5_2_Hyperparameter_Tuning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dVNF7yZjyvoO"
      },
      "source": [
        "## validation set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "banlvMA6RfnM"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "wine = pd.read_csv('https://bit.ly/wine_csv_data')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "abR6QA7qRoKl"
      },
      "outputs": [],
      "source": [
        "data = wine[['alcohol', 'sugar', 'pH']].to_numpy()\n",
        "target = wine['class'].to_numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "auLnVXyMRoeb"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_input, test_input, train_target, test_target = train_test_split(\n",
        "    data, target, test_size=0.2, random_state=42)\n",
        "\n",
        "# train data에서 validation set 나누기\n",
        "sub_input, val_input, sub_target, val_target = train_test_split(train_input, train_target, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dt = DecisionTreeClassifier(random_state=42)\n",
        "# train\n",
        "dt.fit(sub_input, sub_target)\n",
        "\n",
        "# score\n",
        "print(dt.score(sub_input, sub_target))\n",
        "# test set이 아닌 validation set으로 평가\n",
        "print(dt.score(val_input, val_target))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ntghkWecuzra",
        "outputId": "73503ca9-e101-494d-e96a-cad402eeabd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9971133028626413\n",
            "0.864423076923077\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z4gRXnK6y2Pt"
      },
      "source": [
        "## cross validation"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_validate\n",
        "# cross_validate() 함수는 기본적으로 5-fold cross validation을 수행\n",
        "# cv 매개변수 값으로 fold 수 지정 가능\n",
        "scores = cross_validate(dt, train_input, train_target)\n",
        "print(scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6TCbCESjxruX",
        "outputId": "d98fbefb-be16-4ab4-b1a3-1bac340f6591"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'fit_time': array([0.01016903, 0.0116148 , 0.01081276, 0.01055217, 0.01019025]), 'score_time': array([0.00277519, 0.00162005, 0.00159836, 0.00161958, 0.00267005]), 'test_score': array([0.86923077, 0.84615385, 0.87680462, 0.84889317, 0.83541867])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yp3aagOoTHsO",
        "outputId": "86399ba5-bca9-4cae-b98d-5607d791e493"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.855300214703487\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "# 평균 계산\n",
        "print(np.mean(scores['test_score']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0tQyaG0576Vn"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "# cv 매개변수로 splitter 지정\n",
        "scores = cross_validate(dt, train_input, train_target, cv=StratifiedKFold())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1BmP_OTT_agM"
      },
      "outputs": [],
      "source": [
        "# splitter 클래스의 n_splits 매개변수 값으로 fold 개수(k) 지정\n",
        "# 10-fold cross validation\n",
        "splitter = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
        "scores = cross_validate(dt, train_input, train_target, cv=splitter)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q21W8RsqDsDV"
      },
      "source": [
        "## 하이퍼파라미터 튜닝"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# 매개변수와 후보값들의 리스트를 dictionary로 만들기\n",
        "params = {'min_impurity_decrease': [0.0001, 0.0002, 0.0003, 0.0004, 0.0005]}\n",
        "\n",
        "# params를 전달하며 Grid Search 객체 생성\n",
        "# n_jobs 매개변수는 병렬 실행에 사용할 CPU 코어 수 지정: -1로 지정하면 모든 코어를 사용\n",
        "# cv 매개변수 기본값은 5 (5-fold cross validation)\n",
        "gs = GridSearchCV(DecisionTreeClassifier(random_state=42), params, n_jobs=-1)\n",
        "\n",
        "# params 값마다 5-fold cross validation 수행\n",
        "gs.fit(train_input, train_target)\n",
        "# 검증이 끝나면 score가 가장 높은 모델의 매개변수 조합으로 전체 train set에서 모델 훈련하고 gs.best_estimator_ 속성에 저장"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "TnkoOdzX_BRd",
        "outputId": "9a3e913a-c56b-4130-bbde-f72bc9da2d68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,\n",
              "             param_grid={'min_impurity_decrease': [0.0001, 0.0002, 0.0003,\n",
              "                                                   0.0004, 0.0005]})"
            ],
            "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>GridSearchCV(estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,\n",
              "             param_grid={&#x27;min_impurity_decrease&#x27;: [0.0001, 0.0002, 0.0003,\n",
              "                                                   0.0004, 0.0005]})</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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,\n",
              "             param_grid={&#x27;min_impurity_decrease&#x27;: [0.0001, 0.0002, 0.0003,\n",
              "                                                   0.0004, 0.0005]})</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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q6iX3vH-VeEb",
        "outputId": "5621e0e4-2df8-4351-d3fa-5a1b429b87b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9615162593804117\n"
          ]
        }
      ],
      "source": [
        "dt = gs.best_estimator_\n",
        "# 저장된 모델의 score\n",
        "print(dt.score(train_input, train_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lIzod3BwVHq-",
        "outputId": "39c93d2c-167b-4d12-d8d9-d436f3eee66c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'min_impurity_decrease': 0.0001}\n"
          ]
        }
      ],
      "source": [
        "# best_params_ 속성에 찾은 최적의 매개변수 저장됨\n",
        "print(gs.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8jHxZ7XmVU11"
      },
      "outputs": [],
      "source": [
        "# 넘파이 arange()나 파이썬 range()를 이용해 배열을 만들어 매개변수로 줄 수 있음\n",
        "# params = {'min_impurity_decrease': np.arange(0.0001, 0.001, 0.0001),\n",
        "#           'max_depth': range(5, 20, 1),\n",
