06_decision_trees.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p3968sUOY5Xu"
      },
      "source": [
        "**6장 – 결정 트리**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YIR0zb7yY5Xx"
      },
      "source": [
        "_이 노트북에는 6장의 연습 문제에 대한 모든 샘플 코드와 솔루션이 포함되어 있습니다._"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aCJUBEqgY5Xx"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a href=\"https://colab.research.google.com/github/rickiepark/handson-ml3/blob/main/06_decision_trees.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "tags": [],
        "id": "StbkI7c6Y5Xy"
      },
      "source": [
        "# 설정"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1xsOqIt2Y5Xy"
      },
      "source": [
        "이 프로젝트에는 파이썬 3.7 이상이 필요합니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "uf8yFxpqY5Xy"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "\n",
        "assert sys.version_info >= (3, 7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fEMrPF1OY5Xz"
      },
      "source": [
        "또한 사이킷런 ≥ 1.0.1이 필요합니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "roDidmCJY5Xz"
      },
      "outputs": [],
      "source": [
        "from packaging import version\n",
        "import sklearn\n",
        "\n",
        "assert version.parse(sklearn.__version__) >= version.parse(\"1.0.1\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0uzgYPGLY5X0"
      },
      "source": [
        "이전 챕터에서와 마찬가지로 기본 글꼴 크기를 정의하여 그림을 더 예쁘게 만들어 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "UrmsG4g8Y5X0"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rc('font', size=14)\n",
        "plt.rc('axes', labelsize=14, titlesize=14)\n",
        "plt.rc('legend', fontsize=14)\n",
        "plt.rc('xtick', labelsize=10)\n",
        "plt.rc('ytick', labelsize=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9DHCP-sHY5X0"
      },
      "source": [
        "그리고 `images/decision_trees` 폴더를 만들고(아직 존재하지 않는 경우), 이 노트북을 통해 책에 사용할 그림을 고해상도로 저장하는 데 사용되는 `save_fig()` 함수를 정의해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "W9DfjLYVY5X0"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "\n",
        "IMAGES_PATH = Path() / \"images\" / \"decision_trees\"\n",
        "IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
        "    path = IMAGES_PATH / f\"{fig_id}.{fig_extension}\"\n",
        "    if tight_layout:\n",
        "        plt.tight_layout()\n",
        "    plt.savefig(path, format=fig_extension, dpi=resolution)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9l5TwkfY5X0"
      },
      "source": [
        "# 결정 트리 훈련과 시각화"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "bGhDRJKTY5X1",
        "outputId": "a0b79c63-7e08-4840-f4f1-68a24dbc3607",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=2, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=2, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "iris = load_iris(as_frame=True)\n",
        "X_iris = iris.data[[\"petal length (cm)\", \"petal width (cm)\"]].values\n",
        "y_iris = iris.target\n",
        "\n",
        "tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42)\n",
        "tree_clf.fit(X_iris, y_iris)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60RHX1LdY5X1"
      },
      "source": [
        "**이 코드 예제는 그림 6-1 붓꽃 결정 트리를 생성합니다:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "ClGEfRkSY5X1"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import export_graphviz\n",
        "\n",
        "export_graphviz(\n",
        "        tree_clf,\n",
        "        out_file=str(IMAGES_PATH / \"iris_tree.dot\"),  # 경로가 책과 다릅니다.\n",
        "        feature_names=[\"petal length (cm)\", \"petal width (cm)\"],\n",
        "        class_names=iris.target_names,\n",
        "        rounded=True,\n",
        "        filled=True\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Mu8Pzy8vY5X1",
        "outputId": "87bb380a-38a4-4943-fb41-3f7c98db2477",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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215.5,-306 209.5,-306\"/>\n<text text-anchor=\"middle\" x=\"137.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) &lt;= 2.45</text>\n<text text-anchor=\"middle\" x=\"137.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.667</text>\n<text text-anchor=\"middle\" x=\"137.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 150</text>\n<text text-anchor=\"middle\" x=\"137.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [50, 50, 50]</text>\n<text text-anchor=\"middle\" x=\"137.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = setosa</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<path fill=\"#e58139\" stroke=\"black\" d=\"M105,-179.5C105,-179.5 12,-179.5 12,-179.5 6,-179.5 0,-173.5 0,-167.5 0,-167.5 0,-123.5 0,-123.5 0,-117.5 6,-111.5 12,-111.5 12,-111.5 105,-111.5 105,-111.5 111,-111.5 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font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">True</text>\n</g>\n<!-- 2 -->\n<g id=\"node3\" class=\"node\">\n<title>2</title>\n<path fill=\"#ffffff\" stroke=\"black\" d=\"M286,-187C286,-187 147,-187 147,-187 141,-187 135,-181 135,-175 135,-175 135,-116 135,-116 135,-110 141,-104 147,-104 147,-104 286,-104 286,-104 292,-104 298,-110 298,-116 298,-116 298,-175 298,-175 298,-181 292,-187 286,-187\"/>\n<text text-anchor=\"middle\" x=\"216.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal width (cm) &lt;= 1.75</text>\n<text text-anchor=\"middle\" x=\"216.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n<text text-anchor=\"middle\" x=\"216.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 100</text>\n<text text-anchor=\"middle\" x=\"216.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 50, 50]</text>\n<text text-anchor=\"middle\" x=\"216.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n</g>\n<!