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鲍鱼数据案例（岭回归 、LASSO回归）
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鮑魚(yú)數(shù)據(jù)集案例實(shí)戰(zhàn)） 數(shù)據(jù)集探索性分析 鮑魚(yú)數(shù)據(jù)預(yù)處理 對(duì)sex特征進(jìn)行OneHot編碼，便于后續(xù)模型納入啞變量 篩選特征 將鮑魚(yú)數(shù)據(jù)集劃分為訓(xùn)練集和測(cè)試集 實(shí)現(xiàn)線性回歸和嶺回歸 使用numpy實(shí)現(xiàn)線性回歸 使用sklearn實(shí)現(xiàn)線性回歸 使用Numpy實(shí)現(xiàn)嶺回歸 利用sklearn實(shí)現(xiàn)嶺回歸 嶺跡分析 使用LASSO構(gòu)建鮑魚(yú)年齡預(yù)測(cè)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(jī)器學(xué)習(xí)實(shí)踐\abalone_dataset.csv" )
data
. head
( )
sexlengthdiameterheightwhole weightshucked weightviscera weightshell weightrings 0M 0.455 0.365 0.095 0.5140 0.2245 0.1010 0.150 15 1M 0.350 0.265 0.090 0.2255 0.0995 0.0485 0.070 7 2F 0.530 0.420 0.135 0.6770 0.2565 0.1415 0.210 9 3M 0.440 0.365 0.125 0.5160 0.2155 0.1140 0.155 10 4I 0.330 0.255 0.080 0.2050 0.0895 0.0395 0.055 7
data
. shape
(4177, 9)
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 weightrings count4177.000000 4177.000000 4177.000000 4177.000000 4177.000000 4177.000000 4177.000000 4177.000000 mean0.523992 0.407881 0.139516 0.828742 0.359367 0.180594 0.238831 9.933684 std0.120093 0.099240 0.041827 0.490389 0.221963 0.109614 0.139203 3.224169 min0.075000 0.055000 0.000000 0.002000 0.001000 0.000500 0.001500 1.000000 25%0.450000 0.350000 0.115000 0.441500 0.186000 0.093500 0.130000 8.000000 50%0.545000 0.425000 0.140000 0.799500 0.336000 0.171000 0.234000 9.000000 75%0.615000 0.480000 0.165000 1.153000 0.502000 0.253000 0.329000 11.000000 max0.815000 0.650000 1.130000 2.825500 1.488000 0.760000 1.005000 29.000000
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
plt
. figure
( figsize
= ( 16 , 8 ) )
for col
in data
. columns
[ 1 : ] : plt
. subplot
( 4 , 2 , i
) i
= i
+ 1 sns
. 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 weightrings length1.000000 0.986812 0.827554 0.925261 0.897914 0.903018 0.897706 0.556720 diameter0.986812 1.000000 0.833684 0.925452 0.893162 0.899724 0.905330 0.574660 height0.827554 0.833684 1.000000 0.819221 0.774972 0.798319 0.817338 0.557467 whole weight0.925261 0.925452 0.819221 1.000000 0.969405 0.966375 0.955355 0.540390 shucked weight0.897914 0.893162 0.774972 0.969405 1.000000 0.931961 0.882617 0.420884 viscera weight0.903018 0.899724 0.798319 0.966375 0.931961 1.000000 0.907656 0.503819 shell weight0.897706 0.905330 0.817338 0.955355 0.882617 0.907656 1.000000 0.627574 rings0.556720 0.574660 0.557467 0.540390 0.420884 0.503819 0.627574 1.000000
fig
, ax
= plt
. subplots
( figsize
= ( 12 , 12 ) )
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(yú)數(shù)據(jù)預(yù)處理
對(duì)sex特征進(jìn)行OneHot編碼，便于后續(xù)模型納入啞變量
sex_onehot
= pd
. get_dummies
( data
[ "sex" ] , prefix
= "sex" )
data
[ sex_onehot
. columns
] = sex_onehot
data
. head
( )
