鮑魚數(shù)據(jù)集案例實戰(zhàn)）

• 數(shù)據(jù)集探索性分析
• 鮑魚數(shù)據(jù)預處理
• 對sex特征進行OneHot編碼，便于后續(xù)模型納入啞變量
• 篩選特征
• 將鮑魚數(shù)據(jù)集劃分為訓練集和測試集
• 實現(xiàn)線性回歸和嶺回歸
• 使用numpy實現(xiàn)線性回歸
• 使用sklearn實現(xiàn)線性回歸
• 使用Numpy實現(xiàn)嶺回歸
• 利用sklearn實現(xiàn)嶺回歸
• 嶺跡分析
• 使用LASSO構(gòu)建鮑魚年齡預測模型
• LASSO的正則化路徑
• 殘差圖

數(shù)據(jù)集探索性分析

import pandas as pd import warnings warnings.filterwarnings('ignore') data=pd.read_csv(r"E:\大二下\機器學習實踐\abalone_dataset.csv") data.head() sexlengthdiameterheightwhole weightshucked weightviscera weightshell weightrings01234

M	0.455	0.365	0.095	0.5140	0.2245	0.1010	0.150	15
M	0.350	0.265	0.090	0.2255	0.0995	0.0485	0.070	7
F	0.530	0.420	0.135	0.6770	0.2565	0.1415	0.210	9
M	0.440	0.365	0.125	0.5160	0.2155	0.1140	0.155	10
I	0.330	0.255	0.080	0.2050	0.0895	0.0395	0.055	7

#查看數(shù)據(jù)集中樣本數(shù)量和特征數(shù)量 data.shape (4177, 9) #查看數(shù)據(jù)信息，檢查是否有缺失值 data.info() <class 'pandas.core.frame.DataFrame'> RangeIndex: 4177 entries, 0 to 4176 Data columns (total 9 columns): sex 4177 non-null object length 4177 non-null float64 diameter 4177 non-null float64 height 4177 non-null float64 whole weight 4177 non-null float64 shucked weight 4177 non-null float64 viscera weight 4177 non-null float64 shell weight 4177 non-null float64 rings 4177 non-null int64 dtypes: float64(7), int64(1), object(1) memory usage: 293.8+ KB data.describe() lengthdiameterheightwhole weightshucked weightviscera weightshell weightringscountmeanstdmin25%50%75%max

4177.000000	4177.000000	4177.000000	4177.000000	4177.000000	4177.000000	4177.000000	4177.000000
0.523992	0.407881	0.139516	0.828742	0.359367	0.180594	0.238831	9.933684
0.120093	0.099240	0.041827	0.490389	0.221963	0.109614	0.139203	3.224169
0.075000	0.055000	0.000000	0.002000	0.001000	0.000500	0.001500	1.000000
0.450000	0.350000	0.115000	0.441500	0.186000	0.093500	0.130000	8.000000
0.545000	0.425000	0.140000	0.799500	0.336000	0.171000	0.234000	9.000000
0.615000	0.480000	0.165000	1.153000	0.502000	0.253000	0.329000	11.000000
0.815000	0.650000	1.130000	2.825500	1.488000	0.760000	1.005000	29.000000

#觀察sex列的取值的分布情況 import seaborn as sns import matplotlib.pyplot as plt %matplotlib inlinesns.countplot(x = "sex",data=data) <matplotlib.axes._subplots.AxesSubplot at 0x27f16455080>

data['sex'].value_counts() M 1528 I 1342 F 1307 Name: sex, dtype: int64 i=1 #子圖計數(shù) plt.figure(figsize=(16,8)) for col in data.columns[1:]:plt.subplot(4,2,i)i = i + 1sns.distplot(data[col]) plt.tight_layout()

sns.pairplot(data,hue="sex") <seaborn.axisgrid.PairGrid at 0x27f16d16eb8>

corr_df = data.corr() corr_df lengthdiameterheightwhole weightshucked weightviscera weightshell weightringslengthdiameterheightwhole weightshucked weightviscera weightshell weightrings

