosting Accuracy: {accuracy_score(y_test, y_pred)}\")\n",
        "\n",
        "cv_scores = cross_val_score(gb, X, y, cv=5, scoring='accuracy')\n",
        "print(f\"Cross-Validation Scores: {cv_scores}\")\n",
        "print(f\"Average CV Accuracy: {cv_scores.mean()}\")"
      ],
      "metadata": {
        "id": "FAbh1MJnDMXu",
        "outputId": "82ea3169-d5c4-4cb1-d8de-35f9ce4c08d2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting Accuracy: 1.0\n",
            "Cross-Validation Scores: [0.96666667 0.96666667 0.9        0.96666667 1.        ]\n",
            "Average CV Accuracy: 0.9600000000000002\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#BAGGING\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "\n",
        "bagging = BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=50, random_state=42)\n",
        "bagging.fit(X_train, y_train)\n",
        "\n",
        "y_pred = bagging.predict(X_test)\n",
        "print(f\"Bagging Accuracy: {accuracy_score(y_test, y_pred)}\")\n",
        "\n",
        "cv_scores = cross_val_score(bagging, X, y, cv=5, scoring='accuracy')\n",
        "print(f\"Cross-Validation Scores: {cv_scores}\")\n",
        "print(f\"Average CV Accuracy: {cv_scores.mean()}\")"
      ],
      "metadata": {
        "id": "XGxGuvcdDPLL",
        "outputId": "e7c1a3e6-d479-4758-d150-ca85adb31df3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bagging Accuracy: 1.0\n",
            "Cross-Validation Scores: [0.96666667 0.96666667 0.93333333 0.96666667 1.        ]\n",
            "Average CV Accuracy: 0.9666666666666668\n"
          ]
        }
      ]
    }
  ]
}