07_ensemble_learning_and_random_forests.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rJ4NvbsfY91S"
      },
      "source": [
        "**7장 - 앙상블 학습과 랜덤 포레스트**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s0hrEOmVY91V"
      },
      "source": [
        "_이 노트북에는 7장의 모든 샘플 코드와 연습 문제에 대한 솔루션이 포함되어 있습니다._"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E3honfB_Y91V"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a href=\"https://colab.research.google.com/github/rickiepark/handson-ml3/blob/main/07_ensemble_learning_and_random_forests.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": "zesZe-EeY91W"
      },
      "source": [
        "# 설정"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n4VCZtT8Y91W"
      },
      "source": [
        "이 프로젝트에는 파이썬 3.7 이상이 필요합니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "8EERz3L4Y91X"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "\n",
        "assert sys.version_info >= (3, 7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mMOp4LIPY91X"
      },
      "source": [
        "또한 사이킷런 ≥ 1.0.1이 필요합니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "ICkXYqaNY91Y"
      },
      "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": "_4gbPyRwY91Y"
      },
      "source": [
        "이전 챕터에서와 마찬가지로 기본 글꼴 크기를 정의하여 그림을 더 예쁘게 만들어 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "aME7BU_fY91Y",
        "outputId": "acc483f7-2609-4c9c-9ede-e59e99858d73",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package fonts-nanum.\n",
            "(Reading database ... 121918 files and directories currently installed.)\n",
            "Preparing to unpack .../fonts-nanum_20200506-1_all.deb ...\n",
            "Unpacking fonts-nanum (20200506-1) ...\n",
            "Setting up fonts-nanum (20200506-1) ...\n",
            "Processing triggers for fontconfig (2.13.1-4.2ubuntu5) ...\n"
          ]
        }
      ],
      "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)\n",
        "\n",
        "import sys\n",
        "# 코랩의 경우 나눔 폰트를 설치합니다.\n",
        "if 'google.colab' in sys.modules:\n",
        "    !sudo apt-get -qq -y install fonts-nanum\n",
        "    import matplotlib.font_manager as fm\n",
        "    font_files = fm.findSystemFonts(fontpaths=['/usr/share/fonts/truetype/nanum'])\n",
        "    for fpath in font_files:\n",
        "        fm.fontManager.addfont(fpath)\n",
        "\n",
        "# 나눔 폰트를 사용합니다.\n",
        "import matplotlib\n",
        "\n",
        "matplotlib.rc('font', family='NanumBarunGothic')\n",
        "matplotlib.rcParams['axes.unicode_minus'] = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g-0b8JYYY91Z"
      },
      "source": [
        "그리고 아직 존재하지 않는 경우 `images/ensembles` 폴더를 만들고, 이 노트북을 통해 책에 사용할 그림을 고해상도로 저장하는 데 사용되는 `save_fig()` 함수를 정의해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Eonw0HPWY91Z"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "\n",
        "IMAGES_PATH = Path() / \"images\" / \"ensembles\"\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": "LEy0VLcMY91Z"
      },
      "source": [
        "# 투표 기반 분류기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Dy-VIXtXY91Z",
        "outputId": "e5610457-d404-4a7a-b3c3-793e6accb34e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x350 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 추가 코드 - 이 셀은 그림 7-3을 생성하고 저장합니다.\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "heads_proba = 0.51\n",
        "np.random.seed(42)\n",
        "coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)\n",
        "cumulative_heads = coin_tosses.cumsum(axis=0)\n",
        "cumulative_heads_ratio = cumulative_heads / np.arange(1, 10001).reshape(-1, 1)\n",
        "\n",
        "plt.figure(figsize=(8, 3.5))\n",
        "plt.plot(cumulative_heads_ratio)\n",
        "plt.plot([0, 10000], [0.51, 0.51], \"k--\", linewidth=2, label=\"51%\")\n",
        "plt.plot([0, 10000], [0.5, 0.5], \"k-\", label=\"50%\")\n",
        "plt.xlabel(\"동전을 던진 횟수\")\n",
        "plt.ylabel(\"앞면이 나온 비율\")\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.axis([0, 10000, 0.42, 0.58])\n",
        "plt.grid()\n",
        "save_fig(\"law_of_large_numbers_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ab194p0eY91a"
      },
      "source": [
        "투표 기반 분류기를 만들어 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "GPLFV6jJY91a",
        "outputId": "284858eb-d875-443c-a43d-002ef3cfd30c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n",
              "                             ('rf', RandomForestClassifier(random_state=42)),\n",
              "                             ('svc', SVC(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>VotingClassifier(estimators=[(&#x27;lr&#x27;, LogisticRegression(random_state=42)),\n",
              "                             (&#x27;rf&#x27;, RandomForestClassifier(random_state=42)),\n",
              "                             (&#x27;svc&#x27;, SVC(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 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-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;lr&#x27;, LogisticRegression(random_state=42)),\n",
              "                             (&#x27;rf&#x27;, RandomForestClassifier(random_state=42)),\n",
              "                             (&#x27;svc&#x27;, SVC(random_state=42))])</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\"><label>lr</label></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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>rf</label></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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svc</label></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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "from sklearn.datasets import make_moons\n",
        "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "X, y = make_moons(n_samples=500, noise=0.30, random_state=42)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
        "\n",
        "voting_clf = VotingClassifier(\n",
        "    estimators=[\n",
        "        ('lr', LogisticRegression(random_state=42)),\n",
        "        ('rf', RandomForestClassifier(random_state=42)),\n",
        "        ('svc', SVC(random_state=42))\n",
        "    ]\n",
        ")\n",
        "voting_clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "trydL9HNY91a",
        "outputId": "942e8ba1-12ce-4c39-bb0d-3bdd80a0b6e9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "lr = 0.864\n",
            "rf = 0.896\n",
            "svc = 0.896\n"
          ]
        }
      ],
      "source": [
        "for name, clf in voting_clf.named_estimators_.items():\n",
        "    print(name, \"=\", clf.score(X_test, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "uZrRtf3DY91a",
        "outputId": "f3dbc9df-37ac-4994-c35b-e1f676fcc733",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "voting_clf.predict(X_test[:1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "6xNqIwgAY91a",
        "outputId": "99761064-dd9b-4aec-840f-909f8d97226d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([1]), array([1]), array([0])]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "[clf.predict(X_test[:1]) for clf in voting_clf.estimators_]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "wvQo7JaLY91a",
        "outputId": "b0a0e1f1-4c85-4ca5-99bb-e2bd24cbc02b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.912"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "voting_clf.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XysYZMVbY91b"
      },
      "source": [
        "이제 간접 투표를 사용해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "VwMnoFKwY91b",
        "outputId": "f9e95b7f-3ded-4d05-f6ce-95abb257a816",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.92"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "voting_clf.voting = \"soft\"\n",
        "voting_clf.named_estimators[\"svc\"].probability = True\n",
        "voting_clf.fit(X_train, y_train)\n",
        "voting_clf.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0MaYsBesY91b"
      },
      "source": [
        "# 배깅과 페이스팅\n",