1.000000	0.986812	0.827554	0.925261	0.897914	0.903018	0.897706	0.556720
0.986812	1.000000	0.833684	0.925452	0.893162	0.899724	0.905330	0.574660
0.827554	0.833684	1.000000	0.819221	0.774972	0.798319	0.817338	0.557467
0.925261	0.925452	0.819221	1.000000	0.969405	0.966375	0.955355	0.540390
0.897914	0.893162	0.774972	0.969405	1.000000	0.931961	0.882617	0.420884
0.903018	0.899724	0.798319	0.966375	0.931961	1.000000	0.907656	0.503819
0.897706	0.905330	0.817338	0.955355	0.882617	0.907656	1.000000	0.627574
0.556720	0.574660	0.557467	0.540390	0.420884	0.503819	0.627574	1.000000

fig ,ax =plt.subplots(figsize=(12,12)) ##繪制熱力圖 ax = sns.heatmap(corr_df,linewidths=.5,cmap="Greens",annot=True,xticklabels=corr_df.columns,yticklabels=corr_df.index) ax.xaxis.set_label_position('top') ax.xaxis.tick_top()

鮑魚數(shù)據(jù)預處理

對sex特征進行OneHot編碼，便于后續(xù)模型納入啞變量

#只用pandas的get_dummies函數(shù)對sex特征做OneHot編碼處理 sex_onehot = pd.get_dummies(data["sex"],prefix="sex") data[sex_onehot.columns] = sex_onehot data.head() sexlengthdiameterheightwhole weightshucked weightviscera weightshell weightringssex_Fsex_Isex_M01234

M	0.455	0.365	0.095	0.5140	0.2245	0.1010	0.150	15	0	0	1
M	0.350	0.265	0.090	0.2255	0.0995	0.0485	0.070	7	0	0	1
F	0.530	0.420	0.135	0.6770	0.2565	0.1415	0.210	9	1	0	0
M	0.440	0.365	0.125	0.5160	0.2155	0.1140	0.155	10	0	0	1
I	0.330	0.255	0.080	0.2050	0.0895	0.0395	0.055	7	0	1	0

data["ones"]=1 data.head() sexlengthdiameterheightwhole weightshucked weightviscera weightshell weightringssex_Fsex_Isex_Mones01234

M	0.455	0.365	0.095	0.5140	0.2245	0.1010	0.150	15	0	0	1	1
M	0.350	0.265	0.090	0.2255	0.0995	0.0485	0.070	7	0	0	1	1
F	0.530	0.420	0.135	0.6770	0.2565	0.1415	0.210	9	1	0	0	1
M	0.440	0.365	0.125	0.5160	0.2155	0.1140	0.155	10	0	0	1	1
I	0.330	0.255	0.080	0.2050	0.0895	0.0395	0.055	7	0	1	0	1

data["age"]=data["rings"] + 1.5 data.head() sexlengthdiameterheightwhole weightshucked weightviscera weightshell weightringssex_Fsex_Isex_Monesage01234

M	0.455	0.365	0.095	0.5140	0.2245	0.1010	0.150	15	0	0	1	1	16.5
M	0.350	0.265	0.090	0.2255	0.0995	0.0485	0.070	7	0	0	1	1	8.5
F	0.530	0.420	0.135	0.6770	0.2565	0.1415	0.210	9	1	0	0	1	10.5
M	0.440	0.365	0.125	0.5160	0.2155	0.1140	0.155	10	0	0	1	1	11.5
I	0.330	0.255	0.080	0.2050	0.0895	0.0395	0.055	7	0	1	0	1	8.5

篩選特征

data.columns Index(['sex', 'length', 'diameter', 'height', 'whole weight', 'shucked weight','viscera weight', 'shell weight', 'rings', 'sex_F', 'sex_I', 'sex_M','ones', 'age'],dtype='object') y = data["age"] #因變量 features_with_ones = ["length", "diameter", "height", "whole weight", "shucked weight","viscera weight", "shell weight", "sex_F", "sex_M","ones"] features_without_ones = ["length", "diameter", "height", "whole weight", "shucked weight","viscera weight", "shell weight", "sex_F", "sex_M"] X=data[features_with_ones]

