회귀 (LinearRegression).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston 데이터셋 크기: (506, 14)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  PRICE  \n",
       "0     15.3  396.90   4.98   24.0  \n",
       "1     17.8  396.90   9.14   21.6  \n",
       "2     17.8  392.83   4.03   34.7  \n",
       "3     18.7  394.63   2.94   33.4  \n",
       "4     18.7  396.90   5.33   36.2  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#보스턴 주택가격\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sklearn.datasets import load_boston\n",
    "%matplotlib inline\n",
    "\n",
    "# Boston 데이터셋 로드\n",
    "boston = load_boston()\n",
    "\n",
    "# Boston 데이터셋 DataFrame 변환\n",
    "bostonDF = pd.DataFrame(boston.data, columns = boston.feature_names)\n",
    "\n",
    "# Bonston dataset의 target array는 주택 가격. 이를 PRICE 컬럼으로 DataFrame에 추가\n",
    "bostonDF['PRICE'] = boston.target\n",
    "print('Boston 데이터셋 크기:',bostonDF.shape)\n",
    "bostonDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     506 non-null    float64\n",
      " 1   ZN       506 non-null    float64\n",
      " 2   INDUS    506 non-null    float64\n",
      " 3   CHAS     506 non-null    float64\n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      506 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    float64\n",
      " 9   TAX      506 non-null    float64\n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    506 non-null    float64\n",
      " 13  PRICE    506 non-null    float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 55.5 KB\n"
     ]
    },
    {
     "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677083</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT       PRICE  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bostonDF.info()\n",
    "bostonDF.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#상관관계 : 시각화\n",
    "#2개의 행과 4개의 열을 가진 subplots를 이용. axs는 4x2개의 ax를 가짐\n",
    "fig, axs = plt.subplots(figsize=(16,8), ncols=4, nrows=2)\n",
    "lm_features = ['RM', \"ZN\", 'INDUS', 'NOX', \"AGE\", \"PTRATIO\", \"LSTAT\", \"RAD\"]\n",
    "for i, feature in enumerate(lm_features):\n",
    "    row = int(i/4)\n",
    "    col = i%4\n",
    "    # 시본의 regplot을 이용해 산점도와 선형 회귀 직선을 함께 표현\n",
    "    sns.regplot(x=feature, y='PRICE', data=bostonDF, ax=axs[row][col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE : 17.297, RMSE : 4.159\n",
      "Variance score : 0.757\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "y_target = bostonDF['PRICE']\n",
    "X_data = bostonDF.drop(['PRICE'], axis = 1, inplace=False)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_data, y_target, test_size = 0.3, random_state = 156)\n",
    "\n",
    "#Linear Regression 0LS로 학습/예측/평가 수행\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "y_preds = lr.predict(X_test)\n",
    "mse = mean_squared_error(y_test, y_preds)\n",
    "rmse = np.sqrt(mse)\n",
    "\n",
    "#r2_score = 모델의 정확도\n",
    "print('MSE : {0:.3f}, RMSE : {1:.3f}'.format(mse, rmse))\n",
    "print('Variance score : {0:.3f}'.format(r2_score(y_test,y_preds)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "절편 값: 40.995595172164315\n",
      "회귀 계수값: [ -0.1   0.1   0.    3.  -19.8   3.4   0.   -1.7   0.4  -0.   -0.9   0.\n",
      "  -0.6]\n"
     ]
    }
   ],
   "source": [
    "print('절편 값:', lr.intercept_)\n",
    "print('회귀 계수값:', np.round(lr.coef_,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RM          3.4\n",
       "CHAS        3.0\n",
       "RAD         0.4\n",
       "ZN          0.1\n",
       "B           0.0\n",
       "TAX        -0.0\n",
