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": 1,
      "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": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "T7StoNXU2iXm",
        "outputId": "0fedad15-b679-44d2-b9c7-d3bf12198c40"
      },
      "outputs": [
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              "  <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",
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              "    <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",
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              "    <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",
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              "      <th>4</th>\n",
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              "      <td>3.6</td>\n",
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              "\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",
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              "  "
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            "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"
            ]
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      "source": []
    },
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      "metadata": {},
      "source": [
        "1.2. `df_data` の行数と列数を表示しましょう。"
      ]
    },
    {
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        {
          "data": {
            "text/plain": [
              "(150, 4)"
            ]
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      "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"
      ]
    },
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              "      <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",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            "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  "
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          "execution_count": 6,
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    },
    {
      "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": null,
      "metadata": {
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          "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"
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    {
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        "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": 6,
      "metadata": {
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        },
        "id": "_CDs0X1Y8YX8",
        "outputId": "d359aa9d-1044-4eb9-b31c-85f9b79ab99b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     target\n",
              "114       2\n",
              "62        1\n",
              "33        0\n",
              "107       2\n",
              "7         0\n",
              "100       2\n",
              "40        0\n",
              "86        1\n",
              "76        1\n",
              "71        1"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": []
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "XPik8k4I2_Zz"
      },
      "source": [
        "### 問3. データの可視化\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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "TnUb1cRZ5ajf",
        "outputId": "ba5a4fb4-15c2-4dc3-a5d4-a3907bff2a51"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(close=None, block=None)>"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": []
    },
    {
      "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": null,
      "metadata": {
        "id": "KZfVWvll9T-X"
      },
      "outputs": [],
      "source": []
    },
    {
      "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": 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": null,
      "metadata": {
        "id": "tQGdKQsh_HHe"
      },
      "outputs": [],
      "source": []
    },
    {
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "id": "x7ePCmqnJ6Ic",
        "outputId": "cfad6048-c06f-40f2-846c-cafc73b7168c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-13-139fa7af76d2>:1: 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",
            "  model.fit(x_train, y_train)\n"
          ]
        },
        {
          "data": {
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      "source": [
        "### 問6. 推論\n"
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      "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。"
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      "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。"
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              "0.9666666666666667"
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      "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。"
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