        "#           # min_samples_split: 노드를 나누는 최소 샘플 수\n",
        "#           'min_samples_split': range(2, 100, 10)\n",
        "#           }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0k9DQTNlaD6"
      },
      "source": [
        "### 랜덤 서치"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 확률 분포 클래스\n",
        "from scipy.stats import uniform, randint"
      ],
      "metadata": {
        "id": "m4H0yTN1teIa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 0~10 사이의(0~9까지 포함) 범위\n",
        "rgen = randint(0, 10)\n",
        "# 10개의 정수 샘플링\n",
        "rgen.rvs(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iCsc3YqovD9M",
        "outputId": "9fb800fc-aaaa-49db-f9ff-14ea4cf5b9e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 1, 8, 5, 4, 7, 2, 6, 9, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ch3zTUohIJR6",
        "outputId": "3cf5e85f-44fb-4df3-b48a-c7ae0683a62a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
              " array([106,  96, 106,  98,  93,  88, 119,  87, 104, 103]))"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "# 1000개 샘플링하여 각 정수값의 개수 세기\n",
        "np.unique(rgen.rvs(1000), return_counts=True)\n",
        "\n",
        "# 결과를 확인하면 어느정도 균등하게 샘플링됨"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bGhshTn0IjkI",
        "outputId": "5af46476-3c7d-4c02-de93-18680960c8e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.76125445, 0.29597466, 0.00650326, 0.70617209, 0.69318374,\n",
              "       0.64129573, 0.72857217, 0.25058781, 0.27633346, 0.76252958])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "# 0~1 사이의 범위\n",
        "ugen = uniform(0, 1)\n",
        "# 10개의 실수 샘플링\n",
        "ugen.rvs(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "irDX9e6WYTIH"
      },
      "outputs": [],
      "source": [
        "# 매개변수\n",
        "# 탐색할 매개변수 범위를 지정\n",
        "params = {'min_impurity_decrease': uniform(0.0001, 0.001),\n",
        "          'max_depth': randint(20, 50),\n",
        "          'min_samples_split': randint(2, 25),\n",
        "          # min_samples_leaf: 리프 노드의 최소 샘플 개수(분할하여 만들어질 자식 노드의 최소 샘플 개수) - 이 값보다 작으면 분할하지 않음\n",
        "          'min_samples_leaf': randint(1, 25),\n",
        "          }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "Wc4OIingWQCK",
        "outputId": "a732b8d5-be29-448e-f1be-15b1d8f5ff36"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(estimator=DecisionTreeClassifier(random_state=42),\n",
              "                   n_iter=100, n_jobs=-1,\n",
              "                   param_distributions={'max_depth': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7e0e64f66a10>,\n",
              "                                        'min_impurity_decrease': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7e0e64fb2b90>,\n",
              "                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7e0e64fb2f50>,\n",
              "                                        'min_samples_split': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7e0e64fb3730>},\n",
              "                   random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(estimator=DecisionTreeClassifier(random_state=42),\n",
              "                   n_iter=100, n_jobs=-1,\n",
              "                   param_distributions={&#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7e0e64f66a10&gt;,\n",
              "                                        &#x27;min_impurity_decrease&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7e0e64fb2b90&gt;,\n",
              "                                        &#x27;min_samples_leaf&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7e0e64fb2f50&gt;,\n",
              "                                        &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7e0e64fb3730&gt;},\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-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(estimator=DecisionTreeClassifier(random_state=42),\n",
              "                   n_iter=100, n_jobs=-1,\n",
              "                   param_distributions={&#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7e0e64f66a10&gt;,\n",
              "                                        &#x27;min_impurity_decrease&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7e0e64fb2b90&gt;,\n",
              "                                        &#x27;min_samples_leaf&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7e0e64fb2f50&gt;,\n",
              "                                        &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7e0e64fb3730&gt;},\n",
              "                   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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "# 랜덤 서치 클래스\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "# params를 전달하며 랜덤 서치 객체 생성\n",
        "# n_iter 값으로 샘플링 횟수 지정 - 매개변수 범위에서 100번 샘플링하여 교차 검증 수행\n",
        "gs = RandomizedSearchCV(DecisionTreeClassifier(random_state=42), params,\n",
        "                        n_iter=100, n_jobs=-1, random_state=42)\n",
        "gs.fit(train_input, train_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3QV7yRpidByf",
        "outputId": "04894cc6-d51e-4a67-deae-29eb6ae533f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8928227823744468\n",
            "0.86\n"
          ]
        }
      ],
      "source": [
        "  dt = gs.best_estimator_\n",
        "  # 저장된 모델의 score\n",
        "  print(dt.score(train_input, train_target))\n",
        "  print(dt.score(test_input, test_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p7IbsGH3ZSv-",
        "outputId": "324ffee9-8f13-45cf-928f-514a0c07a036"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'max_depth': 39, 'min_impurity_decrease': 0.00034102546602601173, 'min_samples_leaf': 7, 'min_samples_split': 13}\n"
          ]
        }
      ],
      "source": [
        "# best_params_ 속성에 찾은 최적의 매개변수 저장됨\n",
        "print(gs.best_params_)"
      ]
    }
  ],
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