okwrtdsh/data_science1.ipynb

## data_science1.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 機械学習の主なアルゴリズム\n",
    "\n",
    "## 1. [教師あり学習](https://ja.wikipedia.org/wiki/教師あり学習)\n",
    "入力データと正解データがセットで与えられる\n",
    "\n",
    "### 1.1 [回帰](https://ja.wikipedia.org/wiki/回帰分析)\n",
    "$Y$ が連続値の時にデータに $Y = f(X)$ というモデル(「定量的な関係の構造」)を当てはめる事\n",
    "\n",
    "#### 線形\n",
    "* [線形回帰](https://ja.wikipedia.org/wiki/線形回帰)\n",
    "\n",
    "#### 非線形\n",
    "* [k近傍法](https://ja.wikipedia.org/wiki/K近傍法)\n",
    "* [決定木](https://ja.wikipedia.org/wiki/決定木)\n",
    "* [**ランダムフォレスト**](https://ja.wikipedia.org/wiki/ランダムフォレスト)\n",
    "* [ニューラルネットワーク](https://ja.wikipedia.org/wiki/ニューラルネットワーク)\n",
    "* [サポートベクターマシン](https://ja.wikipedia.org/wiki/サポートベクターマシン)\n",
    "\n",
    "### 1.2 [分類](https://ja.wikipedia.org/wiki/分類_%28統計学%29)\n",
    "データを複数のクラス（グループ）に分類すること\n",
    "\n",
    "#### 線形\n",
    "* [ロジスティック回帰](https://ja.wikipedia.org/wiki/ロジスティック回帰)\n",
    "* [単純パーセプトロン](https://ja.wikipedia.org/wiki/パーセプトロン)\n",
    "* [線形サポートベクターマシン](https://ja.wikipedia.org/wiki/サポートベクターマシン)\n",
    "\n",
    "#### 非線形\n",
    "* [k近傍法](https://ja.wikipedia.org/wiki/K近傍法)\n",
    "* [決定木](https://ja.wikipedia.org/wiki/決定木)\n",
    "* [**ランダムフォレスト**](https://ja.wikipedia.org/wiki/ランダムフォレスト)\n",
    "* [ニューラルネットワーク](https://ja.wikipedia.org/wiki/ニューラルネットワーク)\n",
    "* [サポートベクターマシン](https://ja.wikipedia.org/wiki/サポートベクターマシン)\n",
    "\n",
    "## 2. [教師なし学習](https://ja.wikipedia.org/wiki/教師なし学習)\n",
    "入力データのみが与えられる\n",
    "\n",
    "### 2.1 [クラスタリング](https://ja.wikipedia.org/wiki/データ・クラスタリング)\n",
    "与えられたデータを外的基準なしに自動的に分類する手法\n",
    "* [k平均法](https://ja.wikipedia.org/wiki/K平均法)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Kaggle Titanic](https://www.kaggle.com/c/titanic/)\n",
    "上記のようにたくさんのアルゴリズムがありますが、\n",
    "今日は回帰や分類など様々なタスクで使用でき、\n",
    "\n",
    "学習が早く、推論根拠がわかりやすい、**ランダムフォレスト**を使用します。\n",
    "\n",
    "\n",
    "まず、以下のURLからデータセットをダウンロードしてください。\n",
    "\n",
    "https://www.kaggle.com/c/titanic/data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### データの意味\n",
    "|Variable|Definition|Key|\n",
    "|:-:|:-:|:-:|\n",
    "|survival|Survival|0 = No, 1 = Yes|\n",
    "|pclass|Ticket class|1 = 1st, 2 = 2nd, 3 = 3rd|\n",
    "|sex|Sex||\n",
    "|Age|Age in years||\n",
    "|sibsp|# of siblings / spouses aboard the Titanic||\n",
    "|parch|# of parents / children aboard the Titanic||\n",
    "|ticket|Ticket number||\n",
    "|fare|Passenger fare||\n",
    "|cabin|Cabin number||\n",
    "|embarked|Port of Embarkation|C = Cherbourg, Q = Queenstown, S = Southampton|\n",
    "\n",
    "#### Variable Notes\n",
    "##### pclass:\n",
    "A proxy for socio-economic status (SES)\n",
    "* 1st = Upper\n",
    "* 2nd = Middle\n",
    "* 3rd = Lower\n",
    "\n",
    "##### age:\n",
    "Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5\n",
    "##### sibsp:\n",
    "The dataset defines family relations in this way...\n",
    "* Sibling = brother, sister, stepbrother, stepsister\n",
    "* Spouse = husband, wife (mistresses and fiancés were ignored)\n",
    "\n",
    "##### parch:\n",
    "The dataset defines family relations in this way...\n",
    "* Parent = mother, father\n",
    "* Child = daughter, son, stepdaughter, stepson\n",
    "Some children travelled only with a nanny, therefore parch=0 for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np  # 数値計算ライブラリ\n",
