하이퍼패러미터튜닝_예시.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ralbu85/DataScience_2022S/blob/master/%ED%95%98%EC%9D%B4%ED%8D%BC%ED%8C%A8%EB%9F%AC%EB%AF%B8%ED%84%B0%ED%8A%9C%EB%8B%9D_%EC%98%88%EC%8B%9C.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 데이터셋 불러오기"
      ],
      "metadata": {
        "id": "OoMK_b6zoxxd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "VFIrQk0zos9P"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('auto.csv')\n",
        "df2 = pd.get_dummies(df, columns=['origin'])\n",
        "y = df2['mpg']\n",
        "X = df2.drop(columns=['mpg'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 파이프라이닝"
      ],
      "metadata": {
        "id": "GWBBo4WopG6H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler # 데이터를 표준화\n",
        "from sklearn.neighbors import KNeighborsRegressor # KNN 회귀모형\n"
      ],
      "metadata": {
        "id": "08Xp4NHepIK9"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## 파이프라인 하기 전\n",
        "scaler = StandardScaler() # 표준화 객체\n",
        "X_scaled = scaler.fit_transform(X) # 표준화 시키기\n",
        "knn = KNeighborsRegressor(n_neighbors=5) # 하이퍼패러미터 k=5\n",
        "knn.fit(X_scaled,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "zr2HQvHMqL4I",
        "outputId": "74c75b54-2cb7-46c3-c81e-45379f1a08b2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsRegressor()"
            ],
            "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>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "scaler = StandardScaler() # 표준화 객체\n",
        "knn = KNeighborsRegressor(n_neighbors=5) #KNN 객체\n",
        "steps = [('scaler',scaler), ('knn',knn)]\n",
        "pipe = Pipeline(steps)\n",
        "pipe.fit(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "35yYXcELrHdn",
        "outputId": "3d4542de-b8b3-4b30-eea1-ccdad5d84341"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('scaler', StandardScaler()), ('knn', KNeighborsRegressor())])"
            ],
            "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>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;knn&#x27;, KNeighborsRegressor())])</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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;knn&#x27;, KNeighborsRegressor())])</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\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe.predict(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vx_2fn-2sEgZ",
        "outputId": "b721ea09-e73c-4544-dfe6-04f3eeccd9dc"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([17.2 , 15.6 , 16.8 , 17.2 , 17.2 , 13.6 , 14.6 , 14.4 , 13.6 ,\n",
              "       14.6 , 14.8 , 15.2 , 15.4 , 14.2 , 25.4 , 20.  , 20.  , 21.  ,\n",
              "       25.4 , 27.6 , 23.8 , 25.6 , 25.6 , 24.4 , 20.  , 11.2 , 10.4 ,\n",
              "       11.2 , 10.6 , 25.4 , 24.4 , 25.2 , 18.8 , 17.8 , 17.8 , 18.2 ,\n",
              "       17.8 , 13.8 , 13.2 , 14.2 , 14.4 , 12.8 , 13.2 , 12.8 , 18.4 ,\n",
              "       21.6 , 17.8 , 18.2 , 25.8 , 25.6 , 27.6 , 26.4 , 29.8 , 31.2 ,\n",
              "       27.6 , 21.6 , 24.6 , 23.  , 26.8 , 21.  , 24.8 , 13.6 , 13.4 ,\n",
              "       14.2 , 14.2 , 15.2 , 12.6 , 13.2 , 13.2 , 13.2 , 22.4 , 15.2 ,\n",
              "       14.4 , 13.8 , 14.2 , 20.6 , 23.8 , 23.8 , 26.8 , 24.8 , 24.2 ,\n",
              "       24.  , 25.8 , 25.4 , 13.  , 15.2 , 14.4 , 14.2 , 14.8 , 12.  ,\n",
              "       13.  , 13.  , 14.6 , 12.8 , 12.4 , 12.8 , 19.6 , 16.6 , 19.2 ,\n",
              "       19.2 , 19.6 , 27.6 , 12.6 , 12.8 , 12.4 , 13.  , 19.6 , 26.6 ,\n",
              "       21.  , 25.4 , 22.4 , 21.  , 19.8 , 25.8 , 14.  , 13.6 , 27.6 ,\n",
              "       25.8 , 23.8 , 21.2 , 15.8 , 21.2 , 21.6 , 13.2 , 19.8 , 19.6 ,\n",
              "       17.2 , 29.6 , 23.4 , 29.2 , 23.2 , 16.6 , 15.8 , 17.2 , 14.2 ,\n",
              "       14.4 , 14.4 , 14.  , 14.8 , 25.6 , 26.  , 25.2 , 31.6 , 29.6 ,\n",
              "       26.8 , 26.2 , 25.8 , 23.8 , 25.  , 27.  , 19.6 , 17.7 , 16.9 ,\n",
              "       17.6 , 14.7 , 14.4 , 14.4 , 14.4 , 16.8 , 17.  , 16.7 , 17.5 ,\n",
              "       20.8 , 16.28, 16.02, 29.  , 24.3 , 20.2 , 23.2 , 24.9 , 26.8 ,\n",
              "       25.  , 20.8 , 27.8 , 18.6 , 22.6 , 23.8 , 22.8 , 23.1 , 32.2 ,\n",
              "       25.6 , 26.8 , 25.12, 27.2 , 27.9 , 15.9 , 15.3 , 15.3 , 14.8 ,\n",
              "       20.4 , 20.22, 20.76, 20.48, 26.08, 26.08, 29.8 , 32.3 , 18.7 ,\n",
              "       16.9 , 18.5 , 19.9 , 29.4 , 31.3 , 30.5 , 28.08, 21.92, 15.  ,\n",
              "       24.52, 21.22, 18.  , 15.9 , 14.8 , 14.4 , 15.  , 32.14, 30.88,\n",
              "       31.62, 27.9 , 30.6 , 17.22, 17.92, 15.9 , 15.3 , 19.52, 19.62,\n",
              "       19.72, 19.06, 15.9 , 15.1 , 15.9 , 16.18, 29.4 , 25.7 , 32.48,\n",
              "       25.7 , 30.  , 29.98, 30.6 , 29.4 , 22.76, 24.4 , 24.12, 38.56,\n",
              "       33.44, 34.72, 32.3 , 32.64, 17.46, 18.78, 18.78, 19.1 , 20.08,\n",
              "       20.6 , 26.18, 20.  , 20.28, 20.14, 20.14, 19.16, 19.94, 18.78,\n",
              "       19.34, 17.98, 16.88, 32.12, 27.34, 27.34, 33.06, 27.34, 24.78,\n",
              "       24.04, 27.34, 24.98, 18.3 , 23.98, 18.32, 32.04, 31.42, 20.64,\n",
              "       20.14, 25.78, 19.8 , 20.74, 17.5 , 18.24, 17.48, 17.48, 16.28,\n",
              "       16.98, 18.38, 17.48, 34.4 , 36.84, 33.86, 27.8 , 30.12, 20.06,\n",
              "       32.72, 20.94, 34.28, 33.86, 36.58, 34.4 , 26.42, 26.92, 24.52,\n",
              "       28.92, 35.6 , 38.86, 34.94, 35.3 , 26.5 , 25.5 , 27.34, 19.76,\n",
              "       35.58, 30.6 , 32.3 , 31.16, 34.22, 37.7 , 28.32, 36.3 , 42.16,\n",
              "       40.4 , 30.12, 29.42, 36.52, 37.7 , 34.96, 27.26, 28.52, 36.22,\n",
              "       33.42, 28.16, 26.96, 26.78, 25.8 , 31.46, 36.64, 35.24, 35.66,\n",
              "       36.28, 36.94, 37.34, 35.28, 34.74, 35.24, 28.78, 36.42, 35.18,\n",
              "       32.48, 33.54, 32.3 , 30.12, 30.12, 27.26, 27.26, 21.42, 21.5 ,\n",
              "       24.12, 23.46, 29.6 , 29.6 , 28.8 , 28.16, 28.16, 28.6 , 27.12,\n",
              "       34.92, 35.  , 34.4 , 35.64, 34.88, 36.14, 35.14, 34.62, 35.62,\n",
              "       35.44, 35.22, 21.44, 23.46, 27.  , 22.88, 33.54, 29.96, 26.2 ,\n",
              "       27.64, 40.4 , 33.14, 29.6 , 28.2 ])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## GridSearchCV를 통한 하이퍼패러미터 튜닝"
      ],
      "metadata": {
        "id": "D3n5FchpsMFf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import GridSearchCV, KFold\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "scaler = StandardScaler() # 데이터 표준화 객체\n",
        "knn = KNeighborsRegressor() # KNN 회귀 객체\n",
        "steps = [('scaler',scaler),('knn',knn)] # 파이프라인에 순서 지정\n",
        "pipe = Pipeline(steps) # 파이프라인 만들기\n",
        "\n",
