sklearn_quiz.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wqlw5ktcykfi"
      },
      "source": [
        "# sklearnの確認問題"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "YBUtM5k_y5Md"
      },
      "source": [
        "## 必要モジュールのインポート\n",
        "\n",
        "この問題で使うモジュールをインポートします。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "UCgaMzcYFTtW"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dp2rHHDHzZd0"
      },
      "source": [
        "## 前準備\n",
        "\n",
        "\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "2j_gL_RC1_8_"
      },
      "source": [
        "### データの読み取り\n",
        "scikit-learnから提供される`iris`というデータセットを読み込みます。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "YKJY4Q9R0aB7"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris = load_iris()\n",
        "\n",
        "#説明変数の読み取り\n",
        "data = iris.data\n",
        "feature_names = iris.feature_names\n",
        "df_data = pd.DataFrame(data=data, columns=feature_names)\n",
        "\n",
        "#目的変数の読み取り\n",
        "target = iris.target\n",
        "target_names = iris.target_names\n",
        "df_target = pd.DataFrame(data=target, columns=['target'])"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "hfBraFA414qg"
      },
      "source": [
        "### 問1. 説明変数の把握"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "1.1. `df_data` の先頭五行を表示してください。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "T7StoNXU2iXm",
        "outputId": "0fedad15-b679-44d2-b9c7-d3bf12198c40"
      },
      "outputs": [
        {
          "data": {
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              "<div>\n",
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              "    }\n",
              "\n",
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              "    }\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>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "0                5.1               3.5                1.4               0.2\n",
              "1                4.9               3.0                1.4               0.2\n",
              "2                4.7               3.2                1.3               0.2\n",
              "3                4.6               3.1                1.5               0.2\n",
              "4                5.0               3.6                1.4               0.2"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_data.head(5)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "1.2. `df_data` の行数と列数を表示しましょう。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zdx6hnA026SC",
        "outputId": "bfd5bff8-4a4d-4a8d-f8a4-a5dae9e2e82f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(150, 4)"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_data.shape"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "lJ-3Jg-k6QSx"
      },
      "source": [
        "1.3. `df_data` の各列の `mean`, `count`, `std` などの基本特徴量を表示しましょう。\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "mDKbvknP6arO",
        "outputId": "9b0dc5de-a88d-49e4-b2ba-b325ef698519"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.057333</td>\n",
              "      <td>3.758000</td>\n",
              "      <td>1.199333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.435866</td>\n",
              "      <td>1.765298</td>\n",
              "      <td>0.762238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
              "count         150.000000        150.000000         150.000000   \n",
              "mean            5.843333          3.057333           3.758000   \n",
              "std             0.828066          0.435866           1.765298   \n",
              "min             4.300000          2.000000           1.000000   \n",
              "25%             5.100000          2.800000           1.600000   \n",
              "50%             5.800000          3.000000           4.350000   \n",
              "75%             6.400000          3.300000           5.100000   \n",
              "max             7.900000          4.400000           6.900000   \n",
              "\n",
              "       petal width (cm)  \n",
              "count        150.000000  \n",
              "mean           1.199333  \n",
              "std            0.762238  \n",
              "min            0.100000  \n",
              "25%            0.300000  \n",
              "50%            1.300000  \n",
              "75%            1.800000  \n",
              "max            2.500000  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_data.describe()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "1.4. `df_data` に欠損値がないかを確認しましょう。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DqRq3xhB61Sx",
        "outputId": "21e8de9b-478b-4a88-c17e-b2d335c98fc6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "sepal length (cm)    0\n",
              "sepal width (cm)     0\n",
              "petal length (cm)    0\n",
              "petal width (cm)     0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_data.isnull().sum()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "R3ZD__9G7FeJ"
      },
      "source": [
        "### 問2. 目的変数の把握\n",
        "\n",
        "2.1. `df_target` のうち、ランダムに 10 行表示しましょう。\n",
        "\n",
        "目的変数の数字はそれぞれ、\n",
        "\n",
        "- 0 が `setosa`（ヒオウギアヤメ）\n",
        "- 1 が `versicolor`（ブルーフラッグ）\n",
        "- 2 が `virginica`（バージニカ）\n",
