5-2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rOafwFRXG7fb"
      },
      "source": [
        "# 교차 검증과 그리드 서치"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6YuimmymG7fi"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/5-2.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dVNF7yZjyvoO"
      },
      "source": [
        "## 검증 세트"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "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": 2,
      "metadata": {
        "id": "abR6QA7qRoKl"
      },
      "outputs": [],
      "source": [
        "data = wine[['alcohol', 'sugar', 'pH']].to_numpy()\n",
        "target = wine['class'].to_numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "E-yV4cCXRqNK"
      },
      "outputs": [],
      "source": [
        "sub_input, val_input, sub_target, val_target = train_test_split(\n",
        "    train_input, train_target, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k29hKbw4R7Ki",
        "outputId": "17fb7b68-2b4a-4c0d-a026-5b8af9b79ea6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4157, 3) (1040, 3)\n"
          ]
        }
      ],
      "source": [
        "print(sub_input.shape, val_input.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4VQz-UZ2SeLq",
        "outputId": "c02a1d4d-a2a4-4ec4-f1e3-425bbc976e44"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9971133028626413\n",
            "0.864423076923077\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dt = DecisionTreeClassifier(random_state=42)\n",
        "dt.fit(sub_input, sub_target)\n",
        "\n",
        "print(dt.score(sub_input, sub_target))\n",
        "print(dt.score(val_input, val_target))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z4gRXnK6y2Pt"
      },
      "source": [
        "## 교차 검증"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_J3LId-vSmNH",
        "outputId": "5f49522d-6e1b-42b3-9c5c-ccd068038dec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'fit_time': array([0.00931716, 0.00749564, 0.00773239, 0.00731683, 0.00710797]), 'score_time': array([0.00109315, 0.00111032, 0.00101209, 0.00106931, 0.00115085]), 'test_score': array([0.86923077, 0.84615385, 0.87680462, 0.84889317, 0.83541867])}\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_validate\n",
        "\n",
        "scores = cross_validate(dt, train_input, train_target)\n",
        "print(scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yp3aagOoTHsO",
        "outputId": "cebb0e68-1b94-4c90-84ab-35156c4fa01c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.855300214703487\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "print(np.mean(scores['test_score']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0tQyaG0576Vn",
        "outputId": "0835bd01-4940-4093-9576-c1d3b8d6e40a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.855300214703487\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "scores = cross_validate(dt, train_input, train_target, cv=StratifiedKFold())\n",
        "print(np.mean(scores['test_score']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1BmP_OTT_agM",
        "outputId": "16610b69-fdd4-424d-a18a-17e0851b8171"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8574181117533719\n"
          ]
        }
      ],
      "source": [
        "splitter = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
        "scores = cross_validate(dt, train_input, train_target, cv=splitter)\n",
        "print(np.mean(scores['test_score']))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q21W8RsqDsDV"
      },
      "source": [
        "## 하이퍼파라미터 튜닝"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "S8pqss8onjR5"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "params = {'min_impurity_decrease': [0.0001, 0.0002, 0.0003, 0.0004, 0.0005]}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "79MymJqxTu0P"
      },
      "outputs": [],
      "source": [
        "gs = GridSearchCV(DecisionTreeClassifier(random_state=42), params, n_jobs=-1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "tKAlTabkU-Lz",
        "outputId": "4125edd4-08d4-4932-ad11-2108550de929"
      },
      "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-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>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-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" 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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" 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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" 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": 13
        }
      ],
      "source": [
        "gs.fit(train_input, train_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q6iX3vH-VeEb",
        "outputId": "b68ad258-4e2a-4e62-f882-9ebea921942e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9615162593804117\n"
          ]
        }
      ],
      "source": [
        "dt = gs.best_estimator_\n",
        "print(dt.score(train_input, train_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lIzod3BwVHq-",
        "outputId": "fc1ea12a-2a33-4ebe-8438-2c9991e6da1a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'min_impurity_decrease': 0.0001}\n"
          ]
        }
      ],
      "source": [
        "print(gs.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0xfQswiui4Tr",
        "outputId": "7a74abdb-a237-49a0-d8cd-fe97fa9808bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.86819297 0.86453617 0.86492226 0.86780891 0.86761605]\n"
          ]
        }
      ],
      "source": [