        "kfold = KFold(n_splits=5, shuffle=True) # 데이터 분할 객체 생성\n",
        "param_grid = {'knn__n_neighbors':range(1,20)} # 하이퍼패러미터 범위 지정, steps의 이름 참조\n",
        "\n",
        "metrics = 'r2' # 성능측정 지표 (한개만 됨)\n",
        "\n",
        "grid_search = GridSearchCV(estimator=pipe, # 파이프라인\n",
        "                           param_grid=param_grid, # 하이퍼패러미터 범위\n",
        "                           scoring=metrics, # 성능측정지표\n",
        "                           cv=kfold) # 데이터 분할 객체\n",
        "\n",
        "result = grid_search.fit(X,y)\n"
      ],
      "metadata": {
        "id": "xrSmJ5gvsPdt"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tSbUXWT7v_DH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(result.cv_results_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 994
        },
        "id": "fSH3Xn3TvPh9",
        "outputId": "9de5079d-3a3c-4802-96c7-64a4feee0f2d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
              "0        0.011610      0.005240         0.004967        0.000567   \n",
              "1        0.006307      0.001588         0.003333        0.000929   \n",
              "2        0.004480      0.000141         0.002642        0.000059   \n",
              "3        0.004463      0.000217         0.002789        0.000175   \n",
              "4        0.004640      0.000135         0.002738        0.000045   \n",
              "5        0.004500      0.000110         0.002731        0.000102   \n",
              "6        0.004680      0.000362         0.002946        0.000268   \n",
              "7        0.004485      0.000170         0.002759        0.000096   \n",
              "8        0.004738      0.000428         0.003096        0.000316   \n",
              "9        0.004715      0.000300         0.002775        0.000044   \n",
              "10       0.004421      0.000132         0.002792        0.000110   \n",
              "11       0.004542      0.000330         0.002900        0.000239   \n",
              "12       0.004375      0.000080         0.002918        0.000200   \n",
              "13       0.004356      0.000028         0.002786        0.000039   \n",
              "14       0.004286      0.000076         0.002805        0.000097   \n",
              "15       0.004345      0.000058         0.002792        0.000042   \n",
              "16       0.004588      0.000294         0.003035        0.000388   \n",
              "17       0.004424      0.000081         0.003067        0.000191   \n",
              "18       0.004741      0.000229         0.002876        0.000036   \n",
              "\n",
              "   param_knn__n_neighbors                    params  split0_test_score  \\\n",
              "0                       1   {'knn__n_neighbors': 1}           0.771919   \n",
              "1                       2   {'knn__n_neighbors': 2}           0.817494   \n",
              "2                       3   {'knn__n_neighbors': 3}           0.830419   \n",
              "3                       4   {'knn__n_neighbors': 4}           0.848038   \n",
              "4                       5   {'knn__n_neighbors': 5}           0.852629   \n",
              "5                       6   {'knn__n_neighbors': 6}           0.859767   \n",
              "6                       7   {'knn__n_neighbors': 7}           0.858715   \n",
              "7                       8   {'knn__n_neighbors': 8}           0.860221   \n",
              "8                       9   {'knn__n_neighbors': 9}           0.855639   \n",
              "9                      10  {'knn__n_neighbors': 10}           0.852235   \n",
              "10                     11  {'knn__n_neighbors': 11}           0.847847   \n",
              "11                     12  {'knn__n_neighbors': 12}           0.835291   \n",
              "12                     13  {'knn__n_neighbors': 13}           0.830509   \n",
              "13                     14  {'knn__n_neighbors': 14}           0.831473   \n",
              "14                     15  {'knn__n_neighbors': 15}           0.823587   \n",
              "15                     16  {'knn__n_neighbors': 16}           0.821791   \n",
              "16                     17  {'knn__n_neighbors': 17}           0.824289   \n",
              "17                     18  {'knn__n_neighbors': 18}           0.816288   \n",
              "18                     19  {'knn__n_neighbors': 19}           0.812195   \n",
              "\n",
              "    split1_test_score  split2_test_score  split3_test_score  \\\n",
              "0            0.853328           0.762672           0.899793   \n",
              "1            0.845489           0.796991           0.888181   \n",
              "2            0.855475           0.830572           0.901114   \n",
              "3            0.848908           0.838454           0.889253   \n",
              "4            0.848741           0.836822           0.897848   \n",
              "5            0.850740           0.836970           0.904199   \n",
              "6            0.842123           0.832089           0.909569   \n",
              "7            0.838478           0.823431           0.906853   \n",
              "8            0.830406           0.818723           0.903656   \n",
              "9            0.826225           0.811337           0.899196   \n",
              "10           0.815368           0.805536           0.896773   \n",
              "11           0.815697           0.791525           0.892165   \n",
              "12           0.815151           0.787797           0.891947   \n",
              "13           0.811749           0.781384           0.885063   \n",
              "14           0.804199           0.774993           0.879030   \n",
              "15           0.799488           0.770362           0.877701   \n",
              "16           0.793981           0.768638           0.876186   \n",
              "17           0.788894           0.762771           0.870657   \n",
              "18           0.780391           0.760708           0.870378   \n",
              "\n",
              "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
              "0            0.757183         0.808979        0.057297               16  \n",
              "1            0.776117         0.824854        0.039095               11  \n",
              "2            0.767202         0.836957        0.043378                8  \n",
              "3            0.806695         0.846270        0.026401                4  \n",
              "4            0.798701         0.846948        0.031801                3  \n",
              "5            0.809164         0.852168        0.031136                1  \n",
              "6            0.793180         0.847135        0.037934                2  \n",
              "7            0.800472         0.845891        0.036177                5  \n",
