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": 68,
      "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": 2,
      "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": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "T7StoNXU2iXm",
        "outputId": "0fedad15-b679-44d2-b9c7-d3bf12198c40"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\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": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_data.head()"
      ]
    },
    {
      "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": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "mDKbvknP6arO",
        "outputId": "9b0dc5de-a88d-49e4-b2ba-b325ef698519"
      },
      "outputs": [
        {
          "data": {
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              "</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>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": 4,
          "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": 5,
      "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": 5,
          "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": 10,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     target\n",
              "51        1\n",
              "76        1\n",
              "21        0\n",
              "32        0\n",
              "115       2\n",
              "83        1\n",
              "70        1\n",
              "57        1\n",
              "20        0\n",
              "10        0"
            ]
          },
          "execution_count": 10,
          "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": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "TnUb1cRZ5ajf",
        "outputId": "ba5a4fb4-15c2-4dc3-a5d4-a3907bff2a51"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(close=None, block=None)>"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_merged = pd.merge(df_data, df_target,left_index=True, right_index=True)\n",
        "setosa =df_merged[df_merged[\"target\"]==0]\n",
        "versicolor =df_merged[df_merged[\"target\"]==1]\n",
        "virginica =df_merged[df_merged[\"target\"]==2]\n",
        "df_merged\n",
        "\n",
        "#plot\n",
        "plt.scatter(setosa[\"petal length (cm)\"], setosa[\"petal width (cm)\"], label=\"setosa\",c=\"r\")\n",
        "plt.scatter(versicolor[\"petal length (cm)\"], versicolor[\"petal width (cm)\"], label=\"versicolor\",c=\"g\", marker=\"+\")\n",
        "plt.scatter(virginica[\"petal length (cm)\"], virginica[\"petal width (cm)\"], label=\"virginica\", c=\"b\", marker=\"x\")\n",
        "plt.xlabel(\"petal length (cm)\")\n",
        "plt.ylabel(\"petal width (cm)\")\n",
        "plt.title(\"Iris Plats Database\")\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": 56,
      "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)"
      ]
    },
    {
      "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": 57,
      "metadata": {},
      "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:\",x_train.shape)\n",
        "print(\"x_test:\",x_test.shape)\n",
        "print(\"y_train:\",y_train.shape)\n",
        "print(\"y_test:\",y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": []
    },
    {
      "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": 58,
      "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": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "id": "x7ePCmqnJ6Ic",
        "outputId": "cfad6048-c06f-40f2-846c-cafc73b7168c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\a123o\\Desktop\\PG\\BasicPython\\.venv\\Lib\\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": 60,
          "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": 61,
      "metadata": {
        "id": "RUgEb9q-Idbb"
      },
      "outputs": [],
      "source": [
        "test = model.predict(x_test)"
      ]
    },
    {
      "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": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TTI2DqDlIjhP",
        "outputId": "9b0c755f-652e-4017-fd29-382369aec2b3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test,test)"
      ]
    },
    {
      "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": "3.12.3"
    }
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  "nbformat": 4,
  "nbformat_minor": 0
}