sexlengthdiameterheightwhole weightshucked weightviscera weightshell weightringssex_Fsex_Isex_M 0M 0.455 0.365 0.095 0.5140 0.2245 0.1010 0.150 15 0 0 1 1M 0.350 0.265 0.090 0.2255 0.0995 0.0485 0.070 7 0 0 1 2F 0.530 0.420 0.135 0.6770 0.2565 0.1415 0.210 9 1 0 0 3M 0.440 0.365 0.125 0.5160 0.2155 0.1140 0.155 10 0 0 1 4I 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_Mones 0M 0.455 0.365 0.095 0.5140 0.2245 0.1010 0.150 15 0 0 1 1 1M 0.350 0.265 0.090 0.2255 0.0995 0.0485 0.070 7 0 0 1 1 2F 0.530 0.420 0.135 0.6770 0.2565 0.1415 0.210 9 1 0 0 1 3M 0.440 0.365 0.125 0.5160 0.2155 0.1140 0.155 10 0 0 1 1 4I 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_Monesage 0M 0.455 0.365 0.095 0.5140 0.2245 0.1010 0.150 15 0 0 1 1 16.5 1M 0.350 0.265 0.090 0.2255 0.0995 0.0485 0.070 7 0 0 1 1 8.5 2F 0.530 0.420 0.135 0.6770 0.2565 0.1415 0.210 9 1 0 0 1 10.5 3M 0.440 0.365 0.125 0.5160 0.2155 0.1140 0.155 10 0 0 1 1 11.5 4I 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" ]
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(yú)數(shù)據(jù)集劃分為訓(xùn)練集和測(cè)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_Mones 00.455 0.365 0.095 0.5140 0.2245 0.1010 0.1500 0 1 1 10.350 0.265 0.090 0.2255 0.0995 0.0485 0.0700 0 1 1 20.530 0.420 0.135 0.6770 0.2565 0.1415 0.2100 1 0 1 30.440 0.365 0.125 0.5160 0.2155 0.1140 0.1550 0 1 1 40.330 0.255 0.080 0.2050 0.0895 0.0395 0.0550 0 0 1 50.425 0.300 0.095 0.3515 0.1410 0.0775 0.1200 0 0 1 60.530 0.415 0.150 0.7775 0.2370 0.1415 0.3300 1 0 1 70.545 0.425 0.125 0.7680 0.2940 0.1495 0.2600 1 0 1 80.475 0.370 0.125 0.5095 0.2165 0.1125 0.1650 0 1 1 90.550 0.440 0.150 0.8945 0.3145 0.1510 0.3200 1 0 1 100.525 0.380 0.140 0.6065 0.1940 0.1475 0.2100 1 0 1 110.430 0.350 0.110 0.4060 0.1675 0.0810 0.1350 0 1 1 120.490 0.380 0.135 0.5415 0.2175 0.0950 0.1900 0 1 1 130.535 0.405 0.145 0.6845 0.2725 0.1710 0.2050 1 0 1 140.470 0.355 0.100 0.4755 0.1675 0.0805 0.1850 1 0 1 150.500 0.400 0.130 0.6645 0.2580 0.1330 0.2400 0 1 1 160.355 0.280 0.085 0.2905 0.0950 0.0395 0.1150 0 0 1 170.440 0.340 0.100 0.4510 0.1880 0.0870 0.1300 1 0 1 180.365 0.295 0.080 0.2555 0.0970 0.0430 0.1000 0 1 1 190.450 0.320 0.100 0.3810 0.1705 0.0750 0.1150 0 1 1 200.355 0.280 0.095 0.2455 0.0955 0.0620 0.0750 0 1 1 210.380 0.275 0.100 0.2255 0.0800 0.0490 0.0850 0 0 1 220.565 0.440 0.155 0.9395 0.4275 0.2140 0.2700 1 0 1 230.550 0.415 0.135 0.7635 0.3180 0.2100 0.2000 1 0 1 240.615 0.480 0.165 1.1615 0.5130 0.3010 0.3050 1 0 1 250.560 0.440 0.140 0.9285 0.3825 0.1880 0.3000 1 0 1 260.580 0.450 0.185 0.9955 0.3945 0.2720 0.2850 1 0 1 270.590 0.445 0.140 0.9310 0.3560 0.2340 0.2800 0 1 1 280.605 0.475 0.180 0.9365 0.3940 0.2190 0.2950 0 1 1 290.575 0.425 0.140 0.8635 0.3930 0.2270 0.2000 0 1 1 ...... ... ... ... ... ... ... ... ... ... 