              "8            0.809855         0.843656        0.033714                6  \n",
              "9            0.813563         0.840511        0.032750                7  \n",
              "10           0.808920         0.834889        0.034394                9  \n",
              "11           0.804216         0.827779        0.035263               10  \n",
              "12           0.790180         0.823117        0.037916               12  \n",
              "13           0.789302         0.819794        0.037057               13  \n",
              "14           0.781783         0.812719        0.037331               14  \n",
              "15           0.778694         0.809607        0.038427               15  \n",
              "16           0.772647         0.807148        0.039766               17  \n",
              "17           0.773986         0.802519        0.038502               18  \n",
              "18           0.768579         0.798450        0.040014               19  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f41b8b5a-fa32-4453-9b67-f07639eebaf2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_knn__n_neighbors</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.011610</td>\n",
              "      <td>0.005240</td>\n",
              "      <td>0.004967</td>\n",
              "      <td>0.000567</td>\n",
              "      <td>1</td>\n",
              "      <td>{'knn__n_neighbors': 1}</td>\n",
              "      <td>0.771919</td>\n",
              "      <td>0.853328</td>\n",
              "      <td>0.762672</td>\n",
              "      <td>0.899793</td>\n",
              "      <td>0.757183</td>\n",
              "      <td>0.808979</td>\n",
              "      <td>0.057297</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.006307</td>\n",
              "      <td>0.001588</td>\n",
              "      <td>0.003333</td>\n",
              "      <td>0.000929</td>\n",
              "      <td>2</td>\n",
              "      <td>{'knn__n_neighbors': 2}</td>\n",
              "      <td>0.817494</td>\n",
              "      <td>0.845489</td>\n",
              "      <td>0.796991</td>\n",
              "      <td>0.888181</td>\n",
              "      <td>0.776117</td>\n",
              "      <td>0.824854</td>\n",
              "      <td>0.039095</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.004480</td>\n",
              "      <td>0.000141</td>\n",
              "      <td>0.002642</td>\n",
              "      <td>0.000059</td>\n",
              "      <td>3</td>\n",
              "      <td>{'knn__n_neighbors': 3}</td>\n",
              "      <td>0.830419</td>\n",
              "      <td>0.855475</td>\n",
              "      <td>0.830572</td>\n",
              "      <td>0.901114</td>\n",
              "      <td>0.767202</td>\n",
              "      <td>0.836957</td>\n",
              "      <td>0.043378</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.004463</td>\n",
              "      <td>0.000217</td>\n",
              "      <td>0.002789</td>\n",
              "      <td>0.000175</td>\n",
              "      <td>4</td>\n",
              "      <td>{'knn__n_neighbors': 4}</td>\n",
              "      <td>0.848038</td>\n",
              "      <td>0.848908</td>\n",
              "      <td>0.838454</td>\n",
              "      <td>0.889253</td>\n",
              "      <td>0.806695</td>\n",
              "      <td>0.846270</td>\n",
              "      <td>0.026401</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.004640</td>\n",
              "      <td>0.000135</td>\n",
              "      <td>0.002738</td>\n",
              "      <td>0.000045</td>\n",
              "      <td>5</td>\n",
              "      <td>{'knn__n_neighbors': 5}</td>\n",
              "      <td>0.852629</td>\n",
              "      <td>0.848741</td>\n",
              "      <td>0.836822</td>\n",
              "      <td>0.897848</td>\n",
              "      <td>0.798701</td>\n",
              "      <td>0.846948</td>\n",
              "      <td>0.031801</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.004500</td>\n",
              "      <td>0.000110</td>\n",
              "      <td>0.002731</td>\n",
              "      <td>0.000102</td>\n",
              "      <td>6</td>\n",
              "      <td>{'knn__n_neighbors': 6}</td>\n",
              "      <td>0.859767</td>\n",
              "      <td>0.850740</td>\n",
              "      <td>0.836970</td>\n",
              "      <td>0.904199</td>\n",
              "      <td>0.809164</td>\n",
              "      <td>0.852168</td>\n",
              "      <td>0.031136</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.004680</td>\n",
              "      <td>0.000362</td>\n",
              "      <td>0.002946</td>\n",
              "      <td>0.000268</td>\n",
              "      <td>7</td>\n",
              "      <td>{'knn__n_neighbors': 7}</td>\n",
              "      <td>0.858715</td>\n",
              "      <td>0.842123</td>\n",
              "      <td>0.832089</td>\n",
              "      <td>0.909569</td>\n",
              "      <td>0.793180</td>\n",
              "      <td>0.847135</td>\n",
              "      <td>0.037934</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.004485</td>\n",
              "      <td>0.000170</td>\n",
              "      <td>0.002759</td>\n",
              "      <td>0.000096</td>\n",
              "      <td>8</td>\n",
              "      <td>{'knn__n_neighbors': 8}</td>\n",
              "      <td>0.860221</td>\n",
              "      <td>0.838478</td>\n",
              "      <td>0.823431</td>\n",
              "      <td>0.906853</td>\n",
              "      <td>0.800472</td>\n",
              "      <td>0.845891</td>\n",
              "      <td>0.036177</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.004738</td>\n",
              "      <td>0.000428</td>\n",
              "      <td>0.003096</td>\n",
              "      <td>0.000316</td>\n",
              "      <td>9</td>\n",
              "      <td>{'knn__n_neighbors': 9}</td>\n",
              "      <td>0.855639</td>\n",
              "      <td>0.830406</td>\n",
              "      <td>0.818723</td>\n",
              "      <td>0.903656</td>\n",
              "      <td>0.809855</td>\n",
              "      <td>0.843656</td>\n",
              "      <td>0.033714</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.004715</td>\n",
              "      <td>0.000300</td>\n",
              "      <td>0.002775</td>\n",
              "      <td>0.000044</td>\n",
              "      <td>10</td>\n",
              "      <td>{'knn__n_neighbors': 10}</td>\n",
              "      <td>0.852235</td>\n",
              "      <td>0.826225</td>\n",
              "      <td>0.811337</td>\n",
              "      <td>0.899196</td>\n",
              "      <td>0.813563</td>\n",