       "AGE         0.0\n",
       "INDUS       0.0\n",
       "CRIM       -0.1\n",
       "LSTAT      -0.6\n",
       "PTRATIO    -0.9\n",
       "DIS        -1.7\n",
       "NOX       -19.8\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 회귀 계수를 큰 값 순으로 정렬하기 위해 Series로 생성. index가 컬럼명에 유의\n",
    "coeff = pd.Series(data=np.round(lr.coef_, 1), index=X_data.columns)\n",
    "coeff.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 5 folds 의 개별 Negative MSE scores:  [-12.46 -26.05 -33.07 -80.76 -33.31]\n",
      " 5 folds 의 개별 RMSE scores:  [3.53 5.1  5.75 8.99 5.77]\n",
      " 5 folds 의 평균 RMSE : 5.829 \n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "y_target = bostonDF['PRICE']\n",
    "X_data = bostonDF.drop(['PRICE'], axis=1, inplace=False)\n",
    "lr = LinearRegression()\n",
    "\n",
    "#cross_val_score()로 5 Fold 셋으로 MSE를 구한 뒤 이를 기반으로 다시 RMSE구함\n",
    "neg_mse_scores = cross_val_score(lr, X_data, y_target, scoring = 'neg_mean_squared_error', cv = 5)\n",
    "rmse_scores = np.sqrt(-1 * neg_mse_scores)\n",
    "avg_rmse = np.mean(rmse_scores)\n",
    "\n",
    "#cross_val_score(scoring = 'neg_mean_squared_error')로 반환된 값은 모두 음수\n",
    "print(' 5 folds 의 개별 Negative MSE scores: ', np.round(neg_mse_scores, 2))\n",
    "print(' 5 folds 의 개별 RMSE scores: ', np.round(rmse_scores, 2))\n",
    "print(' 5 folds 의 평균 RMSE : {0:.3f} '.format(avg_rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston 데이터셋 크기 :  (506, 14)\n",
      " 5 folds 의 개별 Negative MSE scores:  [-11.422 -24.294 -28.144 -74.599 -28.517]\n",
      " 5 folds 의 개별 RMSE scores:  [3.38  4.929 5.305 8.637 5.34 ]\n",
      " 5 folds 의 평균 RMSE : 5.518 \n"
     ]
    }
   ],
   "source": [
    "#릿지(Ridge) 회귀\n",
    "#앞의 LinearRegression예제에서 분할한 feature 데이터 셋인 X_data과 Target데이터 셋인 Y_target 데이터셋을 그대로\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "#boston 데이터셋 로드\n",
    "boston = load_boston()\n",
    "\n",
    "#boston 데이터셋 DataFrame 변환\n",
    "bostonDF = pd.DataFrame(boston.data , columns = boston.feature_names)\n",
    "\n",
    "#boston dataset의 target array는 주택 가격임. 이를 PRICE컬럼으로 DataFrame에 추가함\n",
    "bostonDF['PRICE'] = boston.target\n",
    "print('Boston 데이터셋 크기 : ', bostonDF.shape)\n",
    "\n",
    "y_target = bostonDF['PRICE']\n",
    "X_data = bostonDF.drop(['PRICE'], axis=1, inplace=False)\n",
    "\n",
    "ridge = Ridge(alpha=10)\n",
    "neg_mse_scores = cross_val_score(ridge, X_data, y_target, scoring = \"neg_mean_squared_error\", cv = 5)\n",
    "rmse_scores = np.sqrt(-1 * neg_mse_scores)\n",
    "avg_rmse = np.mean(rmse_scores)\n",
    "print(' 5 folds 의 개별 Negative MSE scores: ', np.round(neg_mse_scores, 3))\n",
    "print(' 5 folds 의 개별 RMSE scores: ', np.round(rmse_scores, 3))\n",
    "print(' 5 folds 의 평균 RMSE : {0:.3f} '.format(avg_rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha 0일 때 5 folds 의 평균 RMSE : 5.829 \n",
      "alpha 0.1일 때 5 folds 의 평균 RMSE : 5.788 \n",
      "alpha 1일 때 5 folds 의 평균 RMSE : 5.653 \n",
      "alpha 10일 때 5 folds 의 평균 RMSE : 5.518 \n",
      "alpha 100일 때 5 folds 의 평균 RMSE : 5.330 \n"
     ]
    }
   ],
   "source": [
    "#Ridge에 사용될 alpha 파라미터의 값들을 정의\n",
    "alphas = [0, 0.1, 1, 10, 100]\n",
    "\n",
    "# alphas list 값을 iteration하면서 alpha에 따른 평균 rmse 구함\n",
    "for alpha in alphas:\n",
    "    ridge = Ridge(alpha = alpha)\n",
    "    \n",
    "    #cross_val_score를 이용하여 5 fold의 평균 RMSE 계싼\n",
    "    neg_mse_scores = cross_val_score(ridge, X_data, y_target, scoring = \"neg_mean_squared_error\", cv = 5)\n",
    "    avg_rmse = np.mean(np.sqrt(-1 * neg_mse_scores))\n",