將鮑魚數(shù)據(jù)集劃分為訓練集和測試集

#拆分訓練集和測試集 from sklearn.model_selection import train_test_splitX_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=111) X lengthdiameterheightwhole weightshucked weightviscera weightshell weightsex_Fsex_Mones01234567891011121314151617181920212223242526272829...414741484149415041514152415341544155415641574158415941604161416241634164416541664167416841694170417141724173417441754176

0.455	0.365	0.095	0.5140	0.2245	0.1010	0.1500	0	1	1
0.350	0.265	0.090	0.2255	0.0995	0.0485	0.0700	0	1	1
0.530	0.420	0.135	0.6770	0.2565	0.1415	0.2100	1	0	1
0.440	0.365	0.125	0.5160	0.2155	0.1140	0.1550	0	1	1
0.330	0.255	0.080	0.2050	0.0895	0.0395	0.0550	0	0	1
0.425	0.300	0.095	0.3515	0.1410	0.0775	0.1200	0	0	1
0.530	0.415	0.150	0.7775	0.2370	0.1415	0.3300	1	0	1
0.545	0.425	0.125	0.7680	0.2940	0.1495	0.2600	1	0	1
0.475	0.370	0.125	0.5095	0.2165	0.1125	0.1650	0	1	1
0.550	0.440	0.150	0.8945	0.3145	0.1510	0.3200	1	0	1
0.525	0.380	0.140	0.6065	0.1940	0.1475	0.2100	1	0	1
0.430	0.350	0.110	0.4060	0.1675	0.0810	0.1350	0	1	1
0.490	0.380	0.135	0.5415	0.2175	0.0950	0.1900	0	1	1
0.535	0.405	0.145	0.6845	0.2725	0.1710	0.2050	1	0	1
0.470	0.355	0.100	0.4755	0.1675	0.0805	0.1850	1	0	1
0.500	0.400	0.130	0.6645	0.2580	0.1330	0.2400	0	1	1
0.355	0.280	0.085	0.2905	0.0950	0.0395	0.1150	0	0	1
0.440	0.340	0.100	0.4510	0.1880	0.0870	0.1300	1	0	1
0.365	0.295	0.080	0.2555	0.0970	0.0430	0.1000	0	1	1
0.450	0.320	0.100	0.3810	0.1705	0.0750	0.1150	0	1	1
0.355	0.280	0.095	0.2455	0.0955	0.0620	0.0750	0	1	1
0.380	0.275	0.100	0.2255	0.0800	0.0490	0.0850	0	0	1
0.565	0.440	0.155	0.9395	0.4275	0.2140	0.2700	1	0	1
0.550	0.415	0.135	0.7635	0.3180	0.2100	0.2000	1	0	1
0.615	0.480	0.165	1.1615	0.5130	0.3010	0.3050	1	0	1
0.560	0.440	0.140	0.9285	0.3825	0.1880	0.3000	1	0	1
0.580	0.450	0.185	0.9955	0.3945	0.2720	0.2850	1	0	1
0.590	0.445	0.140	0.9310	0.3560	0.2340	0.2800	0	1	1
0.605	0.475	0.180	0.9365	0.3940	0.2190	0.2950	0	1	1
0.575	0.425	0.140	0.8635	0.3930	0.2270	0.2000	0	1	1
...	...	...	...	...	...	...	...	...	...
0.695	0.550	0.195	1.6645	0.7270	0.3600	0.4450	0	1	1
0.770	0.605	0.175	2.0505	0.8005	0.5260	0.3550	0	1	1
0.280	0.215	0.070	0.1240	0.0630	0.0215	0.0300	0	0	1
0.330	0.230	0.080	0.1400	0.0565	0.0365	0.0460	0	0	1
0.350	0.250	0.075	0.1695	0.0835	0.0355	0.0410	0	0	1
0.370	0.280	0.090	0.2180	0.0995	0.0545	0.0615	0	0	1
0.430	0.315	0.115	0.3840	0.1885	0.0715	0.1100	0	0	1
0.435	0.330	0.095	0.3930	0.2190	0.0750	0.0885	0	0	1
0.440	0.350	0.110	0.3805	0.1575	0.0895	0.1150	0	0	1
0.475	0.370	0.110	0.4895	0.2185	0.1070	0.1460	0	1	1
0.475	0.360	0.140	0.5135	0.2410	0.1045	0.1550	0	1	1
0.480	0.355	0.110	0.4495	0.2010	0.0890	0.1400	0	0	1
0.560	0.440	0.135	0.8025	0.3500	0.1615	0.2590	1	0	1
0.585	0.475	0.165	1.0530	0.4580	0.2170	0.3000	1	0	1
0.585	0.455	0.170	0.9945	0.4255	0.2630	0.2845	1	0	1
0.385	0.255	0.100	0.3175	0.1370	0.0680	0.0920	0	1	1
0.390	0.310	0.085	0.3440	0.1810	0.0695	0.0790	0	0	1
0.390	0.290	0.100	0.2845	0.1255	0.0635	0.0810	0	0	1
0.405	0.300	0.085	0.3035	0.1500	0.0505	0.0880	0	0	1
0.475	0.365	0.115	0.4990	0.2320	0.0885	0.1560	0	0	1
0.500	0.380	0.125	0.5770	0.2690	0.1265	0.1535	0	1	1
0.515	0.400	0.125	0.6150	0.2865	0.1230	0.1765	1	0	1
0.520	0.385	0.165	0.7910	0.3750	0.1800	0.1815	0	1	1
0.550	0.430	0.130	0.8395	0.3155	0.1955	0.2405	0	1	1
0.560	0.430	0.155	0.8675	0.4000	0.1720	0.2290	0	1	1
0.565	0.450	0.165	0.8870	0.3700	0.2390	0.2490	1	0	1
0.590	0.440	0.135	0.9660	0.4390	0.2145	0.2605	0	1	1
0.600	0.475	0.205	1.1760	0.5255	0.2875	0.3080	0	1	1
0.625	0.485	0.150	1.0945	0.5310	0.2610	0.2960	1	0	1
0.710	0.555	0.195	1.9485	0.9455	0.3765	0.4950	0	1	1