              "      <td>0.840511</td>\n",
              "      <td>0.032750</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.004421</td>\n",
              "      <td>0.000132</td>\n",
              "      <td>0.002792</td>\n",
              "      <td>0.000110</td>\n",
              "      <td>11</td>\n",
              "      <td>{'knn__n_neighbors': 11}</td>\n",
              "      <td>0.847847</td>\n",
              "      <td>0.815368</td>\n",
              "      <td>0.805536</td>\n",
              "      <td>0.896773</td>\n",
              "      <td>0.808920</td>\n",
              "      <td>0.834889</td>\n",
              "      <td>0.034394</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.004542</td>\n",
              "      <td>0.000330</td>\n",
              "      <td>0.002900</td>\n",
              "      <td>0.000239</td>\n",
              "      <td>12</td>\n",
              "      <td>{'knn__n_neighbors': 12}</td>\n",
              "      <td>0.835291</td>\n",
              "      <td>0.815697</td>\n",
              "      <td>0.791525</td>\n",
              "      <td>0.892165</td>\n",
              "      <td>0.804216</td>\n",
              "      <td>0.827779</td>\n",
              "      <td>0.035263</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.004375</td>\n",
              "      <td>0.000080</td>\n",
              "      <td>0.002918</td>\n",
              "      <td>0.000200</td>\n",
              "      <td>13</td>\n",
              "      <td>{'knn__n_neighbors': 13}</td>\n",
              "      <td>0.830509</td>\n",
              "      <td>0.815151</td>\n",
              "      <td>0.787797</td>\n",
              "      <td>0.891947</td>\n",
              "      <td>0.790180</td>\n",
              "      <td>0.823117</td>\n",
              "      <td>0.037916</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.004356</td>\n",
              "      <td>0.000028</td>\n",
              "      <td>0.002786</td>\n",
              "      <td>0.000039</td>\n",
              "      <td>14</td>\n",
              "      <td>{'knn__n_neighbors': 14}</td>\n",
              "      <td>0.831473</td>\n",
              "      <td>0.811749</td>\n",
              "      <td>0.781384</td>\n",
              "      <td>0.885063</td>\n",
              "      <td>0.789302</td>\n",
              "      <td>0.819794</td>\n",
              "      <td>0.037057</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.004286</td>\n",
              "      <td>0.000076</td>\n",
              "      <td>0.002805</td>\n",
              "      <td>0.000097</td>\n",
              "      <td>15</td>\n",
              "      <td>{'knn__n_neighbors': 15}</td>\n",
              "      <td>0.823587</td>\n",
              "      <td>0.804199</td>\n",
              "      <td>0.774993</td>\n",
              "      <td>0.879030</td>\n",
              "      <td>0.781783</td>\n",
              "      <td>0.812719</td>\n",
              "      <td>0.037331</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.004345</td>\n",
              "      <td>0.000058</td>\n",
              "      <td>0.002792</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>16</td>\n",
              "      <td>{'knn__n_neighbors': 16}</td>\n",
              "      <td>0.821791</td>\n",
              "      <td>0.799488</td>\n",
              "      <td>0.770362</td>\n",
              "      <td>0.877701</td>\n",
              "      <td>0.778694</td>\n",
              "      <td>0.809607</td>\n",
              "      <td>0.038427</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.004588</td>\n",
              "      <td>0.000294</td>\n",
              "      <td>0.003035</td>\n",
              "      <td>0.000388</td>\n",
              "      <td>17</td>\n",
              "      <td>{'knn__n_neighbors': 17}</td>\n",
              "      <td>0.824289</td>\n",
              "      <td>0.793981</td>\n",
              "      <td>0.768638</td>\n",
              "      <td>0.876186</td>\n",
              "      <td>0.772647</td>\n",
              "      <td>0.807148</td>\n",
              "      <td>0.039766</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.004424</td>\n",
              "      <td>0.000081</td>\n",
              "      <td>0.003067</td>\n",
              "      <td>0.000191</td>\n",
              "      <td>18</td>\n",
              "      <td>{'knn__n_neighbors': 18}</td>\n",
              "      <td>0.816288</td>\n",
              "      <td>0.788894</td>\n",
              "      <td>0.762771</td>\n",
              "      <td>0.870657</td>\n",
              "      <td>0.773986</td>\n",
              "      <td>0.802519</td>\n",
              "      <td>0.038502</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.004741</td>\n",
              "      <td>0.000229</td>\n",
              "      <td>0.002876</td>\n",
              "      <td>0.000036</td>\n",
              "      <td>19</td>\n",
              "      <td>{'knn__n_neighbors': 19}</td>\n",
              "      <td>0.812195</td>\n",
              "      <td>0.780391</td>\n",
              "      <td>0.760708</td>\n",
              "      <td>0.870378</td>\n",
              "      <td>0.768579</td>\n",
              "      <td>0.798450</td>\n",
              "      <td>0.040014</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f41b8b5a-fa32-4453-9b67-f07639eebaf2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f41b8b5a-fa32-4453-9b67-f07639eebaf2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f41b8b5a-fa32-4453-9b67-f07639eebaf2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a1782cb7-535a-42e0-8337-4ad466f7a700\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a1782cb7-535a-42e0-8337-4ad466f7a700')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a1782cb7-535a-42e0-8337-4ad466f7a700 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 19,\n  \"fields\": [\n    {\n      \"column\": \"mean_fit_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0016617170001214487,\n        \"min\": 0.0042855262756347655,\n        \"max\": 0.011609554290771484,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.011609554290771484,\n          0.004499578475952148,\n          0.004541730880737305\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_fit_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0011910816765104706,\n        \"min\": 2.7932402776397954e-05,\n        \"max\": 0.005239965362645707,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.005239965362645707,\n          0.00010953195516824028,\n          0.0003298151537000044\n        ],\n        \"semantic_type\": \"\",\n        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  1,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result.best_estimator_, result.best_score_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fHVHOOGjvqCH",
