07_ensemble_learning_and_random_forests.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**7장 - 앙상블 학습과 랜덤 포레스트**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_이 노트북에는 7장의 모든 샘플 코드와 연습 문제에 대한 솔루션이 포함되어 있습니다._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": []
   },
   "source": [
    "# 설정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 프로젝트에는 파이썬 3.7 이상이 필요합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "assert sys.version_info >= (3, 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "또한 사이킷런 ≥ 1.0.1이 필요합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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": {},
   "source": [
    "이전 챕터에서와 마찬가지로 기본 글꼴 크기를 정의하여 그림을 더 예쁘게 만들어 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rc('font', size=14)\n",
    "plt.rc('axes', labelsize=14, titlesize=14)\n",
    "plt.rc('legend', fontsize=14)\n",
    "plt.rc('xtick', labelsize=10)\n",
    "plt.rc('ytick', labelsize=10)\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": {},
   "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": {},
   "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": {},
   "source": [
    "# 투표 기반 분류기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": {},
   "source": [
    "투표 기반 분류기를 만들어 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "text/plain": [
       "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n",
       "                             ('rf', RandomForestClassifier(random_state=42)),\n",
       "                             ('svc', SVC(random_state=42))])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.predict(X_test[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([1]), array([1]), array([0])]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[clf.predict(X_test[:1]) for clf in voting_clf.estimators_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.912"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 간접 투표를 사용해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "source": [
    "# 배깅과 페이스팅\n",
    "## 사이킷런의 배깅과 페이스팅"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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(base_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(base_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\">base_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>"
      ],
      "text/plain": [
       "BaggingClassifier(base_estimator=DecisionTreeClassifier(), max_samples=100,\n",
       "                  n_estimators=500, n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": {},
   "source": [
    "## OOB 평가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.896"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.32352941, 0.67647059],\n",
       "       [0.3375    , 0.6625    ],\n",
       "       [1.        , 0.        ]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf.oob_decision_function_[:3]  # probas for the first 3 instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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": {},
   "source": [
    "# 랜덤 포레스트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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": {},
   "source": [
    "랜덤 포레스트는 결정 트리의 배깅과 같습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "source": [
    "## 특성 중요도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": {},
   "source": [
    "# 부스팅\n",
    "## AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": {},
   "outputs": [
    {
     "data": {
      "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(base_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(base_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\">base_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>"
      ],
      "text/plain": [
       "AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1),\n",
       "                   learning_rate=0.5, n_estimators=30, random_state=42)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": {},
   "source": [
    "## 그레이디언트 부스팅"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "간단한 2차방정식 데이터셋을 만들고 여기에 `DecisionTreeRegressor`를 훈련시켜 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "source": [
    "이제 이전 예측기가 만든 잔여 오차에 대해 다른 결정 트리 회귀를 훈련해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=43)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=44)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.49484029, 0.04021166, 0.75026781])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1100x1100 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": {},
   "source": [
    "이제 그레이디언트 부스팅 회귀를 시도해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "text/plain": [
       "GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,\n",
       "                          random_state=42)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "text/plain": [
       "GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=500,\n",
       "                          n_iter_no_change=10, random_state=42)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbrt_best.n_estimators_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": {},
   "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": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "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))])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "source": [
    "# 스태킹"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "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))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.928"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacking_clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 연습문제 해답"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. to 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "부록 A 참조"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. 투표 기반 분류기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "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": {},
   "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": {},
   "source": [
    "문제: _그런 다음 랜덤 포레스트 분류기, 엑스트라 트리 분류기, SVM 분류기 같은 여러 종류의 분류기를 훈련시킵니다._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9736, 0.9743, 0.8662, 0.9626]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[estimator.score(X_valid, y_valid) for estimator in estimators]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "선형 SVM은 다른 분류기보다 성능이 훨씬 뛰어납니다. 하지만 투표 기반 분류기의 성능을 향상시킬 수 있으므로 그대로 둡니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "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": {},
   "outputs": [],
   "source": [
    "voting_clf = VotingClassifier(named_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "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))])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9746"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.score(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "y_valid_encoded = encoder.fit_transform(y_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그러나 MNIST의 경우 숫자가 클래스 ID와 일치하므로 클래스 이름을 정수로 변환하는 것이 더 간단합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_valid_encoded = y_valid.astype(np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 분류기 복제본을 평가해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9736, 0.9743, 0.8662, 0.9626]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[estimator.score(X_valid, y_valid_encoded)\n",
    " for estimator in voting_clf.estimators_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "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))])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.set_params(svm_clf=\"drop\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이렇게 하면 추정기 목록이 업데이트됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "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))]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.estimators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그러나 _훈련된_ 추정기 목록은 업데이트하지 않습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "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)]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.estimators_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "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)}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.named_estimators_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "outputs": [],
   "source": [
    "svm_clf_trained = voting_clf.named_estimators_.pop(\"svm_clf\")\n",
    "voting_clf.estimators_.remove(svm_clf_trained)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 `VotingClassifier`를 다시 평가해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9758"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.score(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "outputs": [],
   "source": [
    "voting_clf.voting = \"soft\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9693"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.score(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이런, 이 경우에는 직접 투표 방식이 더 낫군요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "문제: _앙상블을 얻고 나면 테스트 세트로 확인해보세요. 개개의 분류기와 비교해서 성능이 얼마나 향상되나요?_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9721"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.voting = \"hard\"\n",
    "voting_clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.968, 0.9703, 0.9622]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[estimator.score(X_test, y_test.astype(np.int64))\n",
    " for estimator in voting_clf.estimators_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "투표 분류기는 최고 모델의 오류율을 약 3%에서 2.7%로 낮췄으며, 이는 10%의 오류를 줄인 것입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. 스태킹 앙상블"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "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": {},
   "outputs": [
    {
     "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)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_valid_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9728"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_forest_blender.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "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": {},
   "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": {},
   "outputs": [],
   "source": [
    "y_pred = rnd_forest_blender.predict(X_test_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9694"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 스태킹 앙상블은 앞서 학습한 투표 기반 분류기보다 성능이 좋지 않습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "문제: _이제 `StackingClassifier`를 사용하여 다시 시도해 보세요. 성능이 더 좋아졌나요? 그렇다면 그 이유는 무엇인가요?_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "outputs": [],
   "source": [
    "X_train_full, y_train_full = X_mnist[:60_000], y_mnist[:60_000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 전체 훈련 세트에서 스태킹 분류기를 생성하고 훈련해 보겠습니다:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "outputs": [
    {
     "data": {
      "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>"
      ],
      "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))"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9793"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack_clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "source": [
    "오늘은 여기까지입니다. 이 장의 코드와 연습 문제를 모두 완료하신 것을 축하드립니다!"
   ]
  }
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