        "## 사이킷런의 배깅과 페이스팅"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "7sqe3AH5Y91b",
        "outputId": "2e5d8564-8984-41e9-c421-e0d13434b5b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BaggingClassifier(estimator=DecisionTreeClassifier(), max_samples=100,\n",
              "                  n_estimators=500, n_jobs=-1, 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>BaggingClassifier(estimator=DecisionTreeClassifier(), max_samples=100,\n",
              "                  n_estimators=500, n_jobs=-1, 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 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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(estimator=DecisionTreeClassifier(), max_samples=100,\n",
              "                  n_estimators=500, n_jobs=-1, random_state=42)</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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</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-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "bag_clf = BaggingClassifier(DecisionTreeClassifier(), n_estimators=500,\n",
        "                            max_samples=100, n_jobs=-1, random_state=42)\n",
        "bag_clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "eW0m90dDY91b",
        "outputId": "f5ddc1f6-1860-4a91-f629-326f976578eb",
        "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 7-5를 생성하고 저장합니다.\n",
        "\n",
        "def plot_decision_boundary(clf, X, y, alpha=1.0):\n",
        "    axes=[-1.5, 2.4, -1, 1.5]\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 * alpha, cmap='Wistia')\n",
        "    plt.contour(x1, x2, y_pred, cmap=\"Greys\", alpha=0.8 * alpha)\n",
        "    colors = [\"#78785c\", \"#c47b27\"]\n",
        "    markers = (\"o\", \"^\")\n",
        "    for idx in (0, 1):\n",
        "        plt.plot(X[:, 0][y == idx], X[:, 1][y == idx],\n",
        "                 color=colors[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",
        "tree_clf = DecisionTreeClassifier(random_state=42)\n",
        "tree_clf.fit(X_train, y_train)\n",
        "\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
        "plt.sca(axes[0])\n",
        "plot_decision_boundary(tree_clf, X_train, y_train)\n",
        "plt.title(\"결정 트리\")\n",
        "plt.sca(axes[1])\n",
        "plot_decision_boundary(bag_clf, X_train, y_train)\n",
        "plt.title(\"배깅을 사용한 결정 트리\")\n",
        "plt.ylabel(\"\")\n",
        "save_fig(\"decision_tree_without_and_with_bagging_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tCmI9ke9Y91b"
      },
      "source": [
        "## OOB 평가"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "UV99ms_8Y91b",
        "outputId": "137e2e52-bdd0-4825-9694-6f2e10bcad92",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.896"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "bag_clf = BaggingClassifier(DecisionTreeClassifier(), n_estimators=500,\n",
        "                            oob_score=True, n_jobs=-1, random_state=42)\n",
        "bag_clf.fit(X_train, y_train)\n",
        "bag_clf.oob_score_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "B39Jkp0tY91b",
        "outputId": "2da43909-e771-4b18-ed3b-fb2e22b0b4e6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.32352941, 0.67647059],\n",
              "       [0.3375    , 0.6625    ],\n",
              "       [1.        , 0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "bag_clf.oob_decision_function_[:3]  # probas for the first 3 instances"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "scrolled": true,
        "id": "4ba-AyMyY91c",
        "outputId": "c1d2ba17-4ca0-4f16-e044-9c58b995c8f3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.92"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "y_pred = bag_clf.predict(X_test)\n",
        "accuracy_score(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i0XogWuMY91c"
      },
      "source": [
        "크기가 _m_인 데이터셋에서 하나의 샘플을 무작위로 뽑는 경우, 데이터셋의 각 샘플이 선택될 확률은 1/_m_ 이므로 선택되지 않을 확률은 1 - 1/_m_ 입니다. 중복을 허용하여 _m_ 개의 샘플을 뽑는 경우, 모든 추첨은 독립적이므로 각 샘플의 선택되지 않을 확률은 (1 - 1/_m_)<sup>_m_</sup> 입니다. 이제 _m_ 이 무한대에 가까워지면 exp(_x_)가 (1 + _x_/_m_)<sup>_m_</sup>의 극한과 같다는 사실을 이용해 보겠습니다. 따라서 _m_ 이 크면 OOB 샘플의 비율은 약 exp(-1) ≈ 0.37이 됩니다. 따라서 대략 63%(1 - 0.37)가 샘플링됩니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "CVY5DtVeY91c",
        "outputId": "d7bf0486-bc8e-4430-9abd-68161e6a5722",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6323045752290363\n",
            "0.6321205588285577\n"
          ]
        }
      ],
      "source": [
        "# 추가 코드 - 63% 확률을 계산하는 방법을 보여줍니다.\n",
        "print(1 - (1 - 1 / 1000) ** 1000)\n",
        "print(1 - np.exp(-1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G_WOPwOZY91c"
      },
      "source": [
        "# 랜덤 포레스트"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "H7sjQgAYY91c"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16,\n",
        "                                 n_jobs=-1, random_state=42)\n",
        "rnd_clf.fit(X_train, y_train)\n",
        "y_pred_rf = rnd_clf.predict(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czXS_oa4Y91c"
      },
      "source": [
        "랜덤 포레스트는 결정 트리의 배깅과 같습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "JmOywZMFY91c"
      },
      "outputs": [],
      "source": [
        "bag_clf = BaggingClassifier(\n",
        "    DecisionTreeClassifier(max_features=\"sqrt\", max_leaf_nodes=16),\n",
        "    n_estimators=500, n_jobs=-1, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "fVBTYTq9Y91c",
        "outputId": "1b751b22-fd68-4121-f7e5-8c3c56aaac5c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# 추가 코드 - 예측이 동일한지 확인합니다.\n",
        "bag_clf.fit(X_train, y_train)\n",
        "y_pred_bag = bag_clf.predict(X_test)\n",
        "np.all(y_pred_bag == y_pred_rf)  # 동일한 예측"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zCbnzzLhY91c"
      },
      "source": [
        "## 특성 중요도"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "zMintuTaY91c",
        "outputId": "328a7864-ce1d-4f66-b40d-57c0743c2f48",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.11 sepal length (cm)\n",
            "0.02 sepal width (cm)\n",
            "0.44 petal length (cm)\n",
            "0.42 petal width (cm)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "\n",
        "iris = load_iris(as_frame=True)\n",
        "rnd_clf = RandomForestClassifier(n_estimators=500, random_state=42)\n",
        "rnd_clf.fit(iris.data, iris.target)\n",
        "for score, name in zip(rnd_clf.feature_importances_, iris.data.columns):\n",
        "    print(round(score, 2), name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "SyAz9hiZY91d",
        "outputId": "7ba43b0b-b676-4e4a-8d7a-abb3aa91e17e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 추가 코드 - 이 셀은 그림 7-6을 생성하고 저장합니다.\n",
        "\n",
        "from sklearn.datasets import fetch_openml\n",
        "\n",
        "# 사이킷런 1.4버전에서 parser 매개변수 기본값이 'liac-arff'에서 'auto'로 바뀌었습니다.\n",
        "# 이전 버전에서도 동일한 결과를 내도록 명시적으로 'auto'로 지정합니다.\n",
        "X_mnist, y_mnist = fetch_openml('mnist_784', return_X_y=True,\n",
        "                                as_frame=False, parser='auto')\n",
        "\n",
        "rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rnd_clf.fit(X_mnist, y_mnist)\n",
        "\n",
        "heatmap_image = rnd_clf.feature_importances_.reshape(28, 28)\n",
        "plt.imshow(heatmap_image, cmap=\"hot\")\n",
        "cbar = plt.colorbar(ticks=[rnd_clf.feature_importances_.min(),\n",
        "                           rnd_clf.feature_importances_.max()])\n",
        "cbar.ax.set_yticklabels(['중요하지 않음', '매우 중요'], fontsize=14)\n",
        "plt.axis(\"off\")\n",
        "save_fig(\"mnist_feature_importance_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kt56w10FY91g"
      },
      "source": [
        "# 부스팅\n",
        "## AdaBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "L8duNqBqY91g",
        "outputId": "4a42554d-41f6-49c6-d0b7-5c6f58067b81",
        "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 7-8을 생성하고 저장합니다.\n",
        "\n",
        "m = len(X_train)\n",