41470.695 0.550 0.195 1.6645 0.7270 0.3600 0.4450 0 1 1 41480.770 0.605 0.175 2.0505 0.8005 0.5260 0.3550 0 1 1 41490.280 0.215 0.070 0.1240 0.0630 0.0215 0.0300 0 0 1 41500.330 0.230 0.080 0.1400 0.0565 0.0365 0.0460 0 0 1 41510.350 0.250 0.075 0.1695 0.0835 0.0355 0.0410 0 0 1 41520.370 0.280 0.090 0.2180 0.0995 0.0545 0.0615 0 0 1 41530.430 0.315 0.115 0.3840 0.1885 0.0715 0.1100 0 0 1 41540.435 0.330 0.095 0.3930 0.2190 0.0750 0.0885 0 0 1 41550.440 0.350 0.110 0.3805 0.1575 0.0895 0.1150 0 0 1 41560.475 0.370 0.110 0.4895 0.2185 0.1070 0.1460 0 1 1 41570.475 0.360 0.140 0.5135 0.2410 0.1045 0.1550 0 1 1 41580.480 0.355 0.110 0.4495 0.2010 0.0890 0.1400 0 0 1 41590.560 0.440 0.135 0.8025 0.3500 0.1615 0.2590 1 0 1 41600.585 0.475 0.165 1.0530 0.4580 0.2170 0.3000 1 0 1 41610.585 0.455 0.170 0.9945 0.4255 0.2630 0.2845 1 0 1 41620.385 0.255 0.100 0.3175 0.1370 0.0680 0.0920 0 1 1 41630.390 0.310 0.085 0.3440 0.1810 0.0695 0.0790 0 0 1 41640.390 0.290 0.100 0.2845 0.1255 0.0635 0.0810 0 0 1 41650.405 0.300 0.085 0.3035 0.1500 0.0505 0.0880 0 0 1 41660.475 0.365 0.115 0.4990 0.2320 0.0885 0.1560 0 0 1 41670.500 0.380 0.125 0.5770 0.2690 0.1265 0.1535 0 1 1 41680.515 0.400 0.125 0.6150 0.2865 0.1230 0.1765 1 0 1 41690.520 0.385 0.165 0.7910 0.3750 0.1800 0.1815 0 1 1 41700.550 0.430 0.130 0.8395 0.3155 0.1955 0.2405 0 1 1 41710.560 0.430 0.155 0.8675 0.4000 0.1720 0.2290 0 1 1 41720.565 0.450 0.165 0.8870 0.3700 0.2390 0.2490 1 0 1 41730.590 0.440 0.135 0.9660 0.4390 0.2145 0.2605 0 1 1 41740.600 0.475 0.205 1.1760 0.5255 0.2875 0.3080 0 1 1 41750.625 0.485 0.150 1.0945 0.5310 0.2610 0.2960 1 0 1 41760.710 0.555 0.195 1.9485 0.9455 0.3765 0.4950 0 1 1
4177 rows × 10 columns
實(shí)現(xiàn)線性回歸和嶺回歸
使用numpy實(shí)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
w1
= linear_regression
( X_train
, y_train
)
w1
= pd
. DataFrame
( data
= w1
, index
= X
. columns
, columns
= [ "numpy_w" ] )
w1
. round ( decimals
= 2 )
numpy_w length-1.12 diameter10.00 height20.74 whole weight9.61 shucked weight-20.05 viscera weight-12.07 shell weight6.55 sex_F0.88 sex_M0.87 ones4.32
使用sklearn實(shí)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_w length-1.118146 diameter10.000946 height20.737126 whole weight9.614847 shucked weight-20.050793 viscera weight-12.068492 shell weight6.545291 sex_F0.878552 sex_M0.872831 ones4.324477
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_w length-1.12 -1.12 diameter10.00 10.00 height20.74 20.74 whole weight9.61 9.61 shucked weight-20.05 -20.05 viscera weight-12.07 -12.07 shell weight6.55 6.55 sex_F0.88 0.88 sex_M0.87 0.87 ones4.32 4.32
使用Numpy實(shí)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 ] = 0 w
= np
. linalg
. inv
( X
. T
. dot
( X
) + ridge_lambda
* penalty_matrix
) . dot
( X
. T
) . dot
( y
) return w