-- 0&#45;&gt;2 -->\n<g id=\"edge2\" class=\"edge\">\n<title>0&#45;&gt;2</title>\n<path fill=\"none\" stroke=\"black\" d=\"M164.91,-222.91C170.91,-214.01 177.33,-204.51 183.53,-195.33\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"186.44,-197.27 189.14,-187.02 180.64,-193.35 186.44,-197.27\"/>\n<text text-anchor=\"middle\" x=\"193.9\" y=\"-207.86\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\n</g>\n<!-- 3 -->\n<g id=\"node4\" class=\"node\">\n<title>3</title>\n<path fill=\"#4de88e\" stroke=\"black\" d=\"M196,-68C196,-68 99,-68 99,-68 93,-68 87,-62 87,-56 87,-56 87,-12 87,-12 87,-6 93,0 99,0 99,0 196,0 196,0 202,0 208,-6 208,-12 208,-12 208,-56 208,-56 208,-62 202,-68 196,-68\"/>\n<text text-anchor=\"middle\" x=\"147.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.168</text>\n<text text-anchor=\"middle\" x=\"147.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 54</text>\n<text text-anchor=\"middle\" x=\"147.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 49, 5]</text>\n<text text-anchor=\"middle\" x=\"147.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n</g>\n<!-- 2&#45;&gt;3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>2&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"black\" d=\"M190.81,-103.73C185.29,-94.97 179.45,-85.7 173.91,-76.91\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"176.78,-74.89 168.48,-68.3 170.85,-78.63 176.78,-74.89\"/>\n</g>\n<!-- 4 -->\n<g id=\"node5\" class=\"node\">\n<title>4</title>\n<path fill=\"#843de6\" stroke=\"black\" d=\"M331,-68C331,-68 238,-68 238,-68 232,-68 226,-62 226,-56 226,-56 226,-12 226,-12 226,-6 232,0 238,0 238,0 331,0 331,0 337,0 343,-6 343,-12 343,-12 343,-56 343,-56 343,-62 337,-68 331,-68\"/>\n<text text-anchor=\"middle\" x=\"284.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.043</text>\n<text text-anchor=\"middle\" x=\"284.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 46</text>\n<text text-anchor=\"middle\" x=\"284.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1, 45]</text>\n<text text-anchor=\"middle\" x=\"284.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n</g>\n<!-- 2&#45;&gt;4 -->\n<g id=\"edge4\" class=\"edge\">\n<title>2&#45;&gt;4</title>\n<path fill=\"none\" stroke=\"black\" d=\"M241.82,-103.73C247.26,-94.97 253.01,-85.7 258.48,-76.91\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"261.52,-78.64 263.82,-68.3 255.57,-74.95 261.52,-78.64\"/>\n</g>\n</g>\n</svg>\n",
            "text/plain": [
              "<graphviz.sources.Source at 0x7b7a3bc21a50>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "from graphviz import Source\n",
        "\n",
        "Source.from_file(IMAGES_PATH / \"iris_tree.dot\")  # 경로가 책과 다릅니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNL_jfasY5X2"
      },
      "source": [
        "Graphviz는 `.dot` 파일을 다양한 포맷으로 변환할 수 있는 `dot` 명령줄 도구도 제공합니다. 다음 명령은 dot 파일을 png 이미지로 변환합니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "Q9jMZIk8Y5X2"
      },
      "outputs": [],
      "source": [
        "# 추가 코드\n",
        "!dot -Tpng {IMAGES_PATH / \"iris_tree.dot\"} -o {IMAGES_PATH / \"iris_tree.png\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dnYRJ1CRY5X2"
      },
      "source": [
        "# 예측하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "fG7QhiPNY5X2",
        "outputId": "770e35b6-8880-45b5-8687-9e44c42a498b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 추가 코드 - 세부 서식 지정\n",
        "from matplotlib.colors import ListedColormap\n",
        "custom_cmap = ListedColormap(['#fafab0', '#9898ff', '#a0faa0'])\n",
        "plt.figure(figsize=(8, 4))\n",
        "\n",
        "lengths, widths = np.meshgrid(np.linspace(0, 7.2, 100), np.linspace(0, 3, 100))\n",
        "X_iris_all = np.c_[lengths.ravel(), widths.ravel()]\n",
        "y_pred = tree_clf.predict(X_iris_all).reshape(lengths.shape)\n",
        "plt.contourf(lengths, widths, y_pred, alpha=0.3, cmap=custom_cmap)\n",
        "for idx, (name, style) in enumerate(zip(iris.target_names, (\"yo\", \"bs\", \"g^\"))):\n",
        "    plt.plot(X_iris[:, 0][y_iris == idx], X_iris[:, 1][y_iris == idx],\n",
        "             style, label=f\"Iris {name}\")\n",
        "\n",
        "# 추가 코드 - 이 섹션에서는 그림 6-2를 아름답게 꾸미고 저장합니다.\n",
        "tree_clf_deeper = DecisionTreeClassifier(max_depth=3, random_state=42)\n",
        "tree_clf_deeper.fit(X_iris, y_iris)\n",
        "th0, th1, th2a, th2b = tree_clf_deeper.tree_.threshold[[0, 2, 3, 6]]\n",
        "plt.xlabel(\"Petal length (cm)\")\n",
        "plt.ylabel(\"Petal width (cm)\")\n",
        "plt.plot([th0, th0], [0, 3], \"k-\", linewidth=2)\n",
        "plt.plot([th0, 7.2], [th1, th1], \"k--\", linewidth=2)\n",
        "plt.plot([th2a, th2a], [0, th1], \"k:\", linewidth=2)\n",
        "plt.plot([th2b, th2b], [th1, 3], \"k:\", linewidth=2)\n",
        "plt.text(th0 - 0.05, 1.0, \"Depth=0\", horizontalalignment=\"right\", fontsize=15)\n",
        "plt.text(3.2, th1 + 0.02, \"Depth=1\", verticalalignment=\"bottom\", fontsize=13)\n",
        "plt.text(th2a + 0.05, 0.5, \"(Depth=2)\", fontsize=11)\n",
        "plt.axis([0, 7.2, 0, 3])\n",
        "plt.legend()\n",
        "save_fig(\"decision_tree_decision_boundaries_plot\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UeMgwarFY5X2"
      },
      "source": [
        "트리 구조는 `tree_` 속성을 통해 액세스할 수 있습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "sISLC_VIY5X2",
        "outputId": "83d8734e-be23-446f-8f51-37ba330d3251",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.tree._tree.Tree at 0x7b7a3bbe78a0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "tree_clf.tree_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5Vvy4BNY5X2"
      },
      "source": [
        "자세한 내용은 이 클래스의 문서를 확인하세요:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "tags": [],
        "id": "6zrAXiLRY5X3"
      },
      "outputs": [],
      "source": [