        "\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
        "for subplot, learning_rate in ((0, 1), (1, 0.5)):\n",
        "    sample_weights = np.ones(m) / m\n",
        "    plt.sca(axes[subplot])\n",
        "    for i in range(5):\n",
        "        svm_clf = SVC(C=0.2, gamma=0.6, random_state=42)\n",
        "        svm_clf.fit(X_train, y_train, sample_weight=sample_weights * m)\n",
        "        y_pred = svm_clf.predict(X_train)\n",
        "\n",
        "        error_weights = sample_weights[y_pred != y_train].sum()\n",
        "        r = error_weights / sample_weights.sum()  # equation 7-1\n",
        "        alpha = learning_rate * np.log((1 - r) / r)  # equation 7-2\n",
        "        sample_weights[y_pred != y_train] *= np.exp(alpha)  # equation 7-3\n",
        "        sample_weights /= sample_weights.sum()  # normalization step\n",
        "\n",
        "        plot_decision_boundary(svm_clf, X_train, y_train, alpha=0.4)\n",
        "        plt.title(f\"learning_rate = {learning_rate}\")\n",
        "    if subplot == 0:\n",
        "        plt.text(-0.75, -0.95, \"1\", fontsize=16)\n",
        "        plt.text(-1.05, -0.95, \"2\", fontsize=16)\n",
        "        plt.text(1.0, -0.95, \"3\", fontsize=16)\n",
        "        plt.text(-1.45, -0.5, \"4\", fontsize=16)\n",
        "        plt.text(1.36,  -0.95, \"5\", fontsize=16)\n",
        "    else:\n",
        "        plt.ylabel(\"\")\n",
        "\n",
        "save_fig(\"boosting_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "yoq-DXPEY91h",
        "outputId": "6fb4a4f5-f903-4df3-9bbb-d218ee9f890d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=1),\n",
              "                   learning_rate=0.5, n_estimators=30, 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>AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=1),\n",
              "                   learning_rate=0.5, n_estimators=30, 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 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-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=1),\n",
              "                   learning_rate=0.5, n_estimators=30, random_state=42)</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-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=1)</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-10\" type=\"checkbox\" ><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=1)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "\n",
        "ada_clf = AdaBoostClassifier(\n",
        "    DecisionTreeClassifier(max_depth=1), n_estimators=30,\n",
        "    learning_rate=0.5, random_state=42)\n",
        "ada_clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "BEmY0JL2Y91h",
        "outputId": "cdbd5315-ca2b-42e4-9ecb-b550558c7bff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 추가 코드 - AdaBoost 분류기에 대한 결정 경계가 어떻게 보이는지 궁금한 경우\n",
        "plot_decision_boundary(ada_clf, X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LVWOzbKjY91h"
      },
      "source": [
        "## 그레이디언트 부스팅"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_qIOqRbY91h"
      },
      "source": [
        "간단한 2차방정식 데이터셋을 만들고 여기에 `DecisionTreeRegressor`를 훈련시켜 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "XciINxFdY91h",
        "outputId": "f06de67a-dc32-4460-84fa-5e9d849ac9cc",
        "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-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=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-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" 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": 26
        }
      ],
      "source": [
        "import numpy as np\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "np.random.seed(42)\n",
        "X = np.random.rand(100, 1) - 0.5\n",
        "y = 3 * X[:, 0] ** 2 + 0.05 * np.random.randn(100)  # y = 3x² + Gaussian noise\n",
        "\n",
        "tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
        "tree_reg1.fit(X, y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iw4Y0NmiY91h"
      },
      "source": [
        "이제 이전 예측기가 만든 잔여 오차에 대해 다른 결정 트리 회귀를 훈련해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "4YWH1oJwY91i",
        "outputId": "c5ee87c8-7007-449c-964b-36fed6fd9419",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(max_depth=2, random_state=43)"
            ],
            "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>DecisionTreeRegressor(max_depth=2, random_state=43)</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-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=2, random_state=43)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "y2 = y - tree_reg1.predict(X)\n",
        "tree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=43)\n",
        "tree_reg2.fit(X, y2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "97LFbRc0Y91i",
        "outputId": "aa4bb898-62fd-4048-9051-04eeb0d7f9c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(max_depth=2, random_state=44)"
            ],
            "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>DecisionTreeRegressor(max_depth=2, random_state=44)</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-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=2, random_state=44)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "y3 = y2 - tree_reg2.predict(X)\n",
        "tree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=44)\n",
        "tree_reg3.fit(X, y3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "_lOWO5XAY91i",
        "outputId": "0006aed6-3b08-472e-9c2e-3fef100c2d0d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.49484029, 0.04021166, 0.75026781])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "X_new = np.array([[-0.4], [0.], [0.5]])\n",
        "sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "ORYxHFdpY91i",
        "outputId": "135c4339-775f-4d82-ad0f-c6ce70097bf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x1100 with 6 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# 추가 코드 - 이 셀은 그림 7-9를 생성하고 저장합니다.\n",
        "\n",
        "def plot_predictions(regressors, X, y, axes, style,\n",
        "                     label=None, data_style=\"b.\", data_label=None):\n",
        "    x1 = np.linspace(axes[0], axes[1], 500)\n",
        "    y_pred = sum(regressor.predict(x1.reshape(-1, 1))\n",
        "                 for regressor in regressors)\n",
        "    plt.plot(X[:, 0], y, data_style, label=data_label)\n",
        "    plt.plot(x1, y_pred, style, linewidth=2, label=label)\n",
        "    if label or data_label:\n",
        "        plt.legend(loc=\"upper center\")\n",
        "    plt.axis(axes)\n",
        "\n",
        "plt.figure(figsize=(11, 11))\n",
        "\n",
        "plt.subplot(3, 2, 1)\n",
        "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.2, 0.8], style=\"g-\",\n",
        "                 label=\"$h_1(x_1)$\", data_label=\"훈련 세트\")\n",
        "plt.ylabel(\"$y$  \", rotation=0)\n",
        "plt.title(\"잔여 오차와 트리의 예측\")\n",
        "\n",
        "plt.subplot(3, 2, 2)\n",
        "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.2, 0.8], style=\"r-\",\n",
        "                 label=\"$h(x_1) = h_1(x_1)$\", data_label=\"훈련 세트\")\n",
        "plt.title(\"앙상블의 예측\")\n",
        "\n",
        "plt.subplot(3, 2, 3)\n",
        "plot_predictions([tree_reg2], X, y2, axes=[-0.5, 0.5, -0.4, 0.6], style=\"g-\",\n",
        "                 label=\"$h_2(x_1)$\", data_style=\"k+\",\n",
        "                 data_label=\"잔여 오차: $y - h_1(x_1)$\")\n",
        "plt.ylabel(\"$y$  \", rotation=0)\n",
        "\n",
        "plt.subplot(3, 2, 4)\n",
        "plot_predictions([tree_reg1, tree_reg2], X, y, axes=[-0.5, 0.5, -0.2, 0.8],\n",
        "                  style=\"r-\", label=\"$h(x_1) = h_1(x_1) + h_2(x_1)$\")\n",
        "\n",
        "plt.subplot(3, 2, 5)\n",
        "plot_predictions([tree_reg3], X, y3, axes=[-0.5, 0.5, -0.4, 0.6], style=\"g-\",\n",
        "                 label=\"$h_3(x_1)$\", data_style=\"k+\",\n",
        "                 data_label=\"잔여 오차: $y - h_1(x_1) - h_2(x_1)$\")\n",