4177 rows × 10 columns

實現(xiàn)線性回歸和嶺回歸

使用numpy實現(xiàn)線性回歸

import numpy as np def linear_regression(X,y):w = np.zeros_like(X.shape[1])if np.linalg.det(X.T.dot(X)) != 0:w = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)return w #使用上述實現(xiàn)的線性回歸模型在鮑魚訓練集上訓練模型 w1 = linear_regression(X_train,y_train) w1 = pd.DataFrame(data = w1,index=X.columns,columns =["numpy_w"]) w1.round(decimals=2) numpy_wlengthdiameterheightwhole weightshucked weightviscera weightshell weightsex_Fsex_Mones

-1.12
10.00
20.74
9.61
-20.05
-12.07
6.55
0.88
0.87
4.32

使用sklearn實現(xiàn)線性回歸

from sklearn.linear_model import LinearRegression lr = LinearRegression() lr.fit(X_train[features_without_ones],y_train) print(lr.coef_) [ -1.118146 10.00094599 20.73712616 9.61484657 -20.05079291-12.06849193 6.54529076 0.87855188 0.87283083] w1 numpy_wlengthdiameterheightwhole weightshucked weightviscera weightshell weightsex_Fsex_Mones

-1.118146
10.000946
20.737126
9.614847
-20.050793
-12.068492
6.545291
0.878552
0.872831
4.324477

w_lr=[] w_lr.extend(lr.coef_) w_lr.append(lr.intercept_) w1["lr_sklearn_w"]=w_lr w1.round(decimals=2) numpy_wlr_sklearn_wlengthdiameterheightwhole weightshucked weightviscera weightshell weightsex_Fsex_Mones

-1.12	-1.12
10.00	10.00
20.74	20.74
9.61	9.61
-20.05	-20.05
-12.07	-12.07
6.55	6.55
0.88	0.88
0.87	0.87
4.32	4.32

#做正則化時不包含b lambda I不是真正的系數(shù)