        "# help(sklearn.tree._tree.Tree)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XhcTS4MqY5X3"
      },
      "source": [
        "예시로 아래의 추가 자료 섹션을 참조하세요."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wwWMLXIvY5X3"
      },
      "source": [
        "# 클래스 확률 추정"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "OXHWLWkjY5X3",
        "outputId": "12dbd96a-65f0-4530-d964-f4361c87e7a5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.   , 0.907, 0.093]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "tree_clf.predict_proba([[5, 1.5]]).round(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "ts7hzuYKY5X3",
        "outputId": "7ab88ef6-c16f-420d-f39f-04b9a21e6a52",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "tree_clf.predict([[5, 1.5]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nEi0LABHY5X3"
      },
      "source": [
        "# 규제 매개변수"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "f9-aRQetY5X3",
        "outputId": "a6e2a69a-5c68-4059-82d2-49585835e65e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(min_samples_leaf=5, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(min_samples_leaf=5, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(min_samples_leaf=5, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "from sklearn.datasets import make_moons\n",
        "\n",
        "X_moons, y_moons = make_moons(n_samples=150, noise=0.2, random_state=42)\n",
        "\n",
        "tree_clf1 = DecisionTreeClassifier(random_state=42)\n",
        "tree_clf2 = DecisionTreeClassifier(min_samples_leaf=5, random_state=42)\n",
        "tree_clf1.fit(X_moons, y_moons)\n",
        "tree_clf2.fit(X_moons, y_moons)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "GOw9DAw-Y5X4",
        "outputId": "09f2b669-bc3b-4549-ed44-bbbdecf16eea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 추가 코드 - 이 셀은 그림 6-3을 생성하고 저장합니다.\n",
        "\n",
        "def plot_decision_boundary(clf, X, y, axes, cmap):\n",
        "    x1, x2 = np.meshgrid(np.linspace(axes[0], axes[1], 100),\n",
        "                         np.linspace(axes[2], axes[3], 100))\n",
        "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
        "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
        "\n",
        "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=cmap)\n",
        "    plt.contour(x1, x2, y_pred, cmap=\"Greys\", alpha=0.8)\n",
        "    colors = {\"Wistia\": [\"#78785c\", \"#c47b27\"], \"Pastel1\": [\"red\", \"blue\"]}\n",
        "    markers = (\"o\", \"^\")\n",
        "    for idx in (0, 1):\n",
        "        plt.plot(X[:, 0][y == idx], X[:, 1][y == idx],\n",
        "                 color=colors[cmap][idx], marker=markers[idx], linestyle=\"none\")\n",
        "    plt.axis(axes)\n",
        "    plt.xlabel(r\"$x_1$\")\n",
        "    plt.ylabel(r\"$x_2$\", rotation=0)\n",
        "\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
        "plt.sca(axes[0])\n",
        "plot_decision_boundary(tree_clf1, X_moons, y_moons,\n",
        "                       axes=[-1.5, 2.4, -1, 1.5], cmap=\"Wistia\")\n",
        "plt.title(\"No restrictions\")\n",
        "plt.sca(axes[1])\n",
        "plot_decision_boundary(tree_clf2, X_moons, y_moons,\n",
        "                       axes=[-1.5, 2.4, -1, 1.5], cmap=\"Wistia\")\n",
        "plt.title(f\"min_samples_leaf = {tree_clf2.min_samples_leaf}\")\n",
        "plt.ylabel(\"\")\n",
        "save_fig(\"min_samples_leaf_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "TM24tQYQY5X4",
        "outputId": "5ef59bed-ce32-40c4-bd0c-bb496fed3a72",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.898"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "X_moons_test, y_moons_test = make_moons(n_samples=1000, noise=0.2,\n",
        "                                        random_state=43)\n",
        "tree_clf1.score(X_moons_test, y_moons_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "o4m-boWzY5X4",
        "outputId": "07d741d4-01f8-4029-d750-a4b7a6ec7062",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.92"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "tree_clf2.score(X_moons_test, y_moons_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7k1bekz8Y5X4"
      },
      "source": [
        "# 회귀"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0nh67MhjY5X4"
      },
      "source": [
        "간단한 2차방정식 훈련 세트를 준비해 보겠습니다:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-dTaOtNHY5X4"
      },
      "source": [
        "**코드 샘플:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "LTjd55eCY5X4",
        "outputId": "fba2b2cd-f920-4e29-a17d-b1325b88b0b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(max_depth=2, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "np.random.seed(42)\n",
        "X_quad = np.random.rand(200, 1) - 0.5  # 간단한 랜덤한 입력 특성\n",
        "y_quad = X_quad ** 2 + 0.025 * np.random.randn(200, 1)\n",
        "\n",
        "tree_reg = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
        "tree_reg.fit(X_quad, y_quad)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "bxs4I-GEY5X5",
        "outputId": "2de55d4f-5f5d-43dc-8a04-e7b96b8d63be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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text-anchor=\"middle\" x=\"328.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x1 &lt;= &#45;0.303</text>\n<text text-anchor=\"middle\" x=\"328.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">squared_error = 0.006</text>\n<text text-anchor=\"middle\" x=\"328.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 200</text>\n<text text-anchor=\"middle\" x=\"328.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.088</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<path fill=\"#eb9d65\" stroke=\"black\" d=\"M308,-157C308,-157 181,-157 181,-157 175,-157 169,-151 169,-145 169,-145 169,-101 169,-101 169,-95 175,-89 181,-89 181,-89 308,-89 308,-89 314,-89 320,-95 320,-101 320,-101 320,-145 320,-145 320,-151 314,-157 308,-157\"/>\n<text text-anchor=\"middle\" x=\"244.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x1 &lt;= 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stroke=\"black\" d=\"M472.61,-88.95C490.52,-78.93 510.11,-67.98 527.73,-58.13\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"529.72,-61.02 536.74,-53.09 526.31,-54.91 529.72,-61.02\"/>\n</g>\n</g>\n</svg>\n",