        "plt.xlabel(\"$x_1$\")\n",
        "plt.ylabel(\"$y$  \", rotation=0)\n",
        "\n",
        "plt.subplot(3, 2, 6)\n",
        "plot_predictions([tree_reg1, tree_reg2, tree_reg3], X, y,\n",
        "                 axes=[-0.5, 0.5, -0.2, 0.8], style=\"r-\",\n",
        "                 label=\"$h(x_1) = h_1(x_1) + h_2(x_1) + h_3(x_1)$\")\n",
        "plt.xlabel(\"$x_1$\")\n",
        "\n",
        "save_fig(\"gradient_boosting_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8gIGpKIzY91i"
      },
      "source": [
        "이제 그레이디언트 부스팅 회귀를 시도해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "Fc-LybNdY91i",
        "outputId": "1c326b80-d2d9-4e94-d4cf-fcaa27e0371b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,\n",
              "                          random_state=42)"
            ],
            "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>GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,\n",
              "                          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-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,\n",
              "                          random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "\n",
        "gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=3,\n",
        "                                 learning_rate=1.0, random_state=42)\n",
        "gbrt.fit(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "ooYJy75LY91j",
        "outputId": "ee452d0d-ebc2-4336-eafe-340c0eb6a0c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=500,\n",
              "                          n_iter_no_change=10, 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>GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=500,\n",
              "                          n_iter_no_change=10, 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-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=500,\n",
              "                          n_iter_no_change=10, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "gbrt_best = GradientBoostingRegressor(\n",
        "    max_depth=2, learning_rate=0.05, n_estimators=500,\n",
        "    n_iter_no_change=10, random_state=42)\n",
        "gbrt_best.fit(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "KH4FEsuWY91j",
        "outputId": "d0e3fa44-231d-49e1-b581-6a68fd0cfd93",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "92"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "gbrt_best.n_estimators_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "SmFX_vumY91j",
        "outputId": "bab1b820-5db8-40fc-bc29-a71f614d92ba",
        "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 7-10을 생성하고 저장합니다.\n",
        "\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
        "\n",
        "plt.sca(axes[0])\n",
        "plot_predictions([gbrt], X, y, axes=[-0.5, 0.5, -0.1, 0.8], style=\"r-\",\n",
        "                 label=\"앙상블의 예측\")\n",
        "plt.title(f\"learning_rate={gbrt.learning_rate}, \"\n",
        "          f\"n_estimators={gbrt.n_estimators_}\")\n",
        "plt.xlabel(\"$x_1$\")\n",
        "plt.ylabel(\"$y$\", rotation=0)\n",
        "\n",
        "plt.sca(axes[1])\n",
        "plot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.1, 0.8], style=\"r-\")\n",
        "plt.title(f\"learning_rate={gbrt_best.learning_rate}, \"\n",
        "          f\"n_estimators={gbrt_best.n_estimators_}\")\n",
        "plt.xlabel(\"$x_1$\")\n",
        "\n",
        "save_fig(\"gbrt_learning_rate_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "eAIXwU6aY91j"
      },
      "outputs": [],
      "source": [
        "# 추가 코드 - 2장에서 설명했습니다.\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tarfile\n",
        "import urllib.request\n",
        "\n",
        "def load_housing_data():\n",
        "    tarball_path = Path(\"datasets/housing.tgz\")\n",
        "    if not tarball_path.is_file():\n",
        "        Path(\"datasets\").mkdir(parents=True, exist_ok=True)\n",
        "        url = \"https://github.com/ageron/data/raw/main/housing.tgz\"\n",
        "        urllib.request.urlretrieve(url, tarball_path)\n",
        "        with tarfile.open(tarball_path) as housing_tarball:\n",
        "            housing_tarball.extractall(path=\"datasets\")\n",
        "    return pd.read_csv(Path(\"datasets/housing/housing.csv\"))\n",
        "\n",
        "housing = load_housing_data()\n",
        "\n",
        "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
        "housing_labels = train_set[\"median_house_value\"]\n",
        "housing = train_set.drop(\"median_house_value\", axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "opUM5h4IY91j",
        "outputId": "e54d8341-2b85-40aa-bae2-40844eb60b78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('columntransformer',\n",
              "                 ColumnTransformer(remainder='passthrough',\n",
              "                                   transformers=[('ordinalencoder',\n",
              "                                                  OrdinalEncoder(),\n",
              "                                                  ['ocean_proximity'])])),\n",
              "                ('histgradientboostingregressor',\n",
              "                 HistGradientBoostingRegressor(categorical_features=[0],\n",
              "                                               random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 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-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 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-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 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-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 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-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;ordinalencoder&#x27;,\n",
              "                                                  OrdinalEncoder(),\n",
              "                                                  [&#x27;ocean_proximity&#x27;])])),\n",
              "                (&#x27;histgradientboostingregressor&#x27;,\n",
              "                 HistGradientBoostingRegressor(categorical_features=[0],\n",
              "                                               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 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-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;ordinalencoder&#x27;,\n",
              "                                                  OrdinalEncoder(),\n",
              "                                                  [&#x27;ocean_proximity&#x27;])])),\n",
              "                (&#x27;histgradientboostingregressor&#x27;,\n",
              "                 HistGradientBoostingRegressor(categorical_features=[0],\n",
              "                                               random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><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-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;ordinalencoder&#x27;, OrdinalEncoder(),\n",
              "                                 [&#x27;ocean_proximity&#x27;])])</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-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinalencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ocean_proximity&#x27;]</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-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder()</pre></div></div></div></div></div></div><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-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;longitude&#x27;, &#x27;latitude&#x27;, &#x27;housing_median_age&#x27;, &#x27;total_rooms&#x27;, &#x27;total_bedrooms&#x27;, &#x27;population&#x27;, &#x27;households&#x27;, &#x27;median_income&#x27;]</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-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">HistGradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>HistGradientBoostingRegressor(categorical_features=[0], random_state=42)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.ensemble import HistGradientBoostingRegressor\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "\n",