    "    print('alpha {0}일 때 5 folds 의 평균 RMSE : {1:.3f} '.format(alpha,avg_rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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huacn2TjBcqVmmB2pP/Mj9Wd+pP7MjzR3TTUuqup9VbVu+gf4S+CfgY8A3wRelGS3SdYotcjsSP2ZH6k/8yP1Z36kuWuqcTEsyU8D/wv4ZeD7wBTwceC5k6xLap3ZkfozP1J/5kfqz/xIO9Zk4yLJrsDfASdX1deHhl4NvCTJqh2ce0KSzUk2T01NjbpUqSkLyU53vvnRimV+pP7Mj9Sf+ZF2rsnGBfC/gSur6pzhg1V1LXAx8OzZTqyqM6pqfVWtX7NmzYjLlJrTOzvdPPOjlcz8SP2ZH6k/8yPtxC6TLmCmJEcBxwKHzTLllcDfA58cU0nSkmB2pP7Mj9Sf+ZH6Mz/S3DS14iLJjwJvB36lqm7b3pyq+iJwFXD0OGuTWmZ2pP7Mj9Sf+ZH6Mz/S3LW24uI3gR8D3pxk+Pi7Zsx7BXDZuIqSlgCzI/VnfqT+zI/Un/mR5qipxkVVvQp41SzDpw7Nu5zGVotIk2R2pP7Mj9Sf+ZH6Mz/S3BkASZIkSZLULBsXkiRJkiSpWTYuJEmSJElSs2xcSJIkSZKkZtm4WAY2Pvejky5BWrLMj9Sf+ZH6Mz9SP2ZnZbJxIUmSJEmSmmXjQpIkSZIkNcvGhSRJkiRJapaNC0mSJEmS1CwbF5IkSZIkqVk2LpaBP3jvEyZdgiRJkiSNnH/3WZlsXEiSJEmSpGbZuJAkSZIkSc2ycSFJkiRJkppl40KSJEmSJDXLxoUkSZIkSWrWojUuktze/d+1SSrJ7wyNnZ5kY7d9VpJrk1ye5JokZyfZd+Z1hvY3Jjm92z4gyUVJtiS5OskZi1W/NEmrV68G4LrrrgM43PxI7UhyZ5eby5NcmuR/TLomaakwP1J/5kf6oVGtuPgm8KIku80yfkpVPQw4ALgMuHAHc4e9ETitqtZV1UHAXyxOuVJTtmF+pJZs7XLzMODlwKsmXZC0hJgfqT/zI3VG1biYAj4OPHdHk2rgNOAm4IlzuO4+wPVD51+xkCKlRm3D/Eitug/wrUkXIS1R5kfqz/xoRdtlhNd+NXB+kjPnMPdS4EDgH3cy7zTgE0k+DXwUeHtVfXtBVUptMj9SO/ZIsgX4EQYNwMdOthxpSTE/Un/mR+qM7OGcVXUtcDHw7DlMz84u113z7cBBwHuBo4DPJtn9LhdKTkiyOcnmqampedcttcD8SE2ZXqp7IPAE4Owkd8ud+ZG2y/xI/ZkfqTPqbxV5JfCyObzOw4Gru+2tM+7Xvx9w8/ROVd1YVWdW1VMZLKk/ZPhCVXVGVa2vqvVr1qxZ8BuQJsj8SI2pqs8A9wfuFhDzI+2Y+ZH6Mz9a6UbauKiqLwJXAUdvbzwDJzJY+nRBd/ifgOO78T2AZwIXdvtPSLJrt703sBdwwyjfgzQp5kdqT5IDgVXALZOuRVpqzI/Un/nRSjfKZ1xMewWDbz4Y9tokfwjcE/gs8JiquqMbexHwV91fyAKcXVWf7MZ+HnhDkv/s9k+pqptGW740UeZHmrzpe4xhkKvnVtWdE6xHWkrMj9Sf+ZE6i9a4qKrV3f+9jqHl51V1OUMrO6pq406ucwOz/AtzVZ0EnLTwaqW23H777QCsXbsW4Mrp4+ZHmryqWjXpGqSlyvxI/Zkf6YdG/YwLSZIkSZKk3mxcSJIkSZKkZtm4kCRJkiRJzbJxIUmSJEmSmmXjQpIkSZIkNcvGxTLwimdcMOkSJEmSJGnk/LvPymTjQpIkSZIkNcvGhSRJkiRJapaNC0mSJEmS1CwbF5IkSZIkqVk2LiRJkqQxetL7f3/SJUjSkmLjQpIkSZIkNcvGhSRJkiRJapaNC0mSJEmS1CwbF5IkSZIkqVk2LiRJkiRJUrOWVOMiyZ1JtiS5PMmlSf7HpGuSlgKzIw0kOSZJJTlw6NgRSS5K8q9dPj6U5NBubFOSG7r8TP/sObE3IE2Q+ZH6Mz/Swuwy6QLmaWtVrQNI8gvAq4D/OdGKpKXB7EgDG4BPAccBm5I8AHgP8Oyq+jRAkiOBBwNXdOecVlV/NolipcaYH6k/8yMtwJJacTHDfYBvTboIaQkyO1qRkqwGHgU8j8EvjgAvBN4x/UsjQFV9qqreP/4KpXaZH6k/8yMt3FJbcbFHki3AjwD7AI+dbDnSkmF2JHgacEFVXZPk1iSHAQ8B3rGT816c5Phu+1tV9ZhRFik16mmYH6mvp2F+pAVZaisutlbVuqo6EHgCcHaSDE9IckKSzUk2T01NTaZKqT07zQ6YHy17G4Bzuu1zuv27SPK5JFcnecPQ4dO6/Kzb0S+N5kfLnPmR+jM/0gIttcbFD1TVZ4D7A2tmHD+jqtZX1fo1a9Zs/2RpBZstO92Y+dGylGQvBiuN3prkOuAU4FnAlcBh0/Oq6pHAHwL3ne9rmB8tV+ZH6s/8SItjyTYuuifyrgJumXQt0lJidrRCPR04u6r2r6q1VfVA4Frgo8DGGd+0c8+JVCi1y/xI/ZkfaREs1WdcAAR4blXdOcF6pKXC7Gil2wC8esaxc4FnM/iXr1OT7At8E7gZ+JOhecP3GAM8raquG2GtUmvMj9Sf+ZEWwZJqXFTVqknXIC1FZkcrXVUdtZ1jbxza3e7XA1fVJmDTSIqSlgjzI/VnfqTFsWRvFZEkSZIkScufjQtJkiRJktQsGxeSJEmSJKlZNi4kSZIkSVKzbFxIkiRJkqRm2biQJEmSxujDT3vlpEuQpCXFxoUkSZIkSWqWjQtJkiRJktQsGxeSJEmSJKlZNi4kSZIkSVKzbFxIkiRJY/Tkc/960iVI0pJi40KSJEmSJDXLxoUkSZIkSWqWjQtJkiRJktQsGxeSJEmSJKlZNi4kSZIkSVKzJta4SHJMkkpy4NCxI5JclORfk1ya5ENJDu3GNiW5IcmWoZ89J1W/NEnmRxpYvXo1ANdddx3A4Ul+Z3osyelJNnbbZyW5NsnlSa5JcnaSfYfm3j583SQbk5zebR/QZWtLkquTnDHyNyY1KMleQ58hN834XHlAkv9O8vyh+fdO8pUkP9Xt75rkiiSPnNy7kCbD/EgLM8kVFxuATwHHASR5APAe4Per6qeq6jDgVcCDh845rarWDf18e9xFS40wP9LdbQNelGS3WcZPqaqHAQcAlwEX7mDusDfyw/wcBPzF4pQrLS1Vdcv0ZwjwFoY+V4Bjgc8y+Hyann8b8HLgTd2hk4FPV9Xnxlq41ADzIy3MRBoXSVYDjwKeR/cXL+CFwDuq6tPT86rqU1X1/vFXKLXL/Eiz2gZ8HHjujibVwGnATcAT53DdfYDrh86/YiFFSsvUBuAlwH7Dq5mq6j3A95O8FPhNBn8Rk3RX5kfaiUmtuHgacEFVXQPcmuQw4CHApTs578VDS6ouHHWRUqOehvmRZvNq4CVJVs1h7qXAgTudBacBn0hyfpIXe5uVdFdJHgjsXVUXM1j996wZU34XOBX406q6dczlSU0zP9LcTKpxsQE4p9s+h6FlUdOSfK67l/gNQ4eHl7o/ZnsXTnJCks1JNk9NTS1+5dLkmR9pFlV1LXAx8Ow5TM/OLtdd8+3AQcB7gaOAzybZ/W4XMz9auY5j8Bcu2P7n0hOAbwCHzHYB86MVzPxIczD2xkWSvYDHAm9Nch1wCoPO4pXAYdPzquqRwB8C953P9avqjKpaX1Xr16xZs2h1Sy0wP9KcvBJ4GTv/jHs4cHW3vXXG8y7uB9w8vVNVN1bVmVX1VAa3pNztF0jzoxVsA7Cx+1w6D3jY0AMFfxw4ETgCeFKSh27vAuZHK5j5keZgEisung6cXVX7V9XaqnogcC3wUQah/R9Dc+85gfqklpkfaSeq6ovAVcDR2xvPwIkMnl1xQXf4n4Dju/E9gGcCF3b7T0iya7e9N7AXcMMo34O0VCQ5ALhXVe3bfS6tZfBw6OlnMJ0GvLKqrgdOAt6UZGernaQVwfxIczeJxsUG4H0zjp3LYFnvs4BXJflykk8z+Eva6UPzhu/R35Jk7VgqltphfqS5eQWw34xjr01yOXAN8AjgMVV1Rzf2IuCXkmxh8GT391bVJ7uxnwe+0J37EQbfTnLTqN+AtETM9rm0IcnjgQcBbwOoqg8A3wJ+ZawVSu0yP9Ic7TLuF6yqo7Zz7I1Du/9zlvM2AZtGUpS0RJgf6a5uv/12ANauXQuDW6YAqKrLGWrOV9XGHV2nqm5glhUaVXUSg3/pktTpPldmG/s8cHC3+7EZY08ZYVnSkmB+pPmb1MM5JUmSJEmSdsrGhSRJkiRJapaNC0mSJEmS1CwbF5IkSZIkqVk2LiRJkiRJUrNsXEiSJElj9KFjf2PSJUjSkmLjQpIkSZIkNcvGhSRJkiRJapaNC0mSJEmS1CwbF5IkSZIkqVk2LiRJkqQx+sW/f9+kS5CWJLOzctm4kCRJkiRJzbJxIUmSJEmSmmXjQpIkSZIkNcvGhSRJkiRJapaNC0mSJEmS1KyJNC6S7JVkS/dzU5IbhvYfkOS/kzx/aP69k3wlyU91+7smuSLJIydRvzRJ5kfqz/xIP7R69WoArrvuOoDDk/zO9FiS05Ns7LbPSnJtksuTXJPk7CT7Ds29ffi6STYmOb3bPiDJRV3Grk5yxsjfmDQG5kcar4k0LqrqlqpaV1XrgLcApw3tHwt8FtgwNP824OXAm7pDJwOfrqrPjbVwqQHmR+rP/Eiz2ga8KMlus4yfUlUPAw4ALgMu3MHcYW/khzk7CPiLxSlXaor5kUasxVtFNgAvAfYb7kZW1XuA7yd5KfCbDH6RlHRX5kfqz/xoJdsGfBx47o4m1cBpwE3AE+dw3X2A64fOv2IhRUqNMj/SiDXVuEjyQGDvqroYeA/wrBlTfhc4FfjTqrp1zOVJTTM/Un/mRwLg1cBLkqyaw9xLgQPnMO804BNJzk/y4iR7LqRAqWHmRxqhphoXwHEMfmEEOIeh5bqdJwDfAA6Z7QJJTkiyOcnmqamp0VQptcn8SP2ZH614VXUtcDHw7DlMz84u113z7cBBwHuBo4DPJtn9bhczP1rizI80Wq01LjYAG5NcB5wHPGzogWg/DpwIHAE8KclDt3eBqjqjqtZX1fo1a9aMqWypCeZH6s/8SAOvBF7Gzn9HfDhwdbe9dcb9+vcDbp7eqaobq+rMqnoqgyX1d2sAmh8tE+ZHGpFmGhdJDgDuVVX7VtXaqloLvIrBv4LBYKnUK6vqeuAk4E1JdtatlFYE8yP1Z36kH6qqLwJXAUdvbzwDJzK49/6C7vA/Acd343sAzwQu7PafkGTXbntvYC/ghlG+B2lSzI80Os00Lhj8a9f7Zhw7F9iQ5PHAg4C3AVTVB4BvAb8y1gqldpkfqT/zI93VK4D9Zhx7bZLLgWuARwCPqao7urEXAb+UZAuDb+Z5b1V9shv7eeAL3bkfYfDtCjeN+g1IE2R+pBHYZdIFVNWmHYx9Hji42/3YjLGnjLAsaUkwP1J/5kcr3e233w7A2rVrAa6cPl5VlzP0j1tVtXFH16mqG5jlX5ir6iQGK5WkZcX8SOPV0ooLSZIkSZKku7BxIUmSJEmSmmXjQpIkSZIkNcvGhSRJkiRJapaNC0mSJEmS1CwbF5IkSdIYfeDpx0y6BGlJMjsrl40LSZIkSZLULBsXkiRJkiSpWTYuJEmSJElSs2xcSJIkSZKkZtm4WMKOPfdijj334kmXIS1J5kfqz/xI/ZkfqR+zs7LZuJAkSZIkSc2ycSFJkiRJkppl40KSJEmSJDXLxoUkSZIkSWqWjQtJkiRJktSssTQukuyd5JwkX0lyVZIPJ/npJFuTbOmOnZ1k127+UUk+2G1vTFJJHjd0vWO6Y08fR/3SJJkfqR+zI/VnfqT+zI+0+EbeuEgS4H3ARVX14Ko6GPh94AHAV6pqHXAosB/wzFkucwWwYWj/OODykRUtNcL8SP2YHak/8yP1Z36k0RjHiovHAP9dVW+ZPlBVW4B/G9q/E7gY2HeWa/wzcESSXZOsBn4S2DKqgqWGmB+pH7Mj9Wd+pP7MjzQC42hcHAJcsqMJSX4EeCRwwSxTCvg/wC8ATwXOW8wCpYaZH6kfsyP1Z36k/syPNAKTfjjng5NsAW4Bvl5Vn9/B3HMYLJM6DnjXbJOSnJBkc5LNU1NTi1qs1BjzI/Wz6NkB86MVw/xI/ZkfqadxNC6uBA6fZWz6Pq+fBH4myVNmu0hVXcygg3n/qrpmB/POqKr1VbV+zZo1CyhbaoL5kfoZa3a6ueZHy4X5kfozP9IIjKNx8Qlg9yS/MX0gySOA/af3q+obwO8BL9/JtV7O4OE20kphfqR+zI7Un/mR+jM/0giMvHFRVQUcAzy++0qgK4FNwI0zpr4fuGeSR+/gWudX1YWjqlVqjfmR+jE7Un/mR+rP/Eijscs4XqSqbmT7X/dzyNCcAh42NHZRd/ws4KztXHPjIpYoNcv8SP2YHak/8yP1Z36kxTfph3NKkiRJkiTNysaFJEmSJElqlo0LSZIkSZLULBsXkiRJkiSpWTYuJEmSJElSs8byrSIajXOPPWLSJUhLlvmR+jM/Un/mR+rH7KxsrriQJEmSJEnNsnEhSZIkSZKaZeNCkiRJkiQ1y8aFJEmSJElqlg/nbNifv++mOc076Zi9R1yJtPSYH6k/8yP1M9fsgPmR5mJ7mTI7K5MrLiRJkiRJUrNsXEiSJEmSpGbZuJAkSZIkSc2ycSFJkiRJkppl40KSJEmSJDXLxoUkSZIkSWrWojcukty+nWMHJLkoyZYkVyc5I8kvdPtbktye5Evd9tndOcckqSQHdvuf68a/nmRq6Ny1i/0epElZvXr13Y6ZH2nOHj7zgPmR5sz8SCPQ5eF1Q/snJ9k0tH9Cki92PxcnObI7flKStw3Ne06SD421eKkhu4zpdd4InFZV/wiQ5NCqugL4SLd/EXByVW0eOmcD8CngOGBTVT2ym7sRWF9VLxxT7dKkmR+pP/Mj9Wd+pIX7L+CXkryqqm4eHkhyNPB84MiqujnJYcD7kxzBIH+bkzwKuBL4U+BxY65dasa4bhXZB7h+eqf70JtVktXAo4DnMfjgk1Yy8yP1Z36k/syPtHDbgDOAF29n7GXAKdMNjaq6FHgH8IKq2gb8NvAm4DXAmVX11fGULLVnXI2L04BPJDk/yYuT7LmT+U8