            "text/plain": [
              "<graphviz.sources.Source at 0x7b7a37ec1db0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# 추가 코드 - export_graphviz()를 사용하는 방법을 이미 살펴봤습니다.\n",
        "export_graphviz(\n",
        "    tree_reg,\n",
        "    out_file=str(IMAGES_PATH / \"regression_tree.dot\"),\n",
        "    feature_names=[\"x1\"],\n",
        "    rounded=True,\n",
        "    filled=True\n",
        ")\n",
        "Source.from_file(IMAGES_PATH / \"regression_tree.dot\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "2lmI_zNTY5X8",
        "outputId": "c1e57d90-e63b-46c3-d24e-fbe8af3d7dcc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(max_depth=3, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=3, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=3, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "tree_reg2 = DecisionTreeRegressor(max_depth=3, random_state=42)\n",
        "tree_reg2.fit(X_quad, y_quad)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "MREEELyFY5X8",
        "outputId": "9a3ee452-9374-448c-c3b9-00f4830ef1ff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.30265072, -0.40830374, -2.        , -2.        ,  0.27175756,\n",
              "       -2.        , -2.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "tree_reg.tree_.threshold"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "VODRXpw4Y5X9",
        "outputId": "ba8bb013-8865-479b-e3ee-87ddc45df99b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.30265072, -0.40830374, -0.45416115, -2.        , -2.        ,\n",
              "       -0.37022041, -2.        , -2.        ,  0.27175756, -0.21270403,\n",
              "       -2.        , -2.        ,  0.40399227, -2.        , -2.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "tree_reg2.tree_.threshold"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "lHSfW8mLY5X9",
        "outputId": "9eebecea-6eb1-4f7e-c40e-d3b2527838b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 추가 코드 - 이 셀은 그림 6-5를 생성하고 저장합니다.\n",
        "\n",
        "def plot_regression_predictions(tree_reg, X, y, axes=[-0.5, 0.5, -0.05, 0.25]):\n",
        "    x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\n",
        "    y_pred = tree_reg.predict(x1)\n",
        "    plt.axis(axes)\n",
        "    plt.xlabel(\"$x_1$\")\n",
        "    plt.plot(X, y, \"b.\")\n",
        "    plt.plot(x1, y_pred, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
        "\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
        "plt.sca(axes[0])\n",
        "plot_regression_predictions(tree_reg, X_quad, y_quad)\n",
        "\n",
        "th0, th1a, th1b = tree_reg.tree_.threshold[[0, 1, 4]]\n",
        "for split, style in ((th0, \"k-\"), (th1a, \"k--\"), (th1b, \"k--\")):\n",
        "    plt.plot([split, split], [-0.05, 0.25], style, linewidth=2)\n",
        "plt.text(th0, 0.16, \"Depth=0\", fontsize=15)\n",
        "plt.text(th1a + 0.01, -0.01, \"Depth=1\", horizontalalignment=\"center\", fontsize=13)\n",
        "plt.text(th1b + 0.01, -0.01, \"Depth=1\", fontsize=13)\n",
        "plt.ylabel(\"$y$\", rotation=0)\n",
        "plt.legend(loc=\"upper center\", fontsize=16)\n",
        "plt.title(\"max_depth=2\")\n",
        "\n",
        "plt.sca(axes[1])\n",
        "th2s = tree_reg2.tree_.threshold[[2, 5, 9, 12]]\n",
        "plot_regression_predictions(tree_reg2, X_quad, y_quad)\n",
        "for split, style in ((th0, \"k-\"), (th1a, \"k--\"), (th1b, \"k--\")):\n",
        "    plt.plot([split, split], [-0.05, 0.25], style, linewidth=2)\n",
        "for split in th2s:\n",
        "    plt.plot([split, split], [-0.05, 0.25], \"k:\", linewidth=1)\n",
        "plt.text(th2s[2] + 0.01, 0.15, \"Depth=2\", fontsize=13)\n",
        "plt.title(\"max_depth=3\")\n",
        "\n",
        "save_fig(\"tree_regression_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "DfYSHqT6Y5X9",
        "outputId": "470bfeb6-642a-454d-b470-7ab7771cfcc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 추가 코드 - 이 셀은 그림 6-6을 생성하고 저장합니다.\n",
        "\n",
        "tree_reg1 = DecisionTreeRegressor(random_state=42)\n",
        "tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n",
        "tree_reg1.fit(X_quad, y_quad)\n",
        "tree_reg2.fit(X_quad, y_quad)\n",
        "\n",
        "x1 = np.linspace(-0.5, 0.5, 500).reshape(-1, 1)\n",
        "y_pred1 = tree_reg1.predict(x1)\n",
        "y_pred2 = tree_reg2.predict(x1)\n",
        "\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
        "\n",
        "plt.sca(axes[0])\n",
        "plt.plot(X_quad, y_quad, \"b.\")\n",
        "plt.plot(x1, y_pred1, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
        "plt.axis([-0.5, 0.5, -0.05, 0.25])\n",
        "plt.xlabel(\"$x_1$\")\n",
        "plt.ylabel(\"$y$\", rotation=0)\n",
        "plt.legend(loc=\"upper center\")\n",
        "plt.title(\"No restrictions\")\n",
        "\n",
        "plt.sca(axes[1])\n",
        "plt.plot(X_quad, y_quad, \"b.\")\n",
        "plt.plot(x1, y_pred2, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
        "plt.axis([-0.5, 0.5, -0.05, 0.25])\n",
        "plt.xlabel(\"$x_1$\")\n",
        "plt.title(f\"min_samples_leaf={tree_reg2.min_samples_leaf}\")\n",
        "\n",
        "save_fig(\"tree_regression_regularization_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wO6ZtO4zY5X9"
      },
      "source": [