        "print(gs.cv_results_['mean_test_score'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rwg2aSyEVO17",
        "outputId": "b358e38f-22e4-4a1b-e688-866cd78398fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'min_impurity_decrease': 0.0001}\n"
          ]
        }
      ],
      "source": [
        "best_index = np.argmax(gs.cv_results_['mean_test_score'])\n",
        "print(gs.cv_results_['params'][best_index])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "8jHxZ7XmVU11"
      },
      "outputs": [],
      "source": [
        "params = {'min_impurity_decrease': np.arange(0.0001, 0.001, 0.0001),\n",
        "          'max_depth': range(5, 20, 1),\n",
        "          'min_samples_split': range(2, 100, 10)\n",
        "          }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "KnP3GA6MVsVH",
        "outputId": "7540ba6a-bda4-4c9e-beca-484dd8ef540f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,\n",
              "             param_grid={'max_depth': range(5, 20),\n",
              "                         'min_impurity_decrease': array([0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007, 0.0008,\n",
              "       0.0009]),\n",
              "                         'min_samples_split': range(2, 100, 10)})"
            ],
            "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;max_depth&#x27;: range(5, 20),\n",
              "                         &#x27;min_impurity_decrease&#x27;: array([0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007, 0.0008,\n",
              "       0.0009]),\n",
              "                         &#x27;min_samples_split&#x27;: range(2, 100, 10)})</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;max_depth&#x27;: range(5, 20),\n",
              "                         &#x27;min_impurity_decrease&#x27;: array([0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007, 0.0008,\n",
              "       0.0009]),\n",
              "                         &#x27;min_samples_split&#x27;: range(2, 100, 10)})</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": 19
        }
      ],
      "source": [
        "gs = GridSearchCV(DecisionTreeClassifier(random_state=42), params, n_jobs=-1)\n",
        "gs.fit(train_input, train_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qi9-O_VGV0Ho",
        "outputId": "c3eae726-486f-4a1d-f4b4-7074715fd6b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'max_depth': 14, 'min_impurity_decrease': 0.0004, 'min_samples_split': 12}\n"
          ]
        }
      ],
      "source": [
        "print(gs.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZnJjLATAV2Sq",
        "outputId": "81351bd9-ec0b-416f-963a-f07fbbf6a0d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8683865773302731\n"
          ]
        }
      ],
      "source": [
        "print(np.max(gs.cv_results_['mean_test_score']))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0k9DQTNlaD6"
      },
      "source": [
        "### 랜덤 서치"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "_T9KTEk1GBcY"
      },
      "outputs": [],
      "source": [
        "from scipy.stats import uniform, randint"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fd0UJpCGGDhz",
        "outputId": "d5472c8c-2bfc-4ee0-aae5-e2b613f1f4b0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 7, 6, 8, 9, 3, 8, 3, 1, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "rgen = randint(0, 10)\n",
        "rgen.rvs(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ch3zTUohIJR6",
        "outputId": "95ae5d40-2edb-43b5-c01f-40d16eb517b1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
              " array([116, 105,  95, 100,  84,  90,  97,  95, 107, 111]))"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "np.unique(rgen.rvs(1000), return_counts=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bGhshTn0IjkI",
        "outputId": "bd9ff812-9b11-4b1e-dd9a-9f9a7010469d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.07156624, 0.51330724, 0.78244744, 0.14237963, 0.05055468,\n",
              "       0.13124955, 0.15801332, 0.99110938, 0.08459786, 0.92447632])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "ugen = uniform(0, 1)\n",
        "ugen.rvs(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "irDX9e6WYTIH"
      },
      "outputs": [],
      "source": [
        "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': randint(1, 25),\n",
        "          }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "Wc4OIingWQCK",
        "outputId": "f1783475-8d2e-4cb3-845f-643fcc517ed5"
      },
      "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 0x7cccce351cc0>,\n",
              "                                        'min_impurity_decrease': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7cccce2f4610>,\n",
              "                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7cccce352da0>,\n",
              "                                        'min_samples_split': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7cccce353bb0>},\n",
              "                   random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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 0x7cccce351cc0&gt;,\n",
              "                                        &#x27;min_impurity_decrease&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7cccce2f4610&gt;,\n",
              "                                        &#x27;min_samples_leaf&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7cccce352da0&gt;,\n",
              "                                        &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7cccce353bb0&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-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" 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 0x7cccce351cc0&gt;,\n",
              "                                        &#x27;min_impurity_decrease&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7cccce2f4610&gt;,\n",
              "                                        &#x27;min_samples_leaf&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7cccce352da0&gt;,\n",
              "                                        &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7cccce353bb0&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-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\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": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p7IbsGH3ZSv-",
        "outputId": "b2f963f8-7f7a-4af0-98aa-20eac00d265a"
      },
      "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": [
        "print(gs.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dYI3HwMQbtnr",
        "outputId": "2cb232e6-28d8-42b0-db66-bfa0bc58832d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8695428296438884\n"
          ]
        }
      ],
      "source": [
        "print(np.max(gs.cv_results_['mean_test_score']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3QV7yRpidByf",
        "outputId": "f449fcd7-ad05-45b8-a86e-7f03bc565937"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.86\n"
          ]
        }
      ],
      "source": [
        "dt = gs.best_estimator_\n",
        "\n",
        "print(dt.score(test_input, test_target))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cA42IsMdhgE7"
      },
      "source": [
        "## 확인문제"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "8qxg36iThiUm",
        "outputId": "f2c84c8b-a07f-4b61-a708-a869c685b5ed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(estimator=DecisionTreeClassifier(random_state=42,\n",
              "                                                    splitter='random'),\n",
              "                   n_iter=100, n_jobs=-1,\n",
              "                   param_distributions={'max_depth': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7cccce351cc0>,\n",
              "                                        'min_impurity_decrease': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7cccce2f4610>,\n",
              "                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7cccce352da0>,\n",
              "                                        'min_samples_split': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7cccce353bb0>},\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",
              "                                                    splitter=&#x27;random&#x27;),\n",
              "                   n_iter=100, n_jobs=-1,\n",
              "                   param_distributions={&#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7cccce351cc0&gt;,\n",
              "                                        &#x27;min_impurity_decrease&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7cccce2f4610&gt;,\n",
              "                                        &#x27;min_samples_leaf&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7cccce352da0&gt;,\n",
              "                                        &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7cccce353bb0&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",
              "                                                    splitter=&#x27;random&#x27;),\n",
              "                   n_iter=100, n_jobs=-1,\n",
              "                   param_distributions={&#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7cccce351cc0&gt;,\n",
              "                                        &#x27;min_impurity_decrease&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7cccce2f4610&gt;,\n",
              "                                        &#x27;min_samples_leaf&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7cccce352da0&gt;,\n",
              "                                        &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7cccce353bb0&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, splitter=&#x27;random&#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-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, splitter=&#x27;random&#x27;)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "gs = RandomizedSearchCV(DecisionTreeClassifier(splitter='random', 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": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CMZ4UE8ihqwg",
        "outputId": "5e7389ae-bf93-404e-eb2f-04f50af7da60"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'max_depth': 43, 'min_impurity_decrease': 0.00011407982271508446, 'min_samples_leaf': 19, 'min_samples_split': 18}\n",
            "0.8458726956392981\n",
            "0.786923076923077\n"
          ]
        }
      ],
      "source": [
        "print(gs.best_params_)\n",
        "print(np.max(gs.cv_results_['mean_test_score']))\n",
        "\n",
        "dt = gs.best_estimator_\n",
        "print(dt.score(test_input, test_target))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "5-2 교차 검증과 그리드 서치.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "default:Python",
      "language": "python",
      "name": "conda-env-default-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}