ID3_sklearn.ipynb

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        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, f1_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "import math"
      ],
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      "source": [
        "df = pd.read_csv(\"play_tennis.csv\")\n",
        "df = df.sample(frac=1) # (shuffle) modelin öğrenme sürecinde veri sırasının etkisini azaltmak için kullanılır. frac=1, veri setinin tamamını karıştıracağını belirtir.\n",
        "df"
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              "     Outlook Temperature Humidity    Wind Play Tennis\n",
              "10     Sunny        Mild   Normal  Strong         Yes\n",
              "12  Overcast         Hot   Normal    Weak         Yes\n",
              "4       Rain        Cool   Normal    Weak         Yes\n",
              "8      Sunny        Cool   Normal    Weak         Yes\n",
              "1      Sunny         Hot     High  Strong          No\n",
              "11  Overcast        Mild     High  Strong         Yes\n",
              "2   Overcast         Hot     High    Weak         Yes\n",
              "0      Sunny         Hot     High    Weak          No\n",
              "7      Sunny        Mild     High    Weak          No\n",
              "9       Rain        Mild   Normal    Weak         Yes\n",
              "6   Overcast        Cool   Normal  Strong         Yes\n",
              "3       Rain        Mild     High    Weak         Yes\n",
              "13      Rain        Mild     High  Strong          No\n",
              "5       Rain        Cool   Normal  Strong          No"
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              "      <th>2</th>\n",
              "      <td>Overcast</td>\n",
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      "cell_type": "code",
      "source": [
        "X = pd.get_dummies(df.drop('Play Tennis', axis=1))  #get dummies ile hedef özellik dışında kullanacağımız özellikleri sayısal değerlere çeviriyoruz, bunun için numpy array de kullanılabilir ama her özellik için manuel ayarlama yapmak gerekir\n",
        "y = df['Play Tennis']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=101)  #random state ile her çalıştırmada aynı eğitim ve test setini elde ederiz"
      ],
      "metadata": {
        "id": "eQhcKgmK9sBN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dtree = DecisionTreeClassifier(criterion='entropy',max_depth=4)  #overfittting'i engellemek için max derinliği ayarlayabiliriz\n",
        "dtree.fit(X_train,y_train)\n",
        "y_pred = dtree.predict(X_test)"
      ],
      "metadata": {
        "id": "caD0b1xi95kd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Modelin entropilerini ve dallanma bilgilerini görüntüleme\n",
        "importances = dtree.feature_importances_\n",
        "print(f\"Feature Importances:\\n{dict(zip(X.columns, importances))}\")\n",
        "\n",
        "# Karar ağacını görselleştirme\n",
        "import matplotlib.pyplot as plt\n",
        "plot_tree(dtree, feature_names=X.columns, class_names=dtree.classes_, filled=True, rounded=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "ZScSfv_QHA0h",
        "outputId": "4b4a445d-9983-4e67-9746-8257dcbe21a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature Importances:\n",
            "{'Outlook_Overcast': 0.0, 'Outlook_Rain': 0.0, 'Outlook_Sunny': 0.6073508754546143, 'Temperature_Cool': 0.0, 'Temperature_Hot': 0.0, 'Temperature_Mild': 0.0, 'Humidity_High': 0.39264912454538575, 'Humidity_Normal': 0.0, 'Wind_Strong': 0.0, 'Wind_Weak': 0.0}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "precision = precision_score(y_test, y_pred, pos_label=\"Yes\")  # \"Yes\" labelini pozitif sınıf olarak alıyoruz\n",
        "recall = recall_score(y_test, y_pred, pos_label=\"Yes\")\n",
        "f1 = f1_score(y_test, y_pred, pos_label=\"Yes\")\n",
        "\n",
        "print(f\"Sklearn Accuracy: {accuracy}\")  #Accuracy: Doğru tahmin edilen örneklerin toplam örneğe oranıdır\n",
        "print(\"Sklearn Confusion Matrix:\\n\", cm) #TP FP FN TN\n",
        "print(f\"Sklearn Precision: {precision}\") #Pozitif olarak tahmin edilen örneklerin ne kadarının gerçekten pozitif olduğunu gösterir\n",
        "print(f\"Sklearn Recall: {recall}\")  #Recall, true positive örneklerin ne kadarının doğru tahmin edildiğini gösterir\n",
        "print(f\"Sklearn F1-Score: {f1}\") #Precision ve Recall'un harmonik ortalaması"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WM3yik8PBvUp",