使用Numpy實現(xiàn)嶺回歸

def ridge_regression(X,y,ridge_lambda):penalty_matrix = np.eye(X.shape[1])penalty_matrix[X.shape[1]-1][X.shape[1]-1] = 0w = np.linalg.inv(X.T.dot(X) + ridge_lambda * penalty_matrix).dot(X.T).dot(y)return w

在鮑魚訓練集上使用ridge_regression函數(shù)訓練嶺回歸模型，正則化系數(shù)設置為1

w2 = ridge_regression(X_train,y_train,1.0) print(w2) [ 2.30976528 6.72038628 10.23298909 7.05879189 -17.16249532-7.2343118 9.3936994 0.96869974 0.9422174 4.80583032] w1["numpy_ridge_w"] = w2 w1.round(decimals=2) numpy_wlr_sklearn_wnumpy_ridge_wlengthdiameterheightwhole weightshucked weightviscera weightshell weightsex_Fsex_Mones

-1.12	-1.12	2.31
10.00	10.00	6.72
20.74	20.74	10.23
9.61	9.61	7.06
-20.05	-20.05	-17.16
-12.07	-12.07	-7.23
6.55	6.55	9.39
0.88	0.88	0.97
0.87	0.87	0.94
4.32	4.32	4.81

利用sklearn實現(xiàn)嶺回歸

與sklearn中嶺回歸對比，同樣正則化敘述設置為1

from sklearn.linear_model import Ridge ridge = Ridge(alpha=1.0) ridge.fit(X_train[features_without_ones],y_train) w_ridge = [] w_ridge.extend(ridge.coef_) w_ridge.append(ridge.intercept_) w1["ridge_sklearn_w"] = w_ridge w1.round(decimals=2) numpy_wlr_sklearn_wnumpy_ridge_wridge_sklearn_wlengthdiameterheightwhole weightshucked weightviscera weightshell weightsex_Fsex_Mones

-1.12	-1.12	2.31	2.31
10.00	10.00	6.72	6.72
20.74	20.74	10.23	10.23
9.61	9.61	7.06	7.06
-20.05	-20.05	-17.16	-17.16
-12.07	-12.07	-7.23	-7.23
6.55	6.55	9.39	9.39
0.88	0.88	0.97	0.97
0.87	0.87	0.94	0.94
4.32	4.32	4.81	4.81

嶺跡分析

alphas = np.logspace(-10,10,20) coef = pd.DataFrame() for alpha in alphas:ridge_clf = Ridge(alpha=alpha)ridge_clf.fit(X_train[features_without_ones],y_train)df = pd.DataFrame([ridge_clf.coef_],columns=X_train[features_without_ones].columns)df['alpha']=alphacoef =coef.append(df,ignore_index=True) coef.round(decimals=2) lengthdiameterheightwhole weightshucked weightviscera weightshell weightsex_Fsex_Malpha012345678910111213141516171819

-1.12	10.00	20.74	9.61	-20.05	-12.07	6.55	0.88	0.87	0.000000e+00
-1.12	10.00	20.74	9.61	-20.05	-12.07	6.55	0.88	0.87	0.000000e+00
-1.12	10.00	20.74	9.61	-20.05	-12.07	6.55	0.88	0.87	0.000000e+00
-1.12	10.00	20.74	9.61	-20.05	-12.07	6.55	0.88	0.87	0.000000e+00
-1.12	10.00	20.74	9.61	-20.05	-12.07	6.55	0.88	0.87	0.000000e+00
-1.12	10.00	20.74	9.61	-20.05	-12.07	6.55	0.88	0.87	0.000000e+00
-1.12	10.00	20.73	9.61	-20.05	-12.07	6.55	0.88	0.87	0.000000e+00
-1.10	9.98	20.68	9.60	-20.04	-12.05	6.56	0.88	0.87	0.000000e+00
-0.88	9.79	20.13	9.50	-19.94	-11.86	6.71	0.88	0.88	3.000000e-02
0.73	8.33	15.60	8.55	-18.97	-10.05	7.98	0.92	0.90	3.000000e-01
3.20	5.02	5.40	5.11	-13.71	-3.67	9.61	1.07	1.00	3.360000e+00
1.66	1.76	1.12	2.53	-3.54	-0.09	3.67	1.33	1.11	3.793000e+01
0.51	0.47	0.22	1.63	0.18	0.30	0.79	0.89	0.69	4.281300e+02
0.12	0.10	0.04	0.46	0.15	0.09	0.16	0.21	0.16	4.832930e+03
0.01	0.01	0.00	0.05	0.02	0.01	0.02	0.02	0.02	5.455595e+04
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	6.158482e+05
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	6.951928e+06
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	7.847600e+07
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	8.858668e+08
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.000000e+10

