Day_010_HW - outliers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業 : (Kaggle)房價預測"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業目標]\n",
    "- 試著模仿範例寫法, 在房價預測中, 觀察去除離群值的影響"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 觀察將極端值以上下限值取代, 對於分布與迴歸分數的影響 (In[5], Out[5])\n",
    "- 觀察將極端值資料直接刪除, 對於分布與迴歸分數的影響 (In[6], Out[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 做完特徵工程前的所有準備 (與前範例相同)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities LotConfig  ... ScreenPorch PoolArea PoolQC Fence  \\\n",
       "0         Lvl    AllPub    Inside  ...           0        0    NaN   NaN   \n",
       "1         Lvl    AllPub       FR2  ...           0        0    NaN   NaN   \n",
       "2         Lvl    AllPub    Inside  ...           0        0    NaN   NaN   \n",
       "3         Lvl    AllPub    Corner  ...           0        0    NaN   NaN   \n",
       "4         Lvl    AllPub       FR2  ...           0        0    NaN   NaN   \n",
       "\n",
       "  MiscFeature MiscVal  MoSold  YrSold  SaleType  SaleCondition  \n",
       "0         NaN       0       2    2008        WD         Normal  \n",
       "1         NaN       0       5    2007        WD         Normal  \n",
       "2         NaN       0       9    2008        WD         Normal  \n",
       "3         NaN       0       2    2006        WD        Abnorml  \n",
       "4         NaN       0      12    2008        WD         Normal  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv(r\"C:\\Users\\user\\100 days\\data\\house_train.csv.gz\")\n",
    "\n",
    "train_Y = np.log1p(df_train['SalePrice'])\n",
    "df = df_train.drop(['Id', 'SalePrice'] , axis=1)\n",
    "df.head()\n",
    "\n",
    "# 数据平滑处理 -- log1p( ) 和 exmp1( )\n",
    "# data frame 可以透過 drop() 方法來刪除觀測值或欄位\n",
    "# 指定參數 axis = 0 表示要刪除觀測值（row），指定參數 axis = 1 表示要刪除欄位（column）\n",
    "# https://ithelp.ithome.com.tw/articles/10185922"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36 Numeric Features : ['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#只取 int64, float64 兩種數值型欄位, 存於 num_features 中\n",
    "num_features = []\n",
    "for dtype, feature in zip(df.dtypes, df.columns):\n",
    "    if dtype == 'float64' or dtype == 'int64':\n",
    "        num_features.append(feature)\n",
    "print(f'{len(num_features)} Numeric Features : {num_features}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>548</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>460</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>608</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>642</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>836</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0          60         65.0     8450            7            5       2003   \n",
       "1          20         80.0     9600            6            8       1976   \n",
       "2          60         68.0    11250            7            5       2001   \n",
       "3          70         60.0     9550            7            5       1915   \n",
       "4          60         84.0    14260            8            5       2000   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  GarageArea  \\\n",
       "0          2003       196.0         706           0  ...         548   \n",
       "1          1976         0.0         978           0  ...         460   \n",
       "2          2002       162.0         486           0  ...         608   \n",
       "3          1970         0.0         216           0  ...         642   \n",
       "4          2000       350.0         655           0  ...         836   \n",
       "\n",
       "   WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  \\\n",
       "0           0           61              0          0            0         0   \n",
       "1         298            0              0          0            0         0   \n",
       "2           0           42              0          0            0         0   \n",
       "3           0           35            272          0            0         0   \n",
       "4         192           84              0          0            0         0   \n",
       "\n",
       "   MiscVal  MoSold  YrSold  \n",
       "0        0       2    2008  \n",
       "1        0       5    2007  \n",
       "2        0       9    2008  \n",
       "3        0       2    2006  \n",
       "4        0      12    2008  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 削減文字型欄位, 只剩數值型欄位\n",
    "df = df[num_features]\n",
    "df = df.fillna(-1)\n",
    "MMEncoder = MinMaxScaler()\n",
    "train_num = train_Y.shape[0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業1\n",
    "* 試著限制 '1樓地板面積(平方英尺)' (1stFlrSF) 欄位的上下限, 看看能否再進一步提高分數?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 顯示 1stFlrSF 與目標值的散佈圖\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "sns.regplot(x = df['1stFlrSF'][:train_num], y=train_Y)\n",
    "plt.show()\n",
    "\n",
    "# Figure size 640x480 with 1 Axes ---> 表示圖畫不出來要加這條 : %matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\data.py:334: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.846643681071632"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 做線性迴歸, 觀察分數\n",
    "train_X = MMEncoder.fit_transform(df)\n",
    "estimator = LinearRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 將 1stFlrSF 限制在你覺得適合的範圍內, 調整離群值\n",
    "df['1stFlrSF'] = df['1stFlrSF'].clip(500, 2000)\n",
    "sns.regplot(x = df['1stFlrSF'][:train_num], y=train_Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\data.py:334: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.876580486010791"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 做線性迴歸, 觀察分數\n",
    "train_X = MMEncoder.fit_transform(df)\n",
    "estimator = LinearRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業2\n",
    "* 續前題, 去除離群值有兩類方式 :  捨棄離群值(刪除離群的資料) 以及調整離群值,  \n",
    "請試著用同樣的上下限, 改為 '捨棄離群值' 的方法, 看看結果會變好還是變差? 並試著解釋原因。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 將 1stFlrSF 限制在你覺得適合的範圍內, 捨棄離群值\n",
    "\n",
    "keep_indexs = (df['1stFlrSF']> 500) & (df['1stFlrSF']< 2000)\n",
    "df = df[keep_indexs]\n",
    "train_Y = train_Y[keep_indexs]\n",
    "sns.regplot(x = df['1stFlrSF'], y=train_Y)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\data.py:334: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8855310108955313"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 做線性迴歸, 觀察分數\n",
    "train_X = MMEncoder.fit_transform(df)\n",
    "estimator = LinearRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# raw data 回歸 0.84 ---> 限制範圍 & 離群值變垂直點 0.87 ---> 再扣掉離群值 0.88\n",
    "\n",
    "# _xsrf argument missing form post ---> 不能存檔 ---> 刷新启动页 http://localhost:8888/tree/[yourAddress] 即可\n",
    "# 2019/09/19"
   ]
  }
 ],
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