        "outputId": "42979a5c-02e2-4a0f-cb25-5895ced1ce92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sklearn Accuracy: 0.6\n",
            "Sklearn Confusion Matrix:\n",
            " [[0 2]\n",
            " [0 3]]\n",
            "Sklearn Precision: 0.6\n",
            "Sklearn Recall: 1.0\n",
            "Sklearn F1-Score: 0.7499999999999999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def custom_accuracy_score(y_true, y_pred):\n",
        "    correct = 0\n",
        "    for true, pred in zip(y_true, y_pred):  #her iki listenin aynı indexlerini birleştirir, böylelikle (true, pred) şeklinde yeni bir liste oluşturmuş oluruz\n",
        "        if true == pred:\n",
        "            correct += 1\n",
        "    accuracy = correct / len(y_true)\n",
        "    return accuracy\n",
        "\n",
        "custom_accuracy = custom_accuracy_score(y_test.tolist(), y_pred.tolist())\n",
        "print(f\"Custom Accuracy: {custom_accuracy}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jqlIUDJMCRNV",
        "outputId": "e4a67244-5ee8-4b52-e4a2-65ededf9c0bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Custom Accuracy: 0.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def custom_confusion_matrix(y_true, y_pred):\n",
        "    labels = ['Yes', 'No']\n",
        "    matrix = {label: {label2: 0 for label2 in labels} for label in labels}\n",
        "\n",
        "    print(\"Initial Confusion Matrix (All zeros):\")\n",
        "    for true_label, preds in matrix.items():\n",
        "        print(f\"{true_label}: {preds}\")\n",
        "\n",
        "    for true, pred in zip(y_true, y_pred):\n",
        "        matrix[true][pred] += 1\n",
        "\n",
        "    return matrix\n",
        "\n",
        "custom_cm = custom_confusion_matrix(y_test.tolist(), y_pred.tolist())\n",
        "print(\"Custom Confusion Matrix:\")\n",
        "for true_label, preds in custom_cm.items():\n",
        "    print(f\"{true_label}: {preds}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9VSUxffrB_mX",
        "outputId": "f7747102-f4d5-44d0-8257-386566dda649"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial Confusion Matrix (All zeros):\n",
            "Yes: {'Yes': 0, 'No': 0}\n",
            "No: {'Yes': 0, 'No': 0}\n",
            "Custom Confusion Matrix:\n",
            "Yes: {'Yes': 3, 'No': 0}\n",
            "No: {'Yes': 2, 'No': 0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def custom_precision(y_true, y_pred, pos_label):\n",
        "    TP = FP = 0\n",
        "    for true, pred in zip(y_true, y_pred):\n",
        "        if true == pos_label and pred == pos_label:\n",
        "            TP += 1  # True Positive\n",
        "        elif true != pos_label and pred == pos_label:\n",
        "            FP += 1  # False Positive\n",
        "    return TP / (TP + FP) if (TP + FP) > 0 else 0\n",
        "\n",
        "def custom_recall(y_true, y_pred, pos_label=\"Yes\"):\n",
        "    TP = FN = 0\n",
        "    for true, pred in zip(y_true, y_pred):\n",
        "        if true == pos_label and pred == pos_label:\n",
        "            TP += 1  # True Positive\n",
        "        elif true == pos_label and pred != pos_label:\n",
        "            FN += 1  # False Negative\n",
        "    return TP / (TP + FN) if (TP + FN) > 0 else 0\n",
        "\n",
        "def custom_f1(y_true, y_pred, pos_label=\"Yes\"):\n",
        "    precision = custom_precision(y_true, y_pred, pos_label)\n",
        "    recall = custom_recall(y_true, y_pred, pos_label)\n",
        "    return 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0\n",
        "\n",
        "custom_precision_value = custom_precision(y_test.tolist(), y_pred.tolist(), \"Yes\")\n",
        "custom_recall_value = custom_recall(y_test.tolist(), y_pred.tolist(), \"Yes\")\n",
        "custom_f1_value = custom_f1(y_test.tolist(), y_pred.tolist(), \"Yes\")\n",
        "\n",
        "print(f\"Custom Precision: {custom_precision_value}\")\n",
        "print(f\"Custom Recall: {custom_recall_value}\")\n",
        "print(f\"Custom F1-Score: {custom_f1_value}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7y0YKLoaDoZc",
        "outputId": "6c606784-7ca4-4aa5-a116-1001306f22df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Custom Precision: 0.6\n",
            "Custom Recall: 1.0\n",
            "Custom F1-Score: 0.7499999999999999\n"
          ]
        }
      ]
    }
  ]
}