        "hgb_reg = make_pipeline(\n",
        "    make_column_transformer((OrdinalEncoder(), [\"ocean_proximity\"]),\n",
        "                            remainder=\"passthrough\"),\n",
        "    HistGradientBoostingRegressor(categorical_features=[0], random_state=42)\n",
        ")\n",
        "hgb_reg.fit(housing, housing_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "byI-9gksY91j",
        "outputId": "ff56d2cd-963a-4d06-d54c-80be513ff36e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       10.000000\n",
              "mean     47613.307194\n",
              "std       1295.422509\n",
              "min      44963.213061\n",
              "25%      47001.233485\n",
              "50%      48000.963564\n",
              "75%      48488.093243\n",
              "max      49176.368465\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "# 추가 코드 - hgb_reg 모델에 대한 RMSE 통계를 평가합니다.\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "hgb_rmses = -cross_val_score(hgb_reg, housing, housing_labels,\n",
        "                             scoring=\"neg_root_mean_squared_error\", cv=10)\n",
        "pd.Series(hgb_rmses).describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AnB-uxLtY91k"
      },
      "source": [
        "# 스태킹"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "oUd_dKVUY91k",
        "outputId": "14a99c79-3666-4f91-e7ec-d6ef1592cc37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StackingClassifier(cv=5,\n",
              "                   estimators=[('lr', LogisticRegression(random_state=42)),\n",
              "                               ('rf', RandomForestClassifier(random_state=42)),\n",
              "                               ('svc', SVC(probability=True, random_state=42))],\n",
              "                   final_estimator=RandomForestClassifier(random_state=43))"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 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-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 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-10 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-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 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-10 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-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 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-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 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-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 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-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(cv=5,\n",
              "                   estimators=[(&#x27;lr&#x27;, LogisticRegression(random_state=42)),\n",
              "                               (&#x27;rf&#x27;, RandomForestClassifier(random_state=42)),\n",
              "                               (&#x27;svc&#x27;, SVC(probability=True, random_state=42))],\n",
              "                   final_estimator=RandomForestClassifier(random_state=43))</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-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StackingClassifier</label><div class=\"sk-toggleable__content\"><pre>StackingClassifier(cv=5,\n",
              "                   estimators=[(&#x27;lr&#x27;, LogisticRegression(random_state=42)),\n",
              "                               (&#x27;rf&#x27;, RandomForestClassifier(random_state=42)),\n",
              "                               (&#x27;svc&#x27;, SVC(probability=True, random_state=42))],\n",
              "                   final_estimator=RandomForestClassifier(random_state=43))</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>lr</label></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-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>rf</label></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-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svc</label></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-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True, random_state=42)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>final_estimator</label></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-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=43)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "from sklearn.ensemble import StackingClassifier\n",
        "\n",
        "stacking_clf = StackingClassifier(\n",
        "    estimators=[\n",
        "        ('lr', LogisticRegression(random_state=42)),\n",
        "        ('rf', RandomForestClassifier(random_state=42)),\n",
        "        ('svc', SVC(probability=True, random_state=42))\n",
        "    ],\n",
        "    final_estimator=RandomForestClassifier(random_state=43),\n",
        "    cv=5  # 교차 검증 폴드 수\n",
        ")\n",
        "stacking_clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "85N9corVY91k",
        "outputId": "ba7c9241-029e-4682-efc3-571546c29f99",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.928"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "stacking_clf.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m4Zt-QgeY91k"
      },
      "source": [
        "# 연습문제 해답"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QTQtKhiOY91k"
      },
      "source": [
        "## 1. to 7."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pDHhz4f4Y91k"
      },
      "source": [
        "부록 A 참조"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "20pzt2EWY91k"
      },
      "source": [
        "## 8. 투표 기반 분류기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eq2FVVW0Y91k"
      },
      "source": [
        "문제: _MNIST 데이터를 불러들여 훈련 세트, 검증 세트, 테스트 세트로 나눕니다(예를 들면 훈련에 50,000개 샘플, 검증에 10,000개 샘플, 테스트에 10,000개 샘플)._"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SpZCykJxY91k"
      },
      "source": [
        "MNIST 데이터셋은 앞서 로드했습니다. 이 데이터셋은 이미 훈련 세트(처음 60,000개의 샘플)와 테스트 세트(마지막 10,000개의 샘플)로 분할되어 있으며, 훈련 세트는 이미 뒤섞여 있습니다. 따라서 처음 50,000개의 샘플을 새 훈련 세트에, 다음 10,000개의 샘플을 검증 세트에, 마지막 10,000개의 샘플을 테스트 세트에 사용하기만 하면 됩니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "X-GkhHoOY91k"
      },
      "outputs": [],
      "source": [
        "X_train, y_train = X_mnist[:50_000], y_mnist[:50_000]\n",
        "X_valid, y_valid = X_mnist[50_000:60_000], y_mnist[50_000:60_000]\n",
        "X_test, y_test = X_mnist[60_000:], y_mnist[60_000:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iWS1cEKuY91l"
      },
      "source": [
        "문제: _그런 다음 랜덤 포레스트 분류기, 엑스트라 트리 분류기, SVM 분류기 같은 여러 종류의 분류기를 훈련시킵니다._"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "xtiiNIHGY91l"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.neural_network import MLPClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "23LsViXtY91l"
      },
      "source": [
        "사이킷런 1.5에서 `LinearSVC`의 `dual` 매개변수 기본값이 `True`에서 `\"auto\"`로 바뀔 예정입니다. 동일한 결과가 유지되도록 명시적으로 `True`로 지정합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "BS455CeCY91l"
      },
      "outputs": [],
      "source": [
        "random_forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "extra_trees_clf = ExtraTreesClassifier(n_estimators=100, random_state=42)\n",
        "svm_clf = LinearSVC(max_iter=100, tol=20, dual=True, random_state=42)\n",
        "mlp_clf = MLPClassifier(random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "ksPqfE94Y91l",
        "outputId": "a877dec2-7159-4676-cd6c-cb578086ba87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "훈련 모델: RandomForestClassifier(random_state=42)\n",
            "훈련 모델: ExtraTreesClassifier(random_state=42)\n",
            "훈련 모델: LinearSVC(max_iter=100, random_state=42, tol=20)\n",
            "훈련 모델: MLPClassifier(random_state=42)\n"
          ]
        }
      ],
      "source": [
        "estimators = [random_forest_clf, extra_trees_clf, svm_clf, mlp_clf]\n",
        "for estimator in estimators:\n",
        "    print(\"훈련 모델:\", estimator)\n",
        "    estimator.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "dqAldcseY91l",