        "# 축 방향에 대한 민감성"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUVWiTWZY5X9"
      },
      "source": [
        "데이터셋을 회전하면 결정 경계가 완전히 달라집니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "gZWZbp9NY5X9",
        "outputId": "9bd3eb49-e597-4208-c815-32047639c44a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 추가 코드 - 이 셀은 그림 6-7을 생성하고 저장합니다.\n",
        "\n",
        "np.random.seed(6)\n",
        "X_square = np.random.rand(100, 2) - 0.5\n",
        "y_square = (X_square[:, 0] > 0).astype(np.int64)\n",
        "\n",
        "angle = np.pi / 4  # 45 도\n",
        "rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)],\n",
        "                            [np.sin(angle), np.cos(angle)]])\n",
        "X_rotated_square = X_square.dot(rotation_matrix)\n",
        "\n",
        "tree_clf_square = DecisionTreeClassifier(random_state=42)\n",
        "tree_clf_square.fit(X_square, y_square)\n",
        "tree_clf_rotated_square = DecisionTreeClassifier(random_state=42)\n",
        "tree_clf_rotated_square.fit(X_rotated_square, y_square)\n",
        "\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
        "plt.sca(axes[0])\n",
        "plot_decision_boundary(tree_clf_square, X_square, y_square,\n",
        "                       axes=[-0.7, 0.7, -0.7, 0.7], cmap=\"Pastel1\")\n",
        "plt.sca(axes[1])\n",
        "plot_decision_boundary(tree_clf_rotated_square, X_rotated_square, y_square,\n",
        "                       axes=[-0.7, 0.7, -0.7, 0.7], cmap=\"Pastel1\")\n",
        "plt.ylabel(\"\")\n",
        "\n",
        "save_fig(\"sensitivity_to_rotation_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "JRK1JtDzY5X-",
        "outputId": "3569ff02-6184-49f0-e156-5aca1faabe05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=2, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=2, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "from sklearn.decomposition import PCA\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "pca_pipeline = make_pipeline(StandardScaler(), PCA())\n",
        "X_iris_rotated = pca_pipeline.fit_transform(X_iris)\n",
        "tree_clf_pca = DecisionTreeClassifier(max_depth=2, random_state=42)\n",
        "tree_clf_pca.fit(X_iris_rotated, y_iris)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "HkUqN6XgY5X-",
        "outputId": "4b053076-94e0-47b1-d46f-574c8fc05b85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 추가 코드 - 이 셀은 그림 6-8을 생성하고 저장합니다.\n",
        "\n",
        "plt.figure(figsize=(8, 4))\n",
        "\n",
        "axes = [-2.2, 2.4, -0.6, 0.7]\n",
        "z0s, z1s = np.meshgrid(np.linspace(axes[0], axes[1], 100),\n",
        "                       np.linspace(axes[2], axes[3], 100))\n",
        "X_iris_pca_all = np.c_[z0s.ravel(), z1s.ravel()]\n",
        "y_pred = tree_clf_pca.predict(X_iris_pca_all).reshape(z0s.shape)\n",
        "\n",
        "plt.contourf(z0s, z1s, y_pred, alpha=0.3, cmap=custom_cmap)\n",
        "for idx, (name, style) in enumerate(zip(iris.target_names, (\"yo\", \"bs\", \"g^\"))):\n",
        "    plt.plot(X_iris_rotated[:, 0][y_iris == idx],\n",
        "             X_iris_rotated[:, 1][y_iris == idx],\n",
        "             style, label=f\"Iris {name}\")\n",
        "\n",
        "plt.xlabel(\"$z_1$\")\n",
        "plt.ylabel(\"$z_2$\", rotation=0)\n",
        "th1, th2 = tree_clf_pca.tree_.threshold[[0, 2]]\n",
        "plt.plot([th1, th1], axes[2:], \"k-\", linewidth=2)\n",
        "plt.plot([th2, th2], axes[2:], \"k--\", linewidth=2)\n",
        "plt.text(th1 - 0.01, axes[2] + 0.05, \"Depth=0\",\n",
        "         horizontalalignment=\"right\", fontsize=15)\n",
        "plt.text(th2 - 0.01, axes[2] + 0.05, \"Depth=1\",\n",
        "         horizontalalignment=\"right\", fontsize=13)\n",
        "plt.axis(axes)\n",
        "plt.legend(loc=(0.32, 0.67))\n",
        "save_fig(\"pca_preprocessing_plot\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MIcb_KhUY5X_"
      },
      "source": [
        "# 결정 트리의 분산 문제"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hk5u3G5PY5X_"
      },
      "source": [
        "데이터셋의 작은 변화(예를 들면, 회전)로 인해 매우 다른 결정 트리가 생성될 수 있음을 살펴보았습니다. 사이킷런에서 사용하는 CART 학습 알고리즘은 확률적이기 때문에 동일한 데이터에 대해 동일한 모델을 학습하더라도 매번 매우 다른 모델이 생성될 수 있습니다. 이를 보여주기 위해 `random_state`를 이전과 다른 값으로 설정하겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "jMUFlEI5Y5X_",
        "outputId": "e9fcf73f-bd5c-4346-e3f0-3bb5c495e7b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=2, random_state=40)"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2, random_state=40)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=2, random_state=40)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "tree_clf_tweaked = DecisionTreeClassifier(max_depth=2, random_state=40)\n",
        "tree_clf_tweaked.fit(X_iris, y_iris)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "4SX2sRy0Y5X_",
        "outputId": "da207b42-b3e3-4003-88ee-78fad6494099",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 추가 코드 - 이 셀은 그림 6-9를 생성하고 저장합니다.\n",
        "\n",
        "plt.figure(figsize=(8, 4))\n",
        "y_pred = tree_clf_tweaked.predict(X_iris_all).reshape(lengths.shape)\n",
        "plt.contourf(lengths, widths, y_pred, alpha=0.3, cmap=custom_cmap)\n",
        "\n",
        "for idx, (name, style) in enumerate(zip(iris.target_names, (\"yo\", \"bs\", \"g^\"))):\n",
        "    plt.plot(X_iris[:, 0][y_iris == idx], X_iris[:, 1][y_iris == idx],\n",
        "             style, label=f\"Iris {name}\")\n",
        "\n",
        "th0, th1 = tree_clf_tweaked.tree_.threshold[[0, 2]]\n",
        "plt.plot([0, 7.2], [th0, th0], \"k-\", linewidth=2)\n",
        "plt.plot([0, 7.2], [th1, th1], \"k--\", linewidth=2)\n",
        "plt.text(1.8, th0 + 0.05, \"Depth=0\", verticalalignment=\"bottom\", fontsize=15)\n",
        "plt.text(2.3, th1 + 0.05, \"Depth=1\", verticalalignment=\"bottom\", fontsize=13)\n",
        "plt.xlabel(\"Petal length (cm)\")\n",