plt.rcParams['figure.dpi'] = 300#分辨率 plt.figure(figsize=(9,6)) coef['alpha']=coef['alpha']for feature in X_train.columns[:-1]:plt.plot('alpha',feature,data=coef) ax = plt.gca() ax.set_xscale('log') plt.legend(loc='upper right') plt.xlabel(r'$\alpha$',fontsize=15) plt.ylabel('系數(shù)',fontsize=15) Text(0, 0.5, '系數(shù)')Font 'default' does not have a glyph for '-' [U+2212], substituting with a dummy symbol. Font 'default' does not have a glyph for '-' [U+2212], substituting with a dummy symbol. Font 'default' does not have a glyph for '-' [U+2212], substituting with a dummy symbol. Font 'default' does not have a glyph for '-' [U+2212], substituting with a dummy symbol. Font 'default' does not have a glyph for '-' [U+2212], substituting with a dummy symbol. Font 'default' does not have a glyph for '-' [U+2212], substituting with a dummy symbol. Font 'default' does not have a glyph for '-' [U+2212], substituting with a dummy symbol. Font 'default' does not have a glyph for '-' [U+2212], substituting with a dummy symbol. Font 'default' does not have a glyph for '-' [U+2212], substituting with a dummy symbol. Font 'default' does not have a glyph for '-' [U+2212], substituting with a dummy symbol. Font 'default' does not have a glyph for '-' [U+2212], substituting with a dummy symbol. Font 'default' does not have a glyph for '-' [U+2212], substituting with a dummy symbol. Font 'default' does not have a glyph for '-' [U+2212], substituting with a dummy symbol. Font 'default' does not have a glyph for '-' [U+2212], substituting with a dummy symbol. Font 'default' does not have a glyph for '-' [U+2212], substituting with a dummy symbol. Font 'default' does not have a glyph for '-' [U+2212], substituting with a dummy symbol.

使用LASSO構(gòu)建鮑魚年齡預測模型

from sklearn.linear_model import Lasso lasso = Lasso(alpha=0.01) lasso.fit(X_train[features_without_ones],y_train) print(lasso.coef_) print(lasso.intercept_) [ 0. 6.37435514 0. 4.46703234 -13.44947667-0. 11.85934842 0.98908791 0.93313403] 6.500338023591298

LASSO的正則化路徑

coef = pd.DataFrame() for alpha in np.linspace(0.0001,0.2,20):lasso_clf = Lasso(alpha=alpha)lasso_clf.fit(X_train[features_without_ones],y_train)df = pd.DataFrame([lasso_clf.coef_],columns=X_train[features_without_ones].columns)df['alpha']=alphacoef = coef.append(df,ignore_index=True) coef.head() #繪圖 plt.figure(figsize=(9,6),dpi=600) for feature in X_train.columns[:-1]:plt.plot('alpha',feature,data=coef) plt.legend(loc='upper right') plt.xlabel(r'$\alpha$',fontsize=15) plt.ylabel('系數(shù)',fontsize=15) plt.show()

coef lengthdiameterheightwhole weightshucked weightviscera weightshell weightsex_Fsex_Malpha012345678910111213141516171819

