Latihan8_Sklearn_PCA.ipynb

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        "<a href=\"https://colab.research.google.com/github/yandri918/Belajar-python/blob/main/Latihan8_Sklearn_PCA.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": "code",
      "metadata": {
        "id": "snyx5YV2ygwM"
      },
      "source": [
        "from sklearn import datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "iris = datasets.load_iris()\n",
        "atribut = iris.data\n",
        "label = iris.target\n",
        "\n",
        "# bagi dataset menjadi train set dan test set\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    atribut, label, test_size=0.2, random_state=1)"
      ],
      "execution_count": 1,
      "outputs": []
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    {
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        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn import tree\n",
        "\n",
        "# menghitung akurasi model tanpa PCA\n",
        "decision_tree = tree.DecisionTreeClassifier()\n",
        "model_pertama = decision_tree.fit(X_train, y_train)\n",
        "model_pertama.score(X_test, y_test)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "0.9666666666666667"
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          "metadata": {},
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        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# membuat objek PCA dengan 4 principal component\n",
        "pca = PCA(n_components=4)\n",
        "\n",
        "# mengaplikasikan PCA pada dataset\n",
        "pca_attributes = pca.fit_transform(X_train)\n",
        "\n",
        "# melihat variance dari setiap atribut\n",
        "pca.explained_variance_ratio_"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.92848323, 0.04764372, 0.01931005, 0.004563  ])"
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          "metadata": {},
          "execution_count": 3
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      ]
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    {
      "cell_type": "code",
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        "id": "2ytSpsehyvpZ"
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      "source": [
        "# PCA dengan 2 principal component\n",
        "pca = PCA(n_components = 2)\n",
        "X_train_pca = pca.fit_transform(X_train)\n",
        "X_test_pca = pca.fit_transform(X_test)"
      ],
      "execution_count": 4,
      "outputs": []
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    {
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      },
      "source": [
        "# uji akurasi classifier\n",
        "\n",
        "model2 = decision_tree.fit(X_train_pca, y_train)\n",
        "model2.score(X_test_pca, y_test)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "0.9333333333333333"
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          "metadata": {},
          "execution_count": 5
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