        "plt.ylabel(\"Petal width (cm)\")\n",
        "plt.axis([0, 7.2, 0, 3])\n",
        "plt.legend()\n",
        "save_fig(\"decision_tree_high_variance_plot\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q9OxIeOVY5X_"
      },
      "source": [
        "# 추가 자료 - 트리 구조에 액세스"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AGijpJrqY5X_"
      },
      "source": [
        "훈련된 `DecisionTreeClassifier`에는 트리의 구조를 저장하는 `tree_` 속성이 있습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "xSIJ5FLDY5YA",
        "outputId": "d66dc016-dd1b-4669-f7c8-b0610a2fe7a6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.tree._tree.Tree at 0x7b7a3bbe78a0>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "tree = tree_clf.tree_\n",
        "tree"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJ_HzBdRY5YA"
      },
      "source": [
        "트리에 있는 총 노드 수를 얻을 수 있습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "9lalO0wrY5YA",
        "outputId": "796a396d-e95b-4a85-a07c-77913b030906",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "tree.node_count"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jsij0NbRY5YA"
      },
      "source": [
        "그 외 설명이 필요 없는 다른 속성이 있습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "_CB3Dz6OY5YA",
        "outputId": "4e0a3395-cda2-4630-ccba-5272e526d7f1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "tree.max_depth"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "K-p5poAdY5YA",
        "outputId": "8834aadd-dc15-4c06-f0d6-3fd444e1c51b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "tree.max_n_classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "KRtDSJ9kY5YA",
        "outputId": "61703f27-d536-4322-e4fd-bbb6e3e2b482",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "tree.n_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "NTNSej65Y5YA",
        "outputId": "d067a257-49ae-4fc3-eec4-92e15441587d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "tree.n_outputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "TWUTVkzWY5YB",
        "outputId": "66875f6e-9d16-4923-9eb8-40719f6799bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "tree.n_leaves"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQoYHh25Y5YB"
      },
      "source": [
        "노드에 대한 모든 정보는 넘파이 배열에 저장됩니다. 예를 들어, 각 노드의 불순도는 다음과 같습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "0aIuTR0FY5YB",
        "outputId": "ef8f0997-3e99-4f46-e32a-bbd6ffc10281",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.66666667, 0.        , 0.5       , 0.16803841, 0.04253308])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "tree.impurity"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UVkYVU6jY5YB"
      },
      "source": [
        "루트 노드의 인덱스는 0입니다. 노드 _i_의 왼쪽 및 오른쪽 자식 노드는 `tree.children_left[i]` 및 `tree.children_right[i]`입니다. 예를 들어 루트 노드의 자식 노드는 다음과 같습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "Onb4vW6IY5YB",
        "outputId": "b66ae774-e1cb-4d02-df4d-f39c4f582d29",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "tree.children_left[0], tree.children_right[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iRQkXkdOY5YB"
      },
      "source": [
        "왼쪽 노드와 오른쪽 노드가 같으면 이것이 리프 노드임을 의미합니다(자식 노드 ID는 임의적임):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "jgnG3mXiY5YB",
        "outputId": "78bb6824-6c86-4b95-a628-d45d401984c4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-1, -1)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "tree.children_left[3], tree.children_right[3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k7iR8RyfY5YB"
      },
      "source": [
        "따라서 다음과 같이 리프 노드 ID를 얻을 수 있습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "M13S0ZqKY5YC",
        "outputId": "e85a4f49-3918-4b4b-c071-2ab45a942dca",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 3, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "is_leaf = (tree.children_left == tree.children_right)\n",
        "np.arange(tree.node_count)[is_leaf]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TtCYwRuWY5YC"
      },
      "source": [
        "리프 노드가 아닌 노드를 _분할 노드_라고 합니다. 분할 특성은 `feature` 배열을 통해 참조할 수 있습니다. 리프 노드에 대한 값은 무시해야 합니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "P1KxLUdkY5YC",
        "outputId": "09a2eade-5299-40f2-ff70-8af1c51ba8c7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0, -2,  1, -2, -2], dtype=int64)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "tree.feature"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TPDGoyh1Y5YC"
      },
      "source": [
        "그리고 해당 임계값은 다음과 같습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "46OwP_mpY5YC",
        "outputId": "543905cc-1cb5-418d-fa23-9c2507233622",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2.44999999, -2.        ,  1.75      , -2.        , -2.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "tree.threshold"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VgCgm0WcY5YC"
      },
      "source": [
        "또한 각 노드에 도달한 클래스당 인스턴스 수도 확인할 수 있습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "H5SK5EqNY5YC",
        "outputId": "38aa8be0-55a5-4cc3-918b-e2e9e31cc67e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[50., 50., 50.]],\n",
              "\n",
              "       [[50.,  0.,  0.]],\n",
              "\n",
              "       [[ 0., 50., 50.]],\n",
              "\n",
              "       [[ 0., 49.,  5.]],\n",
              "\n",
              "       [[ 0.,  1., 45.]]])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "tree.value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "lN6kCINOY5YD",
        "outputId": "16404bdf-b30d-4e75-d1b9-0664dcae970a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([150,  50, 100,  54,  46], dtype=int64)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "tree.n_node_samples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "el_-Ki7_Y5YD",