        "outputId": "2e5895d4-d6ad-488f-bdb8-448ccde7574d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Pipeline(steps=[('scaler', StandardScaler()),\n",
              "                 ('knn', KNeighborsRegressor(n_neighbors=6))]),\n",
              " 0.8521679677778344)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 차수변경 + 릿지회귀 하이퍼패러미터 튜닝"
      ],
      "metadata": {
        "id": "AVn6x5eZwAMV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import GridSearchCV, KFold\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.linear_model import Ridge\n",
        "\n",
        "poly = PolynomialFeatures() # 데이터 다차항 추가하는 객체\n",
        "reg = Ridge() # 릿지회귀 객체\n",
        "steps = [('poly',poly),('reg',reg)] # 파이프라인에 순서 지정\n",
        "pipe = Pipeline(steps) # 파이프라인 만들기\n",
        "\n",
        "kfold = KFold(n_splits=5, shuffle=True) # 데이터 분할 객체 생성\n",
        "param_grid = {'poly__degree':range(1,4), # 1차~3차\n",
        "              'reg__alpha':[0.001, 0.01, 0.05, 0.1, 0.5, 1, 10, 100] # 람다의 범위\n",
        "              } # 하이퍼패러미터 범위 지정, steps의 이름 참조\n",
        "\n",
        "metrics = 'r2' # 성능측정 지표 (한개만 됨)\n",
        "\n",
        "grid_search = GridSearchCV(estimator=pipe, # 파이프라인\n",
        "                           param_grid=param_grid, # 하이퍼패러미터 범위\n",
        "                           scoring=metrics, # 성능측정지표\n",
        "                           cv=kfold) # 데이터 분할 객체\n",
        "\n",
        "result = grid_search.fit(X,y)"
      ],
      "metadata": {
        "id": "5uiBYhcFwDRl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result.best_estimator_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "K7hhycJixPib",
        "outputId": "d55418aa-cbe2-4be7-8895-4feb4eb2f55e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('poly', PolynomialFeatures()), ('reg', Ridge(alpha=0.1))])"
            ],
            "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. 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            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(result.cv_results_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pwXIiajbw7cM",
        "outputId": "03d4f9c0-ad67-4a8f-c35d-632919737198"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
              "0        0.009323      0.009265         0.002419        0.000992   \n",
              "1        0.004212      0.000169         0.001814        0.000060   \n",
              "2        0.004202      0.000280         0.001767        0.000027   \n",
              "3        0.004388      0.000427         0.001772        0.000042   \n",
              "4        0.004074      0.000118         0.001826        0.000090   \n",
              "5        0.003953      0.000091         0.001738        0.000023   \n",
              "6        0.004364      0.000529         0.001799        0.000040   \n",
              "7        0.004192      0.000271         0.001878        0.000146   \n",
              "8        0.008177      0.001322         0.002970        0.000223   \n",
              "9        0.007782      0.001136         0.002785        0.000037   \n",
              "10       0.007021      0.000228         0.002767        0.000089   \n",
              "11       0.008764      0.003183         0.002882        0.000230   \n",
              "12       0.007242      0.000349         0.002949        0.000172   \n",
              "13       0.006910      0.000091         0.002831        0.000213   \n",
              "14       0.006396      0.000107         0.002720        0.000043   \n",
              "15       0.007743      0.002432         0.002816        0.000114   \n",
              "16       0.050911      0.005570         0.006644        0.000100   \n",
              "17       0.036807      0.008510         0.004852        0.001400   \n",
              "18       0.028797      0.011450         0.005023        0.001809   \n",
              "19       0.030618      0.013333         0.006298        0.001476   \n",
              "20       0.025053      0.006203         0.005391        0.001420   \n",
              "21       0.036226      0.019025         0.006686        0.000385   \n",
              "22       0.023154      0.001082         0.009903        0.004925   \n",
              "23       0.023028      0.002710         0.006746        0.000368   \n",
              "\n",
              "   param_poly__degree param_reg__alpha  \\\n",
              "0                   1            0.001   \n",
              "1                   1             0.01   \n",
              "2                   1             0.05   \n",
              "3                   1              0.1   \n",
              "4                   1              0.5   \n",
              "5                   1                1   \n",
              "6                   1               10   \n",
              "7                   1              100   \n",
              "8                   2            0.001   \n",
              "9                   2             0.01   \n",
              "10                  2             0.05   \n",
              "11                  2              0.1   \n",
              "12                  2              0.5   \n",
              "13                  2                1   \n",
              "14                  2               10   \n",
              "15                  2              100   \n",
              "16                  3            0.001   \n",
              "17                  3             0.01   \n",
              "18                  3             0.05   \n",
              "19                  3              0.1   \n",
              "20                  3              0.5   \n",
              "21                  3                1   \n",
              "22                  3               10   \n",
              "23                  3              100   \n",
              "\n",
              "                                      params  split0_test_score  \\\n",
              "0   {'poly__degree': 1, 'reg__alpha': 0.001}           0.832337   \n",
              "1    {'poly__degree': 1, 'reg__alpha': 0.01}           0.832335   \n",
              "2    {'poly__degree': 1, 'reg__alpha': 0.05}           0.832325   \n",
              "3     {'poly__degree': 1, 'reg__alpha': 0.1}           0.832313   \n",
              "4     {'poly__degree': 1, 'reg__alpha': 0.5}           0.832218   \n",
              "5       {'poly__degree': 1, 'reg__alpha': 1}           0.832098   \n",
              "6      {'poly__degree': 1, 'reg__alpha': 10}           0.829950   \n",
              "7     {'poly__degree': 1, 'reg__alpha': 100}           0.817232   \n",
              "8   {'poly__degree': 2, 'reg__alpha': 0.001}           0.872159   \n",
              "9    {'poly__degree': 2, 'reg__alpha': 0.01}           0.872941   \n",
              "10   {'poly__degree': 2, 'reg__alpha': 0.05}           0.874603   \n",
              "11    {'poly__degree': 2, 'reg__alpha': 0.1}           0.875184   \n",