        "outputId": "15341519-e7e2-4b4b-81dd-78930cb23b7c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9736, 0.9743, 0.8662, 0.9613]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "[estimator.score(X_valid, y_valid) for estimator in estimators]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LkPU7DnlY91l"
      },
      "source": [
        "선형 SVM은 다른 분류기보다 성능이 훨씬 뛰어납니다. 하지만 투표 기반 분류기의 성능을 향상시킬 수 있으므로 그대로 둡니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GKma-ACoY91l"
      },
      "source": [
        "문제: _그리고 검증 세트에서 개개의 분류기보다 더 높은 성능을 내도록 이들을 간접 또는 직접 투표 방법을 사용해 앙상블로 연결해보세요._"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "wRmIeurlY91m"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import VotingClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "vxNjvEdjY91m"
      },
      "outputs": [],
      "source": [
        "named_estimators = [\n",
        "    (\"random_forest_clf\", random_forest_clf),\n",
        "    (\"extra_trees_clf\", extra_trees_clf),\n",
        "    (\"svm_clf\", svm_clf),\n",
        "    (\"mlp_clf\", mlp_clf),\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "ls0_OQCqY91m"
      },
      "outputs": [],
      "source": [
        "voting_clf = VotingClassifier(named_estimators)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "2gz5obqFY91m",
        "outputId": "3ca013f0-861f-4270-bf6c-e4d35438cfd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VotingClassifier(estimators=[('random_forest_clf',\n",
              "                              RandomForestClassifier(random_state=42)),\n",
              "                             ('extra_trees_clf',\n",
              "                              ExtraTreesClassifier(random_state=42)),\n",
              "                             ('svm_clf',\n",
              "                              LinearSVC(max_iter=100, random_state=42, tol=20)),\n",
              "                             ('mlp_clf', MLPClassifier(random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 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-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 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-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 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-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 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-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;random_forest_clf&#x27;,\n",
              "                              RandomForestClassifier(random_state=42)),\n",
              "                             (&#x27;extra_trees_clf&#x27;,\n",
              "                              ExtraTreesClassifier(random_state=42)),\n",
              "                             (&#x27;svm_clf&#x27;,\n",
              "                              LinearSVC(max_iter=100, random_state=42, tol=20)),\n",
              "                             (&#x27;mlp_clf&#x27;, MLPClassifier(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 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-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;random_forest_clf&#x27;,\n",
              "                              RandomForestClassifier(random_state=42)),\n",
              "                             (&#x27;extra_trees_clf&#x27;,\n",
              "                              ExtraTreesClassifier(random_state=42)),\n",
              "                             (&#x27;svm_clf&#x27;,\n",
              "                              LinearSVC(max_iter=100, random_state=42, tol=20)),\n",
              "                             (&#x27;mlp_clf&#x27;, MLPClassifier(random_state=42))])</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\"><label>random_forest_clf</label></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-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>extra_trees_clf</label></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-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesClassifier</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svm_clf</label></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-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(max_iter=100, random_state=42, tol=20)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>mlp_clf</label></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-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "voting_clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "supW-my3Y91m",
        "outputId": "cb49d8b4-39f4-4261-83a2-86f873a5aca6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.975"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "voting_clf.score(X_valid, y_valid)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z0jkmBnEY91m"
      },
      "source": [
        "`VotingClassifier`는 각 분류기의 복제본을 만들어 원래 클래스 이름이 아닌 클래스 인덱스를 레이블로 사용하여 복제본을 훈련시켰습니다. 따라서 이러한 복제본을 평가하려면 클래스 인덱스도 제공해야 합니다. 클래스를 클래스 인덱스로 변환하기 위해 `LabelEncoder`를 사용할 수 있습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "l2cRUsm0Y91m"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "encoder = LabelEncoder()\n",
        "y_valid_encoded = encoder.fit_transform(y_valid)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qw6J8XPSY91m"
      },
      "source": [
        "그러나 MNIST의 경우 숫자가 클래스 ID와 일치하므로 클래스 이름을 정수로 변환하는 것이 더 간단합니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "uqFxmRYnY91m"
      },
      "outputs": [],
      "source": [
        "y_valid_encoded = y_valid.astype(np.int64)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWpeUFJCY91n"
      },
      "source": [
        "이제 분류기 복제본을 평가해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "PXSQXcrCY91n",
        "outputId": "c95ca2e3-5420-450e-f110-610876554381",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9736, 0.9743, 0.8662, 0.9613]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "[estimator.score(X_valid, y_valid_encoded)\n",
        " for estimator in voting_clf.estimators_]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sw68-OZ0Y91n"
      },
      "source": [
        "SVM을 제거하여 성능이 향상되는지 확인해 보겠습니다. 다음과 같이 `set_params()`를 사용하여 `\"drop\"`으로 설정하여 추정기를 제거할 수 있습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "-GQUp6ElY91n",
        "outputId": "a322bb72-e3ee-4fec-aeb3-182ebe0e797b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VotingClassifier(estimators=[('random_forest_clf',\n",
              "                              RandomForestClassifier(random_state=42)),\n",
              "                             ('extra_trees_clf',\n",
              "                              ExtraTreesClassifier(random_state=42)),\n",
              "                             ('svm_clf', 'drop'),\n",
              "                             ('mlp_clf', MLPClassifier(random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 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-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 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-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 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-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 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-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;random_forest_clf&#x27;,\n",
              "                              RandomForestClassifier(random_state=42)),\n",
              "                             (&#x27;extra_trees_clf&#x27;,\n",
              "                              ExtraTreesClassifier(random_state=42)),\n",
              "                             (&#x27;svm_clf&#x27;, &#x27;drop&#x27;),\n",
              "                             (&#x27;mlp_clf&#x27;, MLPClassifier(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 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-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;random_forest_clf&#x27;,\n",
              "                              RandomForestClassifier(random_state=42)),\n",
              "                             (&#x27;extra_trees_clf&#x27;,\n",