        "outputId": "48fd3c93-f28a-456f-8775-36beb47f52be",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "np.all(tree.value.sum(axis=(1, 2)) == tree.n_node_samples)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YSVB8zyLY5YD"
      },
      "source": [
        "각 노드의 깊이를 계산하는 방법은 다음과 같습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "nlBdoUBAY5YD",
        "outputId": "667d2db3-6dc9-49d5-a0b3-c35539fcbab4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 1., 1., 2., 2.])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "def compute_depth(tree_clf):\n",
        "    tree = tree_clf.tree_\n",
        "    depth = np.zeros(tree.node_count)\n",
        "    stack = [(0, 0)]\n",
        "    while stack:\n",
        "        node, node_depth = stack.pop()\n",
        "        depth[node] = node_depth\n",
        "        if tree.children_left[node] != tree.children_right[node]:\n",
        "            stack.append((tree.children_left[node], node_depth + 1))\n",
        "            stack.append((tree.children_right[node], node_depth + 1))\n",
        "    return depth\n",
        "\n",
        "depth = compute_depth(tree_clf)\n",
        "depth"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZHRR20RmY5YD"
      },
      "source": [
        "다음은 깊이 1에서 모든 분할 노드의 임계값을 구하는 방법입니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "fy7ZNHZQY5YD",
        "outputId": "c0a1f599-4f0f-48f5-a846-82af841f5ecf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1], dtype=int64)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "tree_clf.tree_.feature[(depth == 1) & (~is_leaf)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "o_auvFEhY5YD",
        "outputId": "908bd7fe-a36e-4436-d511-0e743d06e137",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.75])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "tree_clf.tree_.threshold[(depth == 1) & (~is_leaf)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2jAVajEY5YD"
      },
      "source": [
        "# 연습문제 해답"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nThHXNtIY5YD"
      },
      "source": [
        "## 1. to 6."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vi-OBZ-2Y5YE"
      },
      "source": [
        "부록 A 참조"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iBBGZkn8Y5YE"
      },
      "source": [
        "## 7."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dvQfxGXjY5YE"
      },
      "source": [
        "_문제: moons 데이터셋에 결정 트리를 훈련시키고 세밀하게 튜닝해보세요._"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3V6bIrOqY5YE"
      },
      "source": [
        "a. `make_moons(n_samples=10000, noise=0.4)`를 사용해 데이터셋을 생성합니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "22olkS76Y5YE"
      },
      "source": [
        "`random_state = 42`를 추가하여 이 노트북의 출력을 일정하게 만듭니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "UrZbnaA2Y5YE"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import make_moons\n",
        "\n",
        "X_moons, y_moons = make_moons(n_samples=10000, noise=0.4, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qj04LItCY5YE"
      },
      "source": [
        "b. 이를 `train_test_split()`를 사용해 훈련 세트와 테스트 세트로 나눕니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "S_TG8FE0Y5YE"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_moons, y_moons,\n",
        "                                                    test_size=0.2,\n",
        "                                                    random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qmJMCaeSY5YE"
      },
      "source": [
        "c. `DecisionTreeClassifier`의 최적의 매개변수를 찾기 위해 교차 검증과 함께 그리드 탐색을 수행합니다(`GridSearchCV`를 사용하면 됩니다). 힌트: 여러 가지 `max_leaf_nodes` 값을 시도해보세요."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "emylJK_eY5YE",
        "outputId": "7cda0541-1f10-4435-d84e-3f017a1e61a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, estimator=DecisionTreeClassifier(random_state=42),\n",
              "             param_grid={'max_depth': [1, 2, 3, 4, 5, 6],\n",
              "                         'max_leaf_nodes': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
              "                                            13, 14, 15, 16, 17, 18, 19, 20, 21,\n",
              "                                            22, 23, 24, 25, 26, 27, 28, 29, 30,\n",
              "                                            31, ...],\n",
              "                         'min_samples_split': [2, 3, 4]})"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=DecisionTreeClassifier(random_state=42),\n",
              "             param_grid={&#x27;max_depth&#x27;: [1, 2, 3, 4, 5, 6],\n",
              "                         &#x27;max_leaf_nodes&#x27;: [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
              "                                            13, 14, 15, 16, 17, 18, 19, 20, 21,\n",
              "                                            22, 23, 24, 25, 26, 27, 28, 29, 30,\n",
              "                                            31, ...],\n",
              "                         &#x27;min_samples_split&#x27;: [2, 3, 4]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=DecisionTreeClassifier(random_state=42),\n",
              "             param_grid={&#x27;max_depth&#x27;: [1, 2, 3, 4, 5, 6],\n",
              "                         &#x27;max_leaf_nodes&#x27;: [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
              "                                            13, 14, 15, 16, 17, 18, 19, 20, 21,\n",
              "                                            22, 23, 24, 25, 26, 27, 28, 29, 30,\n",
              "                                            31, ...],\n",
              "                         &#x27;min_samples_split&#x27;: [2, 3, 4]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "params = {\n",