-0.568043	9.39275	20.390041	9.542038	-19.995972	-11.900326	6.635352	0.881496	0.875132	0.000100
0.000000	6.02573	0.000000	4.375754	-13.127223	-0.000000	11.897189	0.995137	0.934129	0.010621
0.384927	0.00000	0.000000	2.797815	-7.702209	-0.000000	12.478541	1.093479	0.948281	0.021142
0.000000	0.00000	0.000000	0.884778	-2.749504	0.000000	11.705974	1.098990	0.897673	0.031663
0.000000	0.00000	0.000000	0.322742	-0.000000	0.000000	9.225919	1.072991	0.834021	0.042184
0.000000	0.00000	0.000000	1.555502	-0.000000	0.000000	4.610425	1.013824	0.757891	0.052705
0.000000	0.00000	0.000000	2.786784	-0.000000	0.000000	0.000000	0.954710	0.681821	0.063226
0.000000	0.00000	0.000000	2.797514	-0.000000	0.000000	0.000000	0.848412	0.581613	0.073747
0.000000	0.00000	0.000000	2.807843	-0.000000	0.000000	0.000000	0.742529	0.481711	0.084268
0.000000	0.00000	0.000000	2.818184	-0.000000	0.000000	0.000000	0.636632	0.381799	0.094789
0.000000	0.00000	0.000000	2.828630	-0.000000	0.000000	0.000000	0.530615	0.281801	0.105311
0.000000	0.00000	0.000000	2.838944	-0.000000	0.000000	0.000000	0.424750	0.181912	0.115832
0.000000	0.00000	0.000000	2.849325	-0.000000	0.000000	0.000000	0.318807	0.081967	0.126353
0.000000	0.00000	0.000000	2.851851	-0.000000	0.000000	0.000000	0.225024	0.000000	0.136874
0.000000	0.00000	0.000000	2.819079	-0.000000	0.000000	0.000000	0.186157	0.000000	0.147395
0.000000	0.00000	0.000000	2.786307	-0.000000	0.000000	0.000000	0.147290	0.000000	0.157916
0.000000	0.00000	0.000000	2.753535	0.000000	0.000000	0.000000	0.108422	0.000000	0.168437
0.000000	0.00000	0.000000	2.720762	0.000000	0.000000	0.000000	0.069555	0.000000	0.178958
0.000000	0.00000	0.000000	2.687990	0.000000	0.000000	0.000000	0.030688	0.000000	0.189479
0.000000	0.00000	0.000000	2.652940	0.000000	0.000000	0.000000	0.000000	0.000000	0.200000

from sklearn.metrics import mean_squared_error from sklearn.metrics import mean_absolute_error from sklearn.metrics import r2_score #MAE y_test_pred_lr = lr.predict(X_test.iloc[:,:-1]) print(round(mean_absolute_error(y_test,y_test_pred_lr),4)) 1.6016 y_test_pred_ridge = ridge.predict(X_test[features_without_ones]) print(round(mean_absolute_error(y_test,y_test_pred_ridge),4)) 1.5984 y_test_pred_lasso = lasso.predict(X_test[features_without_ones]) print(round(mean_absolute_error(y_test,y_test_pred_lasso),4)) 1.6402 #MSE y_test_pred_lr = lr.predict(X_test.iloc[:,:-1]) print(round(mean_squared_error(y_test,y_test_pred_lr),4)) 5.3009 y_test_pred_ridge = ridge.predict(X_test[features_without_ones]) print(round(mean_squared_error(y_test,y_test_pred_ridge),4)) 4.959 y_test_pred_lasso = lasso.predict(X_test[features_without_ones]) print(round(mean_squared_error(y_test,y_test_pred_lasso),4)) 5.1 #R2系數(shù) print(round(r2_score(y_test,y_test_pred_lr),4)) print(round(r2_score(y_test,y_test_pred_ridge),4)) print(round(r2_score(y_test,y_test_pred_lasso),4)) 0.5257 0.5563 0.5437

殘差圖

plt.figure(figsize=(9,6),dpi=600) y_train_pred_ridge = ridge.predict(X_train[features_without_ones]) plt.scatter(y_train_pred_ridge,y_train_pred_ridge - y_train,c="g",alpha=0.6) plt.scatter(y_test_pred_ridge,y_test_pred_ridge - y_test,c="r",alpha=0.6) plt.hlines(y=0,xmin=0,xmax=30,color="b",alpha=0.6) plt.ylabel("Residuals") plt.xlabel("Predict") Text(0.5, 0, 'Predict')

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