              "12    {'poly__degree': 2, 'reg__alpha': 0.5}           0.875081   \n",
              "13      {'poly__degree': 2, 'reg__alpha': 1}           0.874884   \n",
              "14     {'poly__degree': 2, 'reg__alpha': 10}           0.874926   \n",
              "15    {'poly__degree': 2, 'reg__alpha': 100}           0.876816   \n",
              "16  {'poly__degree': 3, 'reg__alpha': 0.001}          -0.473051   \n",
              "17   {'poly__degree': 3, 'reg__alpha': 0.01}           0.178847   \n",
              "18   {'poly__degree': 3, 'reg__alpha': 0.05}           0.462766   \n",
              "19    {'poly__degree': 3, 'reg__alpha': 0.1}           0.510714   \n",
              "20    {'poly__degree': 3, 'reg__alpha': 0.5}           0.549593   \n",
              "21      {'poly__degree': 3, 'reg__alpha': 1}           0.564618   \n",
              "22     {'poly__degree': 3, 'reg__alpha': 10}           0.638333   \n",
              "23    {'poly__degree': 3, 'reg__alpha': 100}           0.747537   \n",
              "\n",
              "    split1_test_score  split2_test_score  split3_test_score  \\\n",
              "0            0.800452           0.780929           0.829468   \n",
              "1            0.800457           0.780929           0.829467   \n",
              "2            0.800481           0.780929           0.829463   \n",
              "3            0.800511           0.780928           0.829458   \n",
              "4            0.800747           0.780926           0.829415   \n",
              "5            0.801034           0.780920           0.829360   \n",
              "6            0.805046           0.780520           0.828127   \n",
              "7            0.813697           0.773919           0.817431   \n",
              "8            0.879251           0.785327           0.882630   \n",
              "9            0.879999           0.788539           0.882593   \n",
              "10           0.881969           0.796179           0.881463   \n",
              "11           0.883062           0.799790           0.879756   \n",
              "12           0.884786           0.801470           0.872883   \n",
              "13           0.885401           0.798771           0.869627   \n",
              "14           0.887425           0.788218           0.861433   \n",
              "15           0.886869           0.785288           0.856637   \n",
              "16          -0.201261          -7.389735           0.724877   \n",
              "17           0.312188          -5.216299           0.730733   \n",
              "18           0.420549          -3.946888           0.746490   \n",
              "19           0.441136          -3.582786           0.758618   \n",
              "20           0.549380          -3.713322           0.787650   \n",
              "21           0.613288          -4.125992           0.796129   \n",
              "22           0.727920          -4.800725           0.805119   \n",
              "23           0.760620          -3.369074           0.804467   \n",
              "\n",
              "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
              "0            0.828755         0.814388        0.020346               15  \n",
              "1            0.828756         0.814389        0.020345               14  \n",
              "2            0.828763         0.814392        0.020340               13  \n",
              "3            0.828771         0.814396        0.020334               12  \n",
              "4            0.828834         0.814428        0.020289               11  \n",
              "5            0.828910         0.814465        0.020234               10  \n",
              "6            0.829785         0.814685        0.019503                9  \n",
              "7            0.828404         0.810137        0.018770               16  \n",
              "8            0.882555         0.860385        0.037722                6  \n",
              "9            0.883458         0.861506        0.036670                5  \n",
              "10           0.885418         0.863927        0.034055                3  \n",
              "11           0.886154         0.864789        0.032702                1  \n",
              "12           0.886052         0.864054        0.031717                2  \n",
              "13           0.885536         0.862844        0.032619                4  \n",
              "14           0.883982         0.859197        0.036610                7  \n",
              "15           0.882820         0.857686        0.037661                8  \n",
              "16          -0.585568        -1.584948        2.938725               24  \n",
              "17           0.691793        -0.660548        2.287800               23  \n",
              "18           0.817032        -0.300010        1.829956               21  \n",
              "19           0.819934        -0.210477        1.692222               19  \n",
              "20           0.807022        -0.203935        1.758201               18  \n",
              "21           0.803144        -0.269763        1.930478               20  \n",
              "22           0.814018        -0.363067        2.219732               22  \n",
              "23           0.832911        -0.044708        1.662464               17  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-154d554b-9e84-4b80-9b0c-d847a1c02783\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_poly__degree</th>\n",
              "      <th>param_reg__alpha</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.009323</td>\n",
              "      <td>0.009265</td>\n",
              "      <td>0.002419</td>\n",
              "      <td>0.000992</td>\n",
              "      <td>1</td>\n",
              "      <td>0.001</td>\n",
              "      <td>{'poly__degree': 1, 'reg__alpha': 0.001}</td>\n",
              "      <td>0.832337</td>\n",
              "      <td>0.800452</td>\n",
              "      <td>0.780929</td>\n",
              "      <td>0.829468</td>\n",
              "      <td>0.828755</td>\n",
              "      <td>0.814388</td>\n",
              "      <td>0.020346</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.004212</td>\n",
              "      <td>0.000169</td>\n",
              "      <td>0.001814</td>\n",
              "      <td>0.000060</td>\n",
              "      <td>1</td>\n",
              "      <td>0.01</td>\n",
              "      <td>{'poly__degree': 1, 'reg__alpha': 0.01}</td>\n",
              "      <td>0.832335</td>\n",
              "      <td>0.800457</td>\n",
              "      <td>0.780929</td>\n",
              "      <td>0.829467</td>\n",
              "      <td>0.828756</td>\n",
              "      <td>0.814389</td>\n",
              "      <td>0.020345</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.004202</td>\n",