              "                              ExtraTreesClassifier(random_state=42)),\n",
              "                             (&#x27;svm_clf&#x27;, &#x27;drop&#x27;),\n",
              "                             (&#x27;mlp_clf&#x27;, MLPClassifier(random_state=42))])</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\"><label>random_forest_clf</label></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-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>extra_trees_clf</label></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-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesClassifier</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svm_clf</label></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-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">drop</label><div class=\"sk-toggleable__content\"><pre>drop</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>mlp_clf</label></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-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "voting_clf.set_params(svm_clf=\"drop\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "unQWhYpcY91n"
      },
      "source": [
        "이렇게 하면 추정기 목록이 업데이트됩니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "h0oZmwX2Y91n",
        "outputId": "fecfef3f-4588-4a08-dbee-b269e3aff1db",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('random_forest_clf', RandomForestClassifier(random_state=42)),\n",
              " ('extra_trees_clf', ExtraTreesClassifier(random_state=42)),\n",
              " ('svm_clf', 'drop'),\n",
              " ('mlp_clf', MLPClassifier(random_state=42))]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "voting_clf.estimators"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bNmhz-A9Y91n"
      },
      "source": [
        "그러나 _훈련된_ 추정기 목록은 업데이트하지 않습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "tLI5Mln9Y91n",
        "outputId": "dde5ffdf-6201-41cd-b7d0-1c9e2eb78d7f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[RandomForestClassifier(random_state=42),\n",
              " ExtraTreesClassifier(random_state=42),\n",
              " LinearSVC(max_iter=100, random_state=42, tol=20),\n",
              " MLPClassifier(random_state=42)]"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "voting_clf.estimators_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "Z4RedApsY91n",
        "outputId": "c5e71d39-65fe-4e21-ed50-2eade7859205",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'random_forest_clf': RandomForestClassifier(random_state=42),\n",
              " 'extra_trees_clf': ExtraTreesClassifier(random_state=42),\n",
              " 'svm_clf': LinearSVC(max_iter=100, random_state=42, tol=20),\n",
              " 'mlp_clf': MLPClassifier(random_state=42)}"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "voting_clf.named_estimators_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zjfKS5-AY91n"
      },
      "source": [
        "따라서 `VotingClassifier`를 다시 훈련하거나 `estimators_`와 `named_estimators_`에 있는 훈련된 추정기 목록에서 SVM을 제거할 수 있습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "iSGRIlECY91o"
      },
      "outputs": [],
      "source": [
        "svm_clf_trained = voting_clf.named_estimators_.pop(\"svm_clf\")\n",
        "voting_clf.estimators_.remove(svm_clf_trained)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sqtXrf6pY91o"
      },
      "source": [
        "이제 `VotingClassifier`를 다시 평가해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "KB2GCAflY91o",
        "outputId": "7cd4a9d1-223a-4f33-ec7f-d9f1c2fc1e25",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9761"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "voting_clf.score(X_valid, y_valid)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yVWTT9E_Y91o"
      },
      "source": [
        "조금 나아졌습니다! SVM이 성능을 저해하고 있었습니다. 이제 간접 투표 분류기를 사용해 보겠습니다. 실제로 분류기를 재학습할 필요 없이 `voting`를 `soft`로 설정하면 됩니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "NcSuaRjzY91o"
      },
      "outputs": [],
      "source": [
        "voting_clf.voting = \"soft\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "XQAeEZiyY91o",
        "outputId": "d51410b8-6333-47f4-ceb0-0d9d03c6c743",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9703"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "voting_clf.score(X_valid, y_valid)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uoy-faPZY91o"
      },
      "source": [
        "이런, 이 경우에는 직접 투표 방식이 더 낫군요."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUAwrhHNY91o"
      },
      "source": [
        "문제: _앙상블을 얻고 나면 테스트 세트로 확인해보세요. 개개의 분류기와 비교해서 성능이 얼마나 향상되나요?_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "GzznuxRiY91o",
        "outputId": "adee353a-3f24-4116-8387-ba6a686f0d8a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9733"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "voting_clf.voting = \"hard\"\n",
        "voting_clf.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "r_UyJ63RY91p",
        "outputId": "393f6cd9-64fd-47e3-c528-1346dfa56593",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.968, 0.9703, 0.9618]"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "[estimator.score(X_test, y_test.astype(np.int64))\n",
        " for estimator in voting_clf.estimators_]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KwcOi-HGY91p"
      },
      "source": [
        "투표 분류기는 최고 모델의 오류율을 약 3%에서 2.7%로 낮췄으며, 이는 10%의 오류를 줄인 것입니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TnpWOw6AY91p"
      },
      "source": [
        "## 9. 스태킹 앙상블"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JwNg5SZgY91p"
      },
      "source": [
        "문제: _이전 연습문제의 각 분류기를 실행해서 검증 세트에서 예측을 만들고 그 결과로 새로운 훈련 세트를 만들어보세요. 각 훈련 샘플은 하나의 이미지에 대한 전체 분류기의 예측을 담은 벡터이고 타깃은 이미지의 클래스입니다. 새로운 훈련 세트에 분류기 하나를 훈련시켜보세요._"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "wxkw7FqcY91p"
      },
      "outputs": [],
      "source": [
        "X_valid_predictions = np.empty((len(X_valid), len(estimators)), dtype=object)\n",
        "\n",
        "for index, estimator in enumerate(estimators):\n",
        "    X_valid_predictions[:, index] = estimator.predict(X_valid)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "Bl2o0gfUY91p",
        "outputId": "5e619435-2ccd-446b-abb7-2921fb348b00",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([['3', '3', '3', '3'],\n",
              "       ['8', '8', '8', '8'],\n",
              "       ['6', '6', '6', '6'],\n",
              "       ...,\n",
              "       ['5', '5', '5', '5'],\n",
              "       ['6', '6', '6', '6'],\n",
              "       ['8', '8', '8', '8']], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "X_valid_predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "CdMuDqGIY91p",
        "outputId": "3ee2db81-143e-4687-e4ac-98be6bc2980b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 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-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 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-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 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-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 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-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=200, oob_score=True, 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-38\" type=\"checkbox\" checked><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "rnd_forest_blender = RandomForestClassifier(n_estimators=200, oob_score=True,\n",
        "                                            random_state=42)\n",
        "rnd_forest_blender.fit(X_valid_predictions, y_valid)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "TpweRZQtY91p",
        "outputId": "08dd533d-1913-4927-c853-0884e88cae33",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9738"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "rnd_forest_blender.oob_score_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OOW94ZPyY91p"
      },
      "source": [
        "이 블렌더를 미세 조정하거나 다른 유형의 블렌더(예: `MLPClassifier`)를 사용해 본 다음, 항상 그렇듯이 교차 검증을 통해 가장 적합한 블렌더를 선택할 수 있습니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O9f_ROe_Y91q"