        "    'max_leaf_nodes': list(range(2, 100)),\n",
        "    'max_depth': list(range(1, 7)),\n",
        "    'min_samples_split': [2, 3, 4]\n",
        "}\n",
        "grid_search_cv = GridSearchCV(DecisionTreeClassifier(random_state=42),\n",
        "                              params,\n",
        "                              cv=3)\n",
        "\n",
        "grid_search_cv.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "YzEhIIKfY5YE",
        "outputId": "460a9d27-0a89-4067-c5df-59f13c00edfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=6, max_leaf_nodes=17, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=6, max_leaf_nodes=17, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=6, max_leaf_nodes=17, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "grid_search_cv.best_estimator_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kpDFZbIwY5YF"
      },
      "source": [
        "d. 찾은 매개변수를 사용해 전체 훈련 세트에 대해 모델을 훈련시키고 테스트 세트에서 성능을 측정합니다. 대략 85~87%의 정확도가 나올 것입니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SAoMj21kY5YF"
      },
      "source": [
        "기본적으로 `GridSearchCV`는 전체 훈련 세트로 찾은 최적의 모델을 다시 훈련시킵니다(`refit=False`로 지정해서 바꿀 수 있습니다). 그래서 별도로 작업할 필요가 없습니다. 모델의 정확도를 바로 평가할 수 있습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "64SK_SWeY5YF",
        "outputId": "b49b436b-f2d5-4ace-84ee-6fafdcab81f0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8595"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "y_pred = grid_search_cv.predict(X_test)\n",
        "accuracy_score(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n8AFmygtY5YF"
      },
      "source": [
        "## 8."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x6Dx0ecPY5YF"
      },
      "source": [
        "_문제: 랜덤 포레스트를 만들어보세요._"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yqv_cUWsY5YF"
      },
      "source": [
        "a. 이전 연습문제에 이어서, 훈련 세트의 서브셋을 1,000개 생성합니다. 각각은 무작위로 선택된 100개의 샘플을 담고 있습니다. 힌트: 사이킷런의 `ShuffleSplit`을 사용할 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "fmuwsYbrY5YF"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import ShuffleSplit\n",
        "\n",
        "n_trees = 1000\n",
        "n_instances = 100\n",
        "\n",
        "mini_sets = []\n",
        "\n",
        "rs = ShuffleSplit(n_splits=n_trees, test_size=len(X_train) - n_instances,\n",
        "                  random_state=42)\n",
        "\n",
        "for mini_train_index, mini_test_index in rs.split(X_train):\n",
        "    X_mini_train = X_train[mini_train_index]\n",
        "    y_mini_train = y_train[mini_train_index]\n",
        "    mini_sets.append((X_mini_train, y_mini_train))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1sc0lPiY5YF"
      },
      "source": [
        "b. 앞에서 찾은 최적의 매개변수를 사용해 각 서브셋에 결정 트리를 훈련시킵니다. 테스트 세트로 이 1,000개의 결정 트리를 평가합니다. 더 작은 데이터셋에서 훈련되었기 때문에 이 결정 트리는 앞서 만든 결정 트리보다 성능이 떨어져 약 80%의 정확도를 냅니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "de7WY3G7Y5YG",
        "outputId": "ad65ea44-cd3a-41e4-bed3-5e53fb507bb1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8056605"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "from sklearn.base import clone\n",
        "\n",
        "forest = [clone(grid_search_cv.best_estimator_) for _ in range(n_trees)]\n",
        "\n",
        "accuracy_scores = []\n",
        "\n",
        "for tree, (X_mini_train, y_mini_train) in zip(forest, mini_sets):\n",
        "    tree.fit(X_mini_train, y_mini_train)\n",
        "\n",
        "    y_pred = tree.predict(X_test)\n",
        "    accuracy_scores.append(accuracy_score(y_test, y_pred))\n",
        "\n",
        "np.mean(accuracy_scores)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "niAR6bqdY5YG"
      },
      "source": [
        "c. 이제 마술을 부릴 차례입니다. 각 테스트 세트 샘플에 대해 1,000개의 결정 트리 예측을 만들고 다수로 나온 예측만 취합니다(사이파이의 `mode()` 함수를 사용할 수 있습니다). 그러면 테스트 세트에 대한 _다수결 예측_이 만들어집니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "UOyNrPpPY5YG"
      },
      "outputs": [],
      "source": [
        "Y_pred = np.empty([n_trees, len(X_test)], dtype=np.uint8)\n",
        "\n",
        "for tree_index, tree in enumerate(forest):\n",
        "    Y_pred[tree_index] = tree.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "J8zHg9OOY5YG"
      },
      "outputs": [],
      "source": [
        "from scipy.stats import mode\n",
        "\n",
        "# scipy 1.11에서 keepdims의 기본값이 False로 바뀝니다.\n",
        "# 이와 관련된 경고를 피하기 위해 명시적으로 False로 지정합니다.\n",
        "y_pred_majority_votes, n_votes = mode(Y_pred, axis=0, keepdims=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "kDt5ndy8Y5YG",
        "outputId": "1a5142c4-4f76-4cea-a15c-0fac881a1214",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2000,), (2000,))"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "y_pred_majority_votes.shape, n_votes.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1K0SqpXkY5YG"
      },
      "source": [
        "d. 테스트 세트에서 이 예측을 평가합니다. 앞서 만든 모델보다 조금 높은(약 0.5~1.5% 정도) 정확도를 얻게 될 것입니다. 축하합니다. 랜덤 포레스트 분류기를 훈련시켰습니다!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "PZCMKhSqY5YG",
        "outputId": "1b5799ad-80b1-4ea7-b5e5-46e2028cc8f5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.873"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "accuracy_score(y_test, y_pred_majority_votes)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
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      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.9"
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    "nav_menu": {
      "height": "309px",
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    "toc": {
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      "threshold": 6,
      "toc_cell": false,
      "toc_section_display": "block",
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    "colab": {
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