DLqiqa4Bbu+7jnHTLrTYn2Tw1NdW/Yqkd5kfqz/xI/ZkfaXG8CXhOkvvOOP4Q4JIZxzZ3x6mqTwNXAz/HoHmxXeZHK8FYGhdV9XbgIOC9wFHAZ5PsvoNTNgDndNvndPtzfa0zqmp9Va1fs2ZNz4qldpgfqT/zI/VnfqTFUVX/AZwNnDiH6QEKfrCKaT2wKzBrMMyPVoJxPeOCqroROBM4M8kXgEO4e4eRJHsBjwUOSVLAKqCSvLSqalz1Si0xP1J/5kfqz/xIi+b1wKXA24eOXQUcDnxi6Nhh3XGAPwbeCfw7gxVQzxh5lVKjxrLiIskTkuzabe8N7AXcMMv0pwNnV9X+VbW2qh4IXAscOY5apdaYH6k/8yP1Z36kxVNVtwLvYfAMmGmvAU7tGn8kWQdsBP4yyaHAk4FTGTwjY/8kjx9nzVJLRrHi4p5Jrh/a/3NgP+ANSf6zO3ZKVd00y/kbgFfPOHYu8Gzgnxe1Uqkx3/ve9wAeOpQh8yPN3T38/JF6Mz/S6L0O+ME361TVeUn2BT7drVS6DTgeuInBLVovrqr/BEjy28DZSdZV1R3jL12arEVvXFTVbKs4TtrBOUdtb3vo2BuHts8Czupbn9Sy73//+yT5fFWtnzFkfqSdu2Q72QHzI82F+ZFGoKpWD23/O3DPGeNvBt68nVOPnDFvM3DwKGqUloJxfauIJEmSJEnSvNm4kCRJkiRJzbJxIUmSJEmSmmXjQpIkSZIkNWsU3yqiRXLSMXtPugRpyTI/Un/mR+rH7EiLy0xpmisuJEmSJElSs2xcSJIkSZKkZtm4kCRJkiRJzbJxIUmSJEmSmuXDOSfoondOLcp1jjp+zaJcR1pKzI+0MIuRIfOjlWyhGTI/0sB8s2R2ViZXXEiSJEmSpGbZuJAkSZIkSc2ycSFJkiRJkppl40KSJEmSJDXLxoUkSZIkSWqWjQtJkiRJktSsnX4dapI7gSu6uVcDvwt8qBveG7gTmP4OmyOArUPzrwV+uaq+PXS9y4GrqmpDkl8FXtQNHQx8qbveBcAXgfVV9cLuvBOAk7q5/wGcVFWfmvc7lsZo1apVHHrooWzbto2DDjqI17/+9Tz5yU8G4KabbmLVqlWsWTP4SqeLL76YPfbYA+DgJF/A/GiFm29+gMOTbMHPH8n8SOP18JkHkhwA/BWwJ7A78M/AucCp3ZSfBG5g8Henz1fVryQ5BvgH4KCq+mKSz3Xn3g/Yo5sP8LSqum5k70Zq0E4bF8DWqloHkORvgWcN7W8Cbq+qP5uenGR4/juAFwCv6PYPYrDK42eT3Kuq3g68vRu7DnhMVd3c7W8cuubRwPOBI6vq5iSHAe9PckRV3dT3zUujtscee7BlyxYAnvOc5/Dud7/7B/ubNm1i9erVnHzyyXeZ/93vfveqqlpvfrTSzTc/wPf9/JEGzI80cW8ETquqfwRIcmhVXQF8pNu/CDi5qjYPnbMB+BRwHLCpqh7Zzd3IUENQWonme6vIPzPoDs7VZ4B9h/afDfwN8FHgKfO4zsuAU6Y/FKvqUmD6Q1VaEh796Efz5S9/eT6nmB+pY36k/syPNBH7ANdP73RNi1klWQ08Cngeg8aFpCFzblwk2QV4IoPbQOYyfxXwOOC8ocPPAt4NvItBR3GuHgJcMuPY5u74zNc9IcnmJJunpqZmDksTsW3bNs4//3wOPfTQOc03P9IPmR+pP/MjTcxpwCeSnJ/kxUn23Mn8pwEXVNU1wK3dCqU5MT9aCebSuNiju+dxM/B14G1znH8Lg/uxPgaQ5BHAVFV9Dfg4cFiSH+1ZN0CAmnmwqs6oqvVVtX763k1pUrZu3cq6detYv349D3rQg3je85630/kM7hc2P1rx5psf4B5+/kgD5kearO6WqoOA9wJHAZ9NsvsOTtkAnNNtn8M8moTmRyvBvJ5xMUdbq2pdkvsCH2SwHPCNDMJ3YHcvJMB9gGOBt87hmlcBhwOfGDp2WHdcatbwPcZznf/d7373Kgb/2mV+tKLNNz909+j7+SOZH6kFVXUjcCZwZvfg9UO4+yokkuwFPBY4JEkBq4BK8tKqulujT1qJRvZ1qFX1HeBE4OSuu/gM4KFVtbaq1gJPZe6dxNcAp3ahJsk6YCPwl4tcttQE8yP1Z36k/syPtDiSPCHJrt323sBe/PBbQWZ6OnB2Ve3fZe2BDL7d58jxVCu1by4rLnqrqsu6r896JnBDVQ2H9ZMMvvZxn6r6xk6uc16SfYFPd13I24Djd3aetJSZH6k/8yP1Z36kebtHkuuH9v8c2A94Q5L/7I6dsoNv09kAvHrGsXMZPBj3nxe1UmmJynJefbR+/fravHnzzidOyEXvXJyH5xx1vPeyLSdJLqmq9ZOuw/xoqWklO9B+fmBxMmR+lg/zM38LzZD5WT7Mz8LMN0tmZ3mZa35GdquIJEmSJEnSQtm4kCRJkiRJzbJxIUmSJEmSmmXjQpIkSZIkNWuk3yqiHfPBMlJ/5kdaGDMkLYwZkhaHWdJcuOJCkiRJkiQ1y8aFJEmSJElqlo0LSZIkSZLULBsXkiRJkiSpWT6ccwS+8ZpvjPX19nnpPmN9PWmUzI+0MOPMkPnRcjbqLJkfrVQLzZbZWZlccSFJkiRJkppl40KSJEmSJDXLxoUkSZIkSWqWjQtJkiRJktQsGxeSJEmSJKlZNi4kSZIkSVKzmmhcJLkzyZYkVya5PMlJSe7RjR2V5IPd9gOSfLCbc1WSD0+2cmmyzI7Un/mR+jM/Wq5WrVrFunXrOOSQQ3jGM57BDTfcwLp161i3bh177703++677w/277jjDlatWgVwcJIvJPlAkj2Hr9f92X9Xt/2rXW62JLkjyRXd9quTbExy+tB5JyT5YvdzcZIjx/ofQmrMLpMuoLO1qtYBJPkx4O+A+wJ/NGPenwAfq6o3dHMfOs4ipQaZHak/8yP1Z360LO2xxx5s2bIFgOc85zm8+93v/sH+pk2bWL16NSeffPJd5n/3u9+9qqrWJ3kH8ALgFQBJDmLwD8U/m+ReVfV24O3d2HXAY6rq5m5/4/Q1kxwNPB84sqpuTnIY8P4kR1TVTaN8/1KrmlhxMayqvgmcALwwSWYM7wNcPzT38+OsTWqZ2ZH6Mz9Sf+ZHy9WjH/1ovvzlL8/nlM8A+w7tPxv4G+CjwFPmcZ2XAadMNzWq6lJguikirUjNNS4AquqrDGr7sRlDbwLeluTCJH+Q5Mdnntstq9qcZPPU1NQ4ypWasZDsgPnRymZ+pP7Mj5abbdu2cf7553PooYfOaX6SVcDjgPOGDj8LeDfwLmDDPF7+IcAlM45t7o5v77XNj5a9JhsXnZkde6rqI8BPAH8NHAhclmTNjDlnVNX6qlq/Zs2amZeQVoJe2enmmR+tdOZH6s/8aMnbunUr69atY/369TzoQQ/iec973k7nAwcDtwD3Az4GkOQRwFRVfQ34OHBYkh9dQGkBansD5kcrQZONiyQ/AdwJfHPmWFXdWlV/V1W/DPwL8LPjrk9qldmR+jM/Un/mR8vF9DMutmzZwl/8xV+w22677XQ+cBWwP7AbP7ydYwNwYPcsi68A9wGOnWMZVwGHzzh2WHdcWpGaa1x0Xfi3AKdXVc0Ye2ySe3bb9wYeDHx9/FVK7TE7Un/mR+rP/EhQVd8BTgROTrI78AzgoVW1tqrWAk9l7reLvAY4NcleAEnWARuBv1zksqUlo5VvFdkjyRZgV2Abg4fY/Pl25h0OnJ5kG4Omy1ur6l/GVqXUHrMj9Wd+pP7MjzRDVV2W5HLgmcANVXXD0PAnGXxt6j5V9Y2dXOe8JPsCn05SwG3A8Ts7T1rOmmhcVNWqHYxdBFzUbb8WeO14qpLaZ3ak/syP1J/50XJ1++23zzq2adOm7c4f/jKdqvrFbvNvhudV1Z0MvmVnen/tjPGzgLOG9t8MvHnOhUvLXHO3ikiSJEmSJE2zcSFJkiRJkppl40KSJEmSJDXLxoUkSZIkSWpWEw/nXG72eek+O58kabvMj7QwZkhaHGZJGg2zpT5ccSFJkiRJkppl40KSJEmSJDXLxoUkSZIkSWqWjQtJkiRJktSsZf1wzv/+5u38+xs/NekyRu4BJx456RK0DJkfqT/zI43PUs2a+dFyN6psmp2VyRUXkiRJkiSpWTYuJEmSJElSs2xcSJIkSZKkZtm4kCRJkiRJzbJxIUmSJEmSmmXjQpIkSZIkNWvsjYskleR1Q/snJ9k0tH9Cki92PxcnObI7flKStw3Ne06SD421eGnCzI/Un/mR+jE70uJKcmeSLUmuTHJ5l5V7dGNHJflgt/2AJB/s5lyV5MOTrVyanEmsuPgv4JeS3H/mQJKjgecDR1bVgcBvAn+XZG/gjcDhSR6VZE/gT4HfGV/ZUhPMj9Sf+ZH6MTvS4tpaVeuq6iHA44EnAX+0nXl/Anysqh5WVQcDvzfOIqWWTKJxsQ04A3jxdsZeBpxSVTcDVNWlwDuAF1TVNuC3gTcBrwHOrKqvjqdkqRnmR+rP/Ej9mB1pRKrqm8AJwAuTZMbwPsD1Q3M/P87apJZM6hkXbwKek+S+M44/BLhkxrHN3XGq6tPA1cDPMfgAvJtuueLmJJtvvf3bi1q01AjzI/VnfqR+RpYduGt+pqamFq9qaQnoGnr3AH5sxtCbgLcluTDJHyT58e2db360EkykcVFV/wGcDZw4h+kBCiDJamA9sCuwZpZrn1FV66tq/f1W77k4BUsNMT9Sf+ZH6meU2emu/4P8rFkz6zRpOZu52oKq+gjwE8BfAwcClyW5W0DMj1aCSX6ryOuB5wH3Gjp2FXD4jHmHdccB/hh4J/AK4LQR1ye17PWYH6mv12N+pD5ej9mRFl2SnwDuBL45c6yqbq2qv6uqXwb+BfjZcdcntWBijYuquhV4D4MPwGmvAU5NshdAknXARuAvkxwKPBk4lcF9lvsnefw4a5ZaYX6k/syP1I/ZkRZft4LiLcDpVVUzxh6b5J7d9r2BBwNfH3+V0uTtMuHXfx3wwumdqjovyb7Ap5MUcBtwPHAT8F7gxVX1nwBJfhs4O8m6qrpj/KVLE2d+pP7Mj9SP2ZEWbo8kWxjcQrUN+Bvgz7cz73Dg9CTbGPyD81ur6l/GVqXUkLE3Lqpq9dD2vwP3nDH+ZuDN2zn1yBnzNgMHj6JGqVXmR+rP/Ej9mB1pcVXVqh2MXQRc1G2/FnjteKqS2jbJZ1xIkiRJkiTtkI0LSZIkSZLULBsXkiRJkiSpWTYuJEmSJElSsyb9rSIjteuPreYBJx6584mS7sb8SP2ZH2l8zJrUJrOpxeSKC0mSJEmS1CwbF5IkSZIkqVmpqknXMDJJpoCvLeIl7w/cvIjXWyyt1gXt1tZqXQAHVNW9J13EIuen5f/erdbWal3Qbm1NZAfMTwNarQvarc38jF+rtbVaF7Rbm/kZr1brgnZra7UumGN+lvUzLqpqzWJeL8nmqlq/mNdcDK3WBe3W1mpdMKht0jXA4uan9f/eLdbWal3Qbm2tZAfMz6S1Whe0W5v5Gb9Wa2u1Lmi3NvMzXq3WBe3W1mpdMPf8eKuIJEmSJElqlo0LSZIkSZLULBsX83PGpAuYRat1Qbu1tVoXtF1bXy2/p1Zra7UuaLe2VutaqJbfV6u1tVoXtFtbq3UtVMvvq9XaWq0L2q2t1boWqtX31Wpd0G5trdYFc6xtWT+cU5IkSZIkLW2uuJAkSZIkSc2ycTEPSV6b5ItJPp/kfUn2bKCmJyT5UpIvJ/m9SdcDkOSBSS5McnWSK5O8aNI1DUuyKsllST446VqGJdkzyd93f8auTvL/TrqmxdRaflrMDpifvszP2OsxPz2Yn8kwP3NjfvoxP2Ovp7n8tJ4daDM/882OjYv5+RhwSFU9FLgGePkki0myCngT8ETgYGBDkoMnWVNnG/CSqjoI+BngBY3UNe1FwNWTLmI73gBcUFUHAg+jzRoXopn8NJwdMD99mZ8xMT8LYn4mw/zMjfnpx/yMScP5aT070GZ+5pUdGxfzUFUfrapt3e5ngf0mWQ9wBPDlqvpqVd0BnAM8dcI1UVXfqKpLu+3bGPwh3HeyVQ0k2Q94MvDWSdcyLMl9gJ8F3gZQVXdU1bcnWtQiayw/TWYHzE8f5mfszE8P5mdyzM/cmJ/5Mz9j12R+Ws4OtJmfPtmxcdHfrwHnT7iGfYF/G9q/noZCApBkLfBw4HMTLmXa64GXAt+fcB0z/QQwBby9W8b11iT3mnRRIzTp/DSfHTA/82B+xsv89PN6zE8LzM8cmJ85Mz/j1Xx+GswOtJmfeWfHxsUMSf5Pki9s5+epQ3P+gMGSoL+dXKWDUrZzrJmviUmyGjgX+N2q+o8G6jka+GZVXTLpWrZjF+Aw4M1V9XDgu0AT9+3NxxLKT9PZAfMzT+ZnvMzP/OsxPyNmfhaP+ZkX8zNeTeentexA0/mZd3Z2GUdVS0lV/dyOxpM8FzgaeFxN/rtkrwceOLS/H3DjhGq5iyS7Mgju31bVP0y6ns6jgKckeRLwI8B9kryzqo6fcF0w+P/l9VU13Z39e5bgB98Syk+z2QHz04P5GS/zM3/mZ8TMz+IwP/Nmfsar2fw0mh1oNz/zzo4rLuYhyROAlwFPqarvTboe4F+An0ry/yTZDTgOOG/CNZEkDO5Xurqq/nzS9UyrqpdX1X5VtZbBf6tPNBBaAKrqJuDfkhzQHXoccNUES1p0jeWnyeyA+enD/Iyd+Zkn8zNZ5mduzM/8mZ+xazI/rWYH2s1Pn+y44mJ+Tgd2Bz42+PPJZ6vqNydVTFVtS/JC4CPAKuDMqrpyUvUMeRTwy8AVSbZ0x36/qj48uZKWhN8B/rb7H+KvAr864XoWWzP5aTg7YH76Mj9jYn6WJfMzJuZnWTI/Y9JwfsxOP/PKTiZ/t4MkSZIkSdL2eauIJEmSJElqlo0LSZIkSZLULBsXkiRJkiSpWTYuJEmSJElSs2xcSJIkSZKkZtm4kCRJkiRJzbJxIUmSJEmSmmXjQpIkSZIkNev/ApKxCBKKVsByAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 각 alpha에 따른 회귀 계수 값을 시각화하기 위해 5개의 열로 된 맷플롯립 축 생성  \n",
    "fig , axs = plt.subplots(figsize=(18,6) , nrows=1 , ncols=5)\n",
    "# 각 alpha에 따른 회귀 계수 값을 데이터로 저장하기 위한 DataFrame 생성  \n",