      },
      "source": [
        "문제: _축하합니다. 방금 블렌더를 훈련시켰습니다. 그리고 이 분류기를 모아서 스태킹 앙상블을 구성했습니다! 이제 테스트 세트에 앙상블을 평가해보세요. 테스트 세트의 각 이미지에 대해 모든 분류기로 예측을 만들고 앙상블의 예측 결과를 만들기 위해 블렌더에 그 예측을 주입합니다. 앞서 만든 투표 분류기와 비교하면 어떤가요?_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "2CjXP6ASY91q"
      },
      "outputs": [],
      "source": [
        "X_test_predictions = np.empty((len(X_test), len(estimators)), dtype=object)\n",
        "\n",
        "for index, estimator in enumerate(estimators):\n",
        "    X_test_predictions[:, index] = estimator.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "fpvIAEZdY91q"
      },
      "outputs": [],
      "source": [
        "y_pred = rnd_forest_blender.predict(X_test_predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "PZUp4p9KY91q",
        "outputId": "54ff60cc-90fb-42e9-c2ac-a55ffca2891b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9688"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "accuracy_score(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1hutlN63Y91q"
      },
      "source": [
        "이 스태킹 앙상블은 앞서 학습한 투표 기반 분류기보다 성능이 좋지 않습니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UTD-ADfSY91q"
      },
      "source": [
        "문제: _이제 `StackingClassifier`를 사용하여 다시 시도해 보세요. 성능이 더 좋아졌나요? 그렇다면 그 이유는 무엇인가요?_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0kbeghjyY91q"
      },
      "source": [
        "`StackingClassifier`는 K-겹 교차 검증을 사용하기 때문에 별도의 검증 세트가 필요하지 않으므로 훈련 세트와 검증 세트를 더 큰 훈련 세트로 합쳐보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "yW8rJBM9Y91q"
      },
      "outputs": [],
      "source": [
        "X_train_full, y_train_full = X_mnist[:60_000], y_mnist[:60_000]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_YlvRoctY91q"
      },
      "source": [
        "이제 전체 훈련 세트에서 스태킹 분류기를 생성하고 훈련해 보겠습니다:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d_kv_hALY91q"
      },
      "source": [
        "**경고**: 다음 셀은 기본적으로 5-겹 검증을 사용하기 때문에 실행하는 데 시간이 꽤 오래 걸립니다(하드웨어에 따라 15~30분 정도 소요). 이 셀은 4개의 분류기를 전체 훈련 세트의 80%에 대해 각각 5회씩 훈련하여 예측을 하고, 마지막으로 전체 훈련 세트에 대해 각각 한 번씩 훈련하여 예측에 대한 최종 모델을 훈련합니다. 총 25개의 모델을 훈련해야 합니다!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "bSxTUGg5Y91q",
        "outputId": "a37895f3-b56e-4274-b1de-513faddfcac1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StackingClassifier(estimators=[('random_forest_clf',\n",
              "                                RandomForestClassifier(random_state=42)),\n",
              "                               ('extra_trees_clf',\n",
              "                                ExtraTreesClassifier(random_state=42)),\n",
              "                               ('svm_clf',\n",
              "                                LinearSVC(max_iter=100, random_state=42,\n",
              "                                          tol=20)),\n",
              "                               ('mlp_clf', MLPClassifier(random_state=42))],\n",
              "                   final_estimator=RandomForestClassifier(n_estimators=200,\n",
              "                                                          oob_score=True,\n",
              "                                                          random_state=42))"
            ],
            "text/html": [
              "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 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-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 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-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 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-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 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-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(estimators=[(&#x27;random_forest_clf&#x27;,\n",
              "                                RandomForestClassifier(random_state=42)),\n",
              "                               (&#x27;extra_trees_clf&#x27;,\n",
              "                                ExtraTreesClassifier(random_state=42)),\n",
              "                               (&#x27;svm_clf&#x27;,\n",
              "                                LinearSVC(max_iter=100, random_state=42,\n",
              "                                          tol=20)),\n",
              "                               (&#x27;mlp_clf&#x27;, MLPClassifier(random_state=42))],\n",
              "                   final_estimator=RandomForestClassifier(n_estimators=200,\n",
              "                                                          oob_score=True,\n",
              "                                                          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 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-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StackingClassifier</label><div class=\"sk-toggleable__content\"><pre>StackingClassifier(estimators=[(&#x27;random_forest_clf&#x27;,\n",
              "                                RandomForestClassifier(random_state=42)),\n",
              "                               (&#x27;extra_trees_clf&#x27;,\n",
              "                                ExtraTreesClassifier(random_state=42)),\n",
              "                               (&#x27;svm_clf&#x27;,\n",
              "                                LinearSVC(max_iter=100, random_state=42,\n",
              "                                          tol=20)),\n",
              "                               (&#x27;mlp_clf&#x27;, MLPClassifier(random_state=42))],\n",
              "                   final_estimator=RandomForestClassifier(n_estimators=200,\n",
              "                                                          oob_score=True,\n",
              "                                                          random_state=42))</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>random_forest_clf</label></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-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>extra_trees_clf</label></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-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesClassifier</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svm_clf</label></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-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(max_iter=100, random_state=42, tol=20)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>mlp_clf</label></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-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>final_estimator</label></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-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "stack_clf = StackingClassifier(named_estimators,\n",
        "                               final_estimator=rnd_forest_blender)\n",
        "stack_clf.fit(X_train_full, y_train_full)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "r-dp87AYY91r",
        "outputId": "42c60b3e-dba0-4399-ce75-789f92d96a56",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9788"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "stack_clf.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EutW6FsLY91r"
      },
      "source": [
        "`StackingClassifier`는 앞서 시도한 사용자 정의 스태킹 구현보다 훨씬 뛰어난 성능을 발휘합니다! 이는 크게 두 가지 이유 때문입니다:\n",
        "\n",
        "* 검증 세트를 별도로 사용하지 않았기 때문에 더 큰 데이터 집합에 대해 `StackingClassifier`를 학습시켰습니다.\n",
        "* 사용 가능한 경우 `predict_proba()`를, 그렇지 않으면 `decision_function()`을, 그렇지 않으면 `predict()`를 사용했습니다. 이렇게 하면 블렌더가 훨씬 더 미묘한 입력을 처리할 수 있습니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Re_RY1BgY91r"
      },
      "source": [
        "오늘은 여기까지입니다. 이 장의 코드와 연습 문제를 모두 완료하신 것을 축하드립니다!"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.9"
    },
    "nav_menu": {
      "height": "252px",
      "width": "333px"
    },
    "toc": {
      "navigate_menu": true,
      "number_sections": true,
      "sideBar": true,
      "threshold": 6,
      "toc_cell": false,
      "toc_section_display": "block",
      "toc_window_display": false
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}