    "import pandas as pd  # SQLライクなデータ解析ライブラリ\n",
    "from matplotlib import pyplot as plt  # 可視化ライブラリ\n",
    "import seaborn as sns  # matplotlibベースの可視化ライブラリ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train(学習用)とtest(検証用)のデータセットをpd.DataFrameに読み込む\n",
    "df_train = pd.read_csv(\"./titanic/train.csv\")\n",
    "df_test = pd.read_csv(\"./titanic/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>Embarked</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>S</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>female</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
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       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 先頭5行を表示\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
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       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
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       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
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       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
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       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# テストには\"Survived\"はありません\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 統計情報の概要\n",
    "# 型が数値でないと表示されない\n",
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 学習に使用する特徴量\n",
    "features = ['Pclass', 'Age']\n",
    "target = 'Survived'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# trainとvalidationデータに分割(train:validation=7:3)\n",
    "train, val = train_test_split(df_train[[target] + features], test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train[features].fillna(0).values  # 取り敢えず欠損値を0で埋める\n",
    "y_train = train[target].values\n",
    "X_val = val[features].fillna(0).values\n",
    "y_val = val[target].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=5, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=4,\n",
       "            oob_score=False, random_state=123, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(\n",
    "    n_estimators=10,  # 木(決定木)の数\n",
    "    max_depth=5,  # 深さ\n",
    "    n_jobs=4,  # 並列プロセス数\n",
    "    random_state=123,  # 乱数を固定(再現性)\n",
    ")\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6902985074626866"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# validationデータでスコアを計算\n",
    "clf.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# 提出してみよう\n",
    "pred = clf.predict(df_test[features].fillna(0))\n",
    "result = pd.DataFrame({'PassengerId': df_test['PassengerId'], target: pred})\n",
    "result.to_csv('./pred.csv', index=False) # 0.67942くらい"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RandomForestClassifierの学習結果を可視化してみよう"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD8CAYAAABzTgP2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAACyhJREFUeJzt3G2MpXdZx/Hf1W4rFKvEttFKS4diY2XbBu1qRBKC1cSnpKCshkasTQqkPr7wIRCLjRSJ0b5QQI2UYLBGU9KiSW20RLA8GWmZ1d2WlRQLAlZbddukIlRry98Xc5C91u3OmZnzMDN8Pskkc+bcc/a6cmb3O/d9ZrbGGAGALzpp2QMAsL0IAwCNMADQCAMAjTAA0AgDAI0wANAIAwCNMADQ7Fn2AJtx5plnjpWVlWWPAbCjHDhw4MgY46z1jtuRYVhZWcnq6uqyxwDYUarq09Mc51ISAI0wANAIAwCNMADQCAMAjTAA0AgDAI0wANAIAwDNjvzN54898HAu/cWblj0GMAMHbrhy2SNwDGcMADTCAEAjDAA0wgBAIwwANMIAQCMMADTCAEAjDAA0wgBAIwwANMIAQCMMADTCAEAjDAA0wgBAIwwANMIAQCMMADTCAEAjDAA0wgBAIwwANMIAQCMMADTCAEAjDAA0wgBAIwwANMIAQCMMADTCAEAjDAA0wgBAIwwANMIAQCMMADTCAEAjDAA064ahqp6sqoNV9dGquqWqTjvBsb9SVb8w2xEBWKRpzhgeG2M8f4xxUZLHk1wz55kAWKKNXkr6YJJvSJKqurKq7qmqQ1X1h8ceWFWvqqqPTO5/1xfPNKrqhydnH4eq6gOTj+2tqrsnZyb3VNUFW10MgM3ZM+2BVbUnyfcluaOq9ia5NskLxxhHquprjvMpfzLGeNvkc381ydVJ3pLkuiTfM8b456p65uTYa5K8aYzxR1V1apKTN78SAFsxzRnD06vqYJLVJJ9J8vYklyW5dYxxJEnGGI8c5/MuqqoPVtW9SX40yd7Jx/86yTuq6lX5UgD+JskvVdVrkpw3xnjs2AerqldX1WpVrT7x+c9uYEUANmIjrzE8f4zxM2OMx5NUkrHO570jyU+PMS5O8vokT0uSMcY1SV6X5NwkB6vqjDHGHye5PMljSd5dVZcd+2BjjBvHGPvGGPv2nHb6tPsBsEGb/XHV9yb5kao6I0me4lLS6UkerKpTsnbGkMmxzx1j3DXGuC7JkSTnVtX5ST45xnhzktuSXLLJuQDYoqlfYzjaGONwVb0xyfur6skkf5fkqmMO++UkdyX5dJJ7sxaKJLlh8uJyZS0wh5K8Nskrqup/kjyU5PrNzAXA1tUY610R2n6e8XXPGRf+2OuXPQYwAwduuHLZI3zZqKoDY4x96x3nN58BaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgGbPsgfYjG8654ys3nDlsscA2JWcMQDQCAMAjTAA0AgDAI0wANAIAwCNMADQCAMAjTAA0AgDAI0wANAIAwCNMADQCAMAjTAA0AgDAI0wANAIAwCNMADQCAMAjTAA0OxZ9gCb8fiDh/OZ6y9e9hgAC/Xs6+5dyJ/jjAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgGYuYaiqH6yqUVUXzuPxAZifeZ0xXJHkQ0lePqfHB2BOZh6GqvrKJC9McnUmYaiqk6rqd6vqcFXdXlV/XlX7J/ddWlXvr6oDVfXuqjp71jMBML15nDG8NMkdY4yPJ3mkqr4lyQ8lWUlycZJXJnlBklTVKUnekmT/GOPSJL+f5I3He9CqenVVrVbV6iOfe3IOYwOQJHvm8JhXJPmtyfs3T26fkuSWMcYXkjxUVXdO7v/GJBcl+cuqSpKTkzx4vAcdY9yY5MYkueRZTx9zmBuAzDgMVXVGksuSXFRVI2v/0I8kf/pUn5Lk8BjjBbOcA4DNm/WlpP1JbhpjnDfGWBljnJvkH5McSfKyyWsNX5vkxZPj70tyVlX936Wlqto745kA2IBZh+GK/P+zg3cl+fokDyT5aJK3JrkryaNjjMezFpNfr6pDSQ4m+Y4ZzwTABsz0UtIY48XH+dibk7WfVhpj/OfkctPdSe6d3H8wyYtmOQcAmzePF5+fyu1V9cwkpyZ5wxjjoQX+2QBMaWFhON7ZBADbj/8rCYBGGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgEYYAGj2LHuAzTj17L159nWryx4DYFdyxgBAIwwANMIAQCMMADTCAEAjDAA0wgBAIwwANMIAQCMMADQ1xlj2DBtWVZ9Nct+y55ijM5McWfYQc7bbd7TfzrZb9ztvjHHWegftyP8rKcl9Y4x9yx5iXqpqdTfvl+z+He23s+32/dbjUhIAjTAA0OzUMNy47AHmbLfvl+z+He23s+32/U5oR774DMD87NQzBgDmZFuHoaq+t6ruq6r7q+q1x7n/K6rqnZP776qqlcVPuXlT7PeiqvrbqnqiqvYvY8atmGK/n6uqv6+qe6rqvVV13jLm3Iopdrymqu6tqoNV9aGqet4y5tys9fY76rj9VTWqakf9JM8Uz99VVfXvk+fvYFW9chlzLtwYY1u+JTk5ySeSnJ/k1CSHkjzvmGN+MsnvTd5/eZJ3LnvuGe+3kuSSJDcl2b/smeew33cmOW3y/k/spOdvAzt+1VHvX57kjmXPPcv9JsednuQDST6cZN+y557x83dVkt9e9qyLftvOZwzfluT+McYnxxiPJ7k5yUuOOeYlSf5g8v6tSb6rqmqBM27FuvuNMT41xrgnyReWMeAWTbPfnWOMz09ufjjJOQuecaum2fE/jrr5jCQ76UW9af4OJskbkvxGkv9a5HAzMO1+X3a2cxieleSfjrr9wORjxz1mjPFEkkeTnLGQ6bZumv12so3ud3WSv5jrRLM31Y5V9VNV9Yms/eP5swuabRbW3a+qvjnJuWOM2xc52IxM+zX6ssnlzlur6tzFjLZc2zkMx/vO/9jvtqY5ZrvaybNPY+r9quoVSfYluWGuE83eVDuOMX5njPHcJK9J8rq5TzU7J9yvqk5K8ptJfn5hE83WNM/fnyVZGWNckuQ9+dIVil1tO4fhgSRH1/mcJP/yVMdU1Z4kX53kkYVMt3XT7LeTTbVfVX13kmuTXD7G+O8FzTYrG30Ob07y0rlONFvr7Xd6kouSvK+qPpXk25PctoNegF73+RtjPHzU1+Xbkly6oNmWajuH4SNJLqiq51TVqVl7cfm2Y465LcmPT97fn+SvxuQVox1gmv12snX3m1yGeGvWovBvS5hxq6bZ8YKjbv5Akn9Y4HxbdcL9xhiPjjHOHGOsjDFWsvY60eVjjNXljLth0zx/Zx918/IkH1vgfMuz7Fe/T/SW5PuTfDxrPzlw7eRj12ftiy9JnpbkliT3J7k7yfnLnnnG+31r1r6r+VySh5McXvbMM97vPUn+NcnBydtty555Dju+KcnhyX53Jtm77Jlnud8xx74vO+inkqZ8/n5t8vwdmjx/Fy575kW8+c1nAJrtfCkJgCUQBgAaYQCgEQYAGmEAoBEGABphAKARBgCa/wV3njJUyc1VQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 特徴量の重要度\n",
    "sns.barplot(x=clf.feature_importances_, y=features)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# ubuntuの場合\n",
    "# !apt-get update -qq\n",
    "# !apt-get install -y graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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