    "coeff_df = pd.DataFrame()\n",
    "\n",
    "# alphas 리스트 값을 차례로 입력해 회귀 계수 값 시각화 및 데이터 저장. pos는 axis의 위치 지정\n",
    "for pos , alpha in enumerate(alphas) :\n",
    "    ridge = Ridge(alpha = alpha)\n",
    "    ridge.fit(X_data , y_target)\n",
    "    # alpha에 따른 피처별 회귀 계수를 Series로 변환하고 이를 DataFrame의 컬럼으로 추가.  \n",
    "    coeff = pd.Series(data=ridge.coef_ , index=X_data.columns )\n",
    "    colname='alpha:'+str(alpha)\n",
    "    coeff_df[colname] = coeff\n",
    "    # 막대 그래프로 각 alpha 값에서의 회귀 계수를 시각화. 회귀 계수값이 높은 순으로 표현\n",
    "    coeff = coeff.sort_values(ascending=False)\n",
    "    axs[pos].set_title(colname)\n",
    "    axs[pos].set_xlim(-3,6)\n",
    "    sns.barplot(x=coeff.values , y=coeff.index, ax=axs[pos])\n",
    "\n",
    "# for 문 바깥에서 맷플롯립의 show 호출 및 alpha에 따른 피처별 회귀 계수를 DataFrame으로 표시\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>alpha:0</th>\n",
       "      <th>alpha:0.1</th>\n",
       "      <th>alpha:1</th>\n",
       "      <th>alpha:10</th>\n",
       "      <th>alpha:100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>3.809865</td>\n",
       "      <td>3.818233</td>\n",
       "      <td>3.854000</td>\n",
       "      <td>3.702272</td>\n",
       "      <td>2.334536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>2.686734</td>\n",
       "      <td>2.670019</td>\n",
       "      <td>2.552393</td>\n",
       "      <td>1.952021</td>\n",
       "      <td>0.638335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>0.306049</td>\n",
       "      <td>0.303515</td>\n",
       "      <td>0.290142</td>\n",
       "      <td>0.279596</td>\n",
       "      <td>0.315358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>0.046420</td>\n",
       "      <td>0.046572</td>\n",
       "      <td>0.047443</td>\n",
       "      <td>0.049579</td>\n",
       "      <td>0.054496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>0.020559</td>\n",
       "      <td>0.015999</td>\n",
       "      <td>-0.008805</td>\n",
       "      <td>-0.042962</td>\n",
       "      <td>-0.052826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.009312</td>\n",
       "      <td>0.009368</td>\n",
       "      <td>0.009673</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.009393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.000692</td>\n",
       "      <td>-0.000269</td>\n",
       "      <td>-0.005415</td>\n",
       "      <td>-0.010707</td>\n",
       "      <td>0.001212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>-0.012335</td>\n",
       "      <td>-0.012421</td>\n",
       "      <td>-0.012912</td>\n",
       "      <td>-0.013993</td>\n",
       "      <td>-0.015856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>-0.108011</td>\n",
       "      <td>-0.107474</td>\n",
       "      <td>-0.104595</td>\n",
       "      <td>-0.101435</td>\n",
       "      <td>-0.102202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>-0.524758</td>\n",
       "      <td>-0.525966</td>\n",
       "      <td>-0.533343</td>\n",
       "      <td>-0.559366</td>\n",
       "      <td>-0.660764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>-0.952747</td>\n",
       "      <td>-0.940759</td>\n",
       "      <td>-0.876074</td>\n",
       "      <td>-0.797945</td>\n",
       "      <td>-0.829218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>-1.475567</td>\n",
       "      <td>-1.459626</td>\n",
       "      <td>-1.372654</td>\n",
       "      <td>-1.248808</td>\n",
       "      <td>-1.153390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>-17.766611</td>\n",