              "      <td>0.000280</td>\n",
              "      <td>0.001767</td>\n",
              "      <td>0.000027</td>\n",
              "      <td>1</td>\n",
              "      <td>0.05</td>\n",
              "      <td>{'poly__degree': 1, 'reg__alpha': 0.05}</td>\n",
              "      <td>0.832325</td>\n",
              "      <td>0.800481</td>\n",
              "      <td>0.780929</td>\n",
              "      <td>0.829463</td>\n",
              "      <td>0.828763</td>\n",
              "      <td>0.814392</td>\n",
              "      <td>0.020340</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.004388</td>\n",
              "      <td>0.000427</td>\n",
              "      <td>0.001772</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>{'poly__degree': 1, 'reg__alpha': 0.1}</td>\n",
              "      <td>0.832313</td>\n",
              "      <td>0.800511</td>\n",
              "      <td>0.780928</td>\n",
              "      <td>0.829458</td>\n",
              "      <td>0.828771</td>\n",
              "      <td>0.814396</td>\n",
              "      <td>0.020334</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.004074</td>\n",
              "      <td>0.000118</td>\n",
              "      <td>0.001826</td>\n",
              "      <td>0.000090</td>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>{'poly__degree': 1, 'reg__alpha': 0.5}</td>\n",
              "      <td>0.832218</td>\n",
              "      <td>0.800747</td>\n",
              "      <td>0.780926</td>\n",
              "      <td>0.829415</td>\n",
              "      <td>0.828834</td>\n",
              "      <td>0.814428</td>\n",
              "      <td>0.020289</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.003953</td>\n",
              "      <td>0.000091</td>\n",
              "      <td>0.001738</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>{'poly__degree': 1, 'reg__alpha': 1}</td>\n",
              "      <td>0.832098</td>\n",
              "      <td>0.801034</td>\n",
              "      <td>0.780920</td>\n",
              "      <td>0.829360</td>\n",
              "      <td>0.828910</td>\n",
              "      <td>0.814465</td>\n",
              "      <td>0.020234</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.004364</td>\n",
              "      <td>0.000529</td>\n",
              "      <td>0.001799</td>\n",
              "      <td>0.000040</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>{'poly__degree': 1, 'reg__alpha': 10}</td>\n",
              "      <td>0.829950</td>\n",
              "      <td>0.805046</td>\n",
              "      <td>0.780520</td>\n",
              "      <td>0.828127</td>\n",
              "      <td>0.829785</td>\n",
              "      <td>0.814685</td>\n",
              "      <td>0.019503</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.004192</td>\n",
              "      <td>0.000271</td>\n",
              "      <td>0.001878</td>\n",
              "      <td>0.000146</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>{'poly__degree': 1, 'reg__alpha': 100}</td>\n",
              "      <td>0.817232</td>\n",
              "      <td>0.813697</td>\n",
              "      <td>0.773919</td>\n",
              "      <td>0.817431</td>\n",
              "      <td>0.828404</td>\n",
              "      <td>0.810137</td>\n",
              "      <td>0.018770</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.008177</td>\n",
              "      <td>0.001322</td>\n",
              "      <td>0.002970</td>\n",
              "      <td>0.000223</td>\n",
              "      <td>2</td>\n",
              "      <td>0.001</td>\n",
              "      <td>{'poly__degree': 2, 'reg__alpha': 0.001}</td>\n",
              "      <td>0.872159</td>\n",
              "      <td>0.879251</td>\n",
              "      <td>0.785327</td>\n",
              "      <td>0.882630</td>\n",
              "      <td>0.882555</td>\n",
              "      <td>0.860385</td>\n",
              "      <td>0.037722</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.007782</td>\n",
              "      <td>0.001136</td>\n",
              "      <td>0.002785</td>\n",
              "      <td>0.000037</td>\n",
              "      <td>2</td>\n",
              "      <td>0.01</td>\n",
              "      <td>{'poly__degree': 2, 'reg__alpha': 0.01}</td>\n",
              "      <td>0.872941</td>\n",
              "      <td>0.879999</td>\n",
              "      <td>0.788539</td>\n",
              "      <td>0.882593</td>\n",
              "      <td>0.883458</td>\n",
              "      <td>0.861506</td>\n",
              "      <td>0.036670</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.007021</td>\n",
              "      <td>0.000228</td>\n",
              "      <td>0.002767</td>\n",
              "      <td>0.000089</td>\n",
              "      <td>2</td>\n",
              "      <td>0.05</td>\n",
              "      <td>{'poly__degree': 2, 'reg__alpha': 0.05}</td>\n",
              "      <td>0.874603</td>\n",
              "      <td>0.881969</td>\n",
              "      <td>0.796179</td>\n",
              "      <td>0.881463</td>\n",
              "      <td>0.885418</td>\n",
              "      <td>0.863927</td>\n",
              "      <td>0.034055</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.008764</td>\n",
              "      <td>0.003183</td>\n",
              "      <td>0.002882</td>\n",
              "      <td>0.000230</td>\n",
              "      <td>2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>{'poly__degree': 2, 'reg__alpha': 0.1}</td>\n",
              "      <td>0.875184</td>\n",
              "      <td>0.883062</td>\n",
              "      <td>0.799790</td>\n",
              "      <td>0.879756</td>\n",
              "      <td>0.886154</td>\n",
              "      <td>0.864789</td>\n",
              "      <td>0.032702</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.007242</td>\n",
              "      <td>0.000349</td>\n",
              "      <td>0.002949</td>\n",
              "      <td>0.000172</td>\n",
              "      <td>2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>{'poly__degree': 2, 'reg__alpha': 0.5}</td>\n",
              "      <td>0.875081</td>\n",
              "      <td>0.884786</td>\n",
              "      <td>0.801470</td>\n",
              "      <td>0.872883</td>\n",
              "      <td>0.886052</td>\n",
              "      <td>0.864054</td>\n",
              "      <td>0.031717</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.006910</td>\n",
              "      <td>0.000091</td>\n",
              "      <td>0.002831</td>\n",
              "      <td>0.000213</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>{'poly__degree': 2, 'reg__alpha': 1}</td>\n",
              "      <td>0.874884</td>\n",
              "      <td>0.885401</td>\n",