        "\n",
        "を指しています。\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_CDs0X1Y8YX8",
        "outputId": "d359aa9d-1044-4eb9-b31c-85f9b79ab99b"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     target\n",
              "76        1\n",
              "12        0\n",
              "57        1\n",
              "145       2\n",
              "28        0\n",
              "130       2\n",
              "124       2\n",
              "24        0\n",
              "122       2\n",
              "72        1"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_target.sample(n = 10)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "XPik8k4I2_Zz"
      },
      "source": [
        "### 問3. データの可視化（Extra）\n",
        "\n",
        "3.1. x 軸を petal length (cm)、y 軸を petal width (cm)にして散布図を作成しましょう。 ただし、目的変数に応じて点の色と形を変更してください。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "TnUb1cRZ5ajf",
        "outputId": "ba5a4fb4-15c2-4dc3-a5d4-a3907bff2a51"
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      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "extra = pd.merge(df_data, df_target, left_index=True, right_index=True)\n",
        "\n",
        "#図を描く工程\n",
        "setosa = extra[extra[\"target\"] == 0 ]\n",
        "versicolor = extra[extra[\"target\"] == 1]\n",
        "virginica  = extra[extra[\"target\"] == 2]\n",
        "\n",
        "plt.scatter(setosa[\"petal length (cm)\"], setosa[\"petal width (cm)\"], label = \"setosa\", color = \"red\")\n",
        "plt.scatter(versicolor[\"petal length (cm)\"], versicolor[\"petal width (cm)\"], label = \"versicolor\", color = \"green\", marker = \"+\")\n",
        "plt.scatter(virginica[\"petal length (cm)\"], virginica[\"petal width (cm)\"], label = \"virginica\", color = \"blue\", marker = \"x\")\n",
        "\n",
        "plt.title(\"Iris Plats Database\")\n",
        "plt.xlabel(\"petal length(cm)\")\n",
        "plt.ylabel(\"petal width(cm)\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "sjDOl12S86VJ"
      },
      "source": [
        "## 機械学習\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "OZYFySxQ9BrG"
      },
      "source": [
        "### 問4. データの分割\n",
        "\n",
        "4.1. iris データを 4:1 に分割して、80% を学習データとして 20% をテストデータとしてください。\n",
        "\n",
        "ただし、分割した後の変数名は以下の通りにしてください。\n",
        "\n",
        "| 変数名  |      内容      |\n",
        "| :-----: | :------------: |\n",
        "| x_train | 学習用の説明変数 |\n",
        "| x_test | テスト用の説明変数 |\n",
        "| y_train | 学習用の目的変数 |\n",
        "| y_test | テスト用の目的変数 |\n",
        "\n",
        "学習データでモデルの学習を行い、テストデータでモデルの評価を行います。\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "KZfVWvll9T-X"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(df_data, df_target, test_size=0.2, random_state=0)\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "Ce6m5w8K-Zui"
      },
      "source": [
        "4.2. 本当に4:1に分割できているか確認しましょう。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X4dBgVxV-dz9",
        "outputId": "c74c6df3-1d92-4ef8-b7ca-53e779e61ad5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x_train : (120, 4)\n",
            "x_test : (30, 4)\n",
            "y_train : (120, 1)\n",
            "y_test : (30, 1)\n"
          ]
        }
      ],
      "source": [
        "print(\"x_train : \" + str(x_train.shape))\n",
        "print(\"x_test : \" + str(x_test.shape))\n",
        "print(\"y_train : \" + str(y_train.shape))\n",
        "print(\"y_test : \" + str(y_test.shape))\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "psKYYN9_-jTI"
      },
      "source": [
        "### 問5. 学習\n",
        "\n",
        "5.1. RandamForest の学習モデルのインスタンスを生成しましょう。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "tQGdKQsh_HHe"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model = RandomForestClassifier()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "OseolTqIGfsc"
      },
      "source": [
        "5.2. `x_train` と `y_train` を用いて、モデルを学習させましょう。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "id": "x7ePCmqnJ6Ic",
        "outputId": "cfad6048-c06f-40f2-846c-cafc73b7168c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/pontagudetama/.pyenv/versions/3.12.2/lib/python3.12/site-packages/sklearn/base.py:1474: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return fit_method(estimator, *args, **kwargs)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.fit(x_train, y_train)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "5cKU0YfKIUTQ"
      },
      "source": [
        "### 問6. 推論\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "iHjsUA3K3MeI"
      },
      "source": [
        "6.1. 学習したモデルに `x_test` を入力して予測をしましょう。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "RUgEb9q-Idbb"
      },
      "outputs": [],
      "source": [
        "test = model.predict(x_test)\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "c1ZE3GGx3Tal"
      },
      "source": [
        "6.2. 予測結果と `y_test` に対して、`accuracy` を計算することでモデルを評価しましょう。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TTI2DqDlIjhP",
        "outputId": "9b0c755f-652e-4017-fd29-382369aec2b3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9666666666666667"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test,test)\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "実は、iris データセットはかなり簡単な分布をしているので、正解率はほぼ 100% になったと思います。"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}