       "      <td>-16.684645</td>\n",
       "      <td>-10.777015</td>\n",
       "      <td>-2.371619</td>\n",
       "      <td>-0.262847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           alpha:0  alpha:0.1    alpha:1  alpha:10  alpha:100\n",
       "RM        3.809865   3.818233   3.854000  3.702272   2.334536\n",
       "CHAS      2.686734   2.670019   2.552393  1.952021   0.638335\n",
       "RAD       0.306049   0.303515   0.290142  0.279596   0.315358\n",
       "ZN        0.046420   0.046572   0.047443  0.049579   0.054496\n",
       "INDUS     0.020559   0.015999  -0.008805 -0.042962  -0.052826\n",
       "B         0.009312   0.009368   0.009673  0.010037   0.009393\n",
       "AGE       0.000692  -0.000269  -0.005415 -0.010707   0.001212\n",
       "TAX      -0.012335  -0.012421  -0.012912 -0.013993  -0.015856\n",
       "CRIM     -0.108011  -0.107474  -0.104595 -0.101435  -0.102202\n",
       "LSTAT    -0.524758  -0.525966  -0.533343 -0.559366  -0.660764\n",
       "PTRATIO  -0.952747  -0.940759  -0.876074 -0.797945  -0.829218\n",
       "DIS      -1.475567  -1.459626  -1.372654 -1.248808  -1.153390\n",
       "NOX     -17.766611 -16.684645 -10.777015 -2.371619  -0.262847"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_alphas = [0, 0.1, 1, 10, 100]\n",
    "sort_column = 'alpha:'+str(ridge_alphas[0])\n",
    "coeff_df.sort_values(by=sort_column, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#라쏘(Lasso) 회귀\n",
    "from sklearn.linear_model import Lasso, ElasticNet\n",
    "\n",
    "#alpha값에 따른 회귀 모델의 폴드 평균 RMSE를 출력하고 회귀 계수값들을 DataFrame으로 반환\n",
    "def get_linear_reg_eval(model_name, params=None, X_data_n=None, y_target_n=None,\n",
    "                       verbose=True, return_coeff=True):\n",
    "    coeff_df = pd.DataFrame()\n",
    "    if verbose : print('####### ', model_name , '#######')\n",
    "    for param in params:\n",
    "        if model_name == 'Ridge': model = Ridge(alpha=param)\n",
    "        elif model_name == 'Lasso': model = Lasso(alpha=param)\n",
    "        elif model_name == 'ElasticNet': model = ElasticNet(alpha=param, l1_ratio = 0.7)\n",
    "        neg_mse_scores = cross_val_score(model, X_data_n,\n",
    "                                            y_target_n, scoring='neg_mean_squared_error', cv=5)\n",
    "        avg_rmse = np.mean(np.sqrt(-1 * neg_mse_scores))\n",
    "        print('alpha {0}일 때 5 폴드 세트의 평균 RMSE: {1:.3f} '.format(param, avg_rmse))\n",
    "        # cross_val_score는 evaluation metric만 반환하므로 모델을 다시 학습하여 회귀 계수 추출\n",
    "            \n",
    "        model.fit(X_data_n, y_target_n)\n",
    "        if return_coeff:\n",
    "            # alpha에 따른 피처별 회귀 계수를 Series로 변환하고 이를 DataFrame의 컬럼으로 추가\n",
    "            coeff = pd.Series(data=model.coef_, index=X_data_n.columns)\n",
    "            colname = 'alpha:'+str(param)\n",
    "            coeff_df[colname] = coeff\n",
    "    \n",
    "    return coeff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#######  Lasso #######\n",
      "alpha 0.07일 때 5 폴드 세트의 평균 RMSE: 5.612 \n",
      "alpha 0.1일 때 5 폴드 세트의 평균 RMSE: 5.615 \n",
      "alpha 0.5일 때 5 폴드 세트의 평균 RMSE: 5.669 \n",
      "alpha 1일 때 5 폴드 세트의 평균 RMSE: 5.776 \n",
      "alpha 3일 때 5 폴드 세트의 평균 RMSE: 6.189 \n"
     ]
    }
   ],
   "source": [
    "# Lasso에 사용될 alpha 파라미터의 값들을 정의하고 get_linear_reg_eval() 함수 호출\n",
    "lasso_alphas = [0.07, 0.1, 0.5, 1, 3]\n",
    "coeff_lasso_df = get_linear_reg_eval('Lasso', params=lasso_alphas, X_data_n=X_data, y_target_n=y_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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