              "      <td>0.798771</td>\n",
              "      <td>0.869627</td>\n",
              "      <td>0.885536</td>\n",
              "      <td>0.862844</td>\n",
              "      <td>0.032619</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.006396</td>\n",
              "      <td>0.000107</td>\n",
              "      <td>0.002720</td>\n",
              "      <td>0.000043</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>{'poly__degree': 2, 'reg__alpha': 10}</td>\n",
              "      <td>0.874926</td>\n",
              "      <td>0.887425</td>\n",
              "      <td>0.788218</td>\n",
              "      <td>0.861433</td>\n",
              "      <td>0.883982</td>\n",
              "      <td>0.859197</td>\n",
              "      <td>0.036610</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.007743</td>\n",
              "      <td>0.002432</td>\n",
              "      <td>0.002816</td>\n",
              "      <td>0.000114</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>{'poly__degree': 2, 'reg__alpha': 100}</td>\n",
              "      <td>0.876816</td>\n",
              "      <td>0.886869</td>\n",
              "      <td>0.785288</td>\n",
              "      <td>0.856637</td>\n",
              "      <td>0.882820</td>\n",
              "      <td>0.857686</td>\n",
              "      <td>0.037661</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.050911</td>\n",
              "      <td>0.005570</td>\n",
              "      <td>0.006644</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>3</td>\n",
              "      <td>0.001</td>\n",
              "      <td>{'poly__degree': 3, 'reg__alpha': 0.001}</td>\n",
              "      <td>-0.473051</td>\n",
              "      <td>-0.201261</td>\n",
              "      <td>-7.389735</td>\n",
              "      <td>0.724877</td>\n",
              "      <td>-0.585568</td>\n",
              "      <td>-1.584948</td>\n",
              "      <td>2.938725</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.036807</td>\n",
              "      <td>0.008510</td>\n",
              "      <td>0.004852</td>\n",
              "      <td>0.001400</td>\n",
              "      <td>3</td>\n",
              "      <td>0.01</td>\n",
              "      <td>{'poly__degree': 3, 'reg__alpha': 0.01}</td>\n",
              "      <td>0.178847</td>\n",
              "      <td>0.312188</td>\n",
              "      <td>-5.216299</td>\n",
              "      <td>0.730733</td>\n",
              "      <td>0.691793</td>\n",
              "      <td>-0.660548</td>\n",
              "      <td>2.287800</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.028797</td>\n",
              "      <td>0.011450</td>\n",
              "      <td>0.005023</td>\n",
              "      <td>0.001809</td>\n",
              "      <td>3</td>\n",
              "      <td>0.05</td>\n",
              "      <td>{'poly__degree': 3, 'reg__alpha': 0.05}</td>\n",
              "      <td>0.462766</td>\n",
              "      <td>0.420549</td>\n",
              "      <td>-3.946888</td>\n",
              "      <td>0.746490</td>\n",
              "      <td>0.817032</td>\n",
              "      <td>-0.300010</td>\n",
              "      <td>1.829956</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.030618</td>\n",
              "      <td>0.013333</td>\n",
              "      <td>0.006298</td>\n",
              "      <td>0.001476</td>\n",
              "      <td>3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>{'poly__degree': 3, 'reg__alpha': 0.1}</td>\n",
              "      <td>0.510714</td>\n",
              "      <td>0.441136</td>\n",
              "      <td>-3.582786</td>\n",
              "      <td>0.758618</td>\n",
              "      <td>0.819934</td>\n",
              "      <td>-0.210477</td>\n",
              "      <td>1.692222</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.025053</td>\n",
              "      <td>0.006203</td>\n",
              "      <td>0.005391</td>\n",
              "      <td>0.001420</td>\n",
              "      <td>3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>{'poly__degree': 3, 'reg__alpha': 0.5}</td>\n",
              "      <td>0.549593</td>\n",
              "      <td>0.549380</td>\n",
              "      <td>-3.713322</td>\n",
              "      <td>0.787650</td>\n",
              "      <td>0.807022</td>\n",
              "      <td>-0.203935</td>\n",
              "      <td>1.758201</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.036226</td>\n",
              "      <td>0.019025</td>\n",
              "      <td>0.006686</td>\n",
              "      <td>0.000385</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>{'poly__degree': 3, 'reg__alpha': 1}</td>\n",
              "      <td>0.564618</td>\n",
              "      <td>0.613288</td>\n",
              "      <td>-4.125992</td>\n",
              "      <td>0.796129</td>\n",
              "      <td>0.803144</td>\n",
              "      <td>-0.269763</td>\n",
              "      <td>1.930478</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.023154</td>\n",
              "      <td>0.001082</td>\n",
              "      <td>0.009903</td>\n",
              "      <td>0.004925</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>{'poly__degree': 3, 'reg__alpha': 10}</td>\n",
              "      <td>0.638333</td>\n",
              "      <td>0.727920</td>\n",
              "      <td>-4.800725</td>\n",
              "      <td>0.805119</td>\n",
              "      <td>0.814018</td>\n",
              "      <td>-0.363067</td>\n",
              "      <td>2.219732</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.023028</td>\n",
              "      <td>0.002710</td>\n",
              "      <td>0.006746</td>\n",
              "      <td>0.000368</td>\n",
              "      <td>3</td>\n",
              "      <td>100</td>\n",
              "      <td>{'poly__degree': 3, 'reg__alpha': 100}</td>\n",
              "      <td>0.747537</td>\n",
              "      <td>0.760620</td>\n",
              "      <td>-3.369074</td>\n",
              "      <td>0.804467</td>\n",
              "      <td>0.832911</td>\n",
              "      <td>-0.044708</td>\n",
              "      <td>1.662464</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-154d554b-9e84-4b80-9b0c-d847a1c02783')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-154d554b-9e84-4b80-9b0c-d847a1c02783 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-154d554b-9e84-4b80-9b0c-d847a1c02783');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c223da6f-ce9b-4a7e-9379-0b754926468d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c223da6f-ce9b-4a7e-9379-0b754926468d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c223da6f-ce9b-4a7e-9379-0b754926468d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    }
  ]
}