在鮑魚(yú)訓(xùn)練集上使用ridge_regression函數(shù)訓(xùn)練嶺回歸模型，正則化系數(shù)設(shè)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_w length-1.12 -1.12 2.31 diameter10.00 10.00 6.72 height20.74 20.74 10.23 whole weight9.61 9.61 7.06 shucked weight-20.05 -20.05 -17.16 viscera weight-12.07 -12.07 -7.23 shell weight6.55 6.55 9.39 sex_F0.88 0.88 0.97 sex_M0.87 0.87 0.94 ones4.32 4.32 4.81
利用sklearn實(shí)現(xiàn)嶺回歸
與sklearn中嶺回歸對(duì)比，同樣正則化敘述設(shè)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_w length-1.12 -1.12 2.31 2.31 diameter10.00 10.00 6.72 6.72 height20.74 20.74 10.23 10.23 whole weight9.61 9.61 7.06 7.06 shucked weight-20.05 -20.05 -17.16 -17.16 viscera weight-12.07 -12.07 -7.23 -7.23 shell weight6.55 6.55 9.39 9.39 sex_F0.88 0.88 0.97 0.97 sex_M0.87 0.87 0.94 0.94 ones4.32 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_Malpha 0-1.12 10.00 20.74 9.61 -20.05 -12.07 6.55 0.88 0.87 0.000000e+00 1-1.12 10.00 20.74 9.61 -20.05 -12.07 6.55 0.88 0.87 0.000000e+00 2-1.12 10.00 20.74 9.61 -20.05 -12.07 6.55 0.88 0.87 0.000000e+00 3-1.12 10.00 20.74 9.61 -20.05 -12.07 6.55 0.88 0.87 0.000000e+00 4-1.12 10.00 20.74 9.61 -20.05 -12.07 6.55 0.88 0.87 0.000000e+00 5-1.12 10.00 20.74 9.61 -20.05 -12.07 6.55 0.88 0.87 0.000000e+00 6-1.12 10.00 20.73 9.61 -20.05 -12.07 6.55 0.88 0.87 0.000000e+00 7-1.10 9.98 20.68 9.60 -20.04 -12.05 6.56 0.88 0.87 0.000000e+00 8-0.88 9.79 20.13 9.50 -19.94 -11.86 6.71 0.88 0.88 3.000000e-02 90.73 8.33 15.60 8.55 -18.97 -10.05 7.98 0.92 0.90 3.000000e-01 103.20 5.02 5.40 5.11 -13.71 -3.67 9.61 1.07 1.00 3.360000e+00 111.66 1.76 1.12 2.53 -3.54 -0.09 3.67 1.33 1.11 3.793000e+01 120.51 0.47 0.22 1.63 0.18 0.30 0.79 0.89 0.69 4.281300e+02 130.12 0.10 0.04 0.46 0.15 0.09 0.16 0.21 0.16 4.832930e+03 140.01 0.01 0.00 0.05 0.02 0.01 0.02 0.02 0.02 5.455595e+04 150.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.158482e+05 160.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.951928e+06 170.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 7.847600e+07 180.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8.858668e+08 190.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.000000e+10
plt
. rcParams
[ 'figure.dpi' ] = 300
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(yú)年齡預(yù)測(cè)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
( )
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_Malpha 0-0.568043 9.39275 20.390041 9.542038 -19.995972 -11.900326 6.635352 0.881496 0.875132 0.000100 10.000000 6.02573 0.000000 4.375754 -13.127223 -0.000000 11.897189 0.995137 0.934129 0.010621 20.384927 0.00000 0.000000 2.797815 -7.702209 -0.000000 12.478541 1.093479 0.948281 0.021142 30.000000 0.00000 0.000000 0.884778 -2.749504 0.000000 11.705974 1.098990 0.897673 0.031663 40.000000 0.00000 0.000000 0.322742 -0.000000 0.000000 9.225919 1.072991 0.834021 0.042184 50.000000 0.00000 0.000000 1.555502 -0.000000 0.000000 4.610425 1.013824 0.757891 0.052705 60.000000 0.00000 0.000000 2.786784 -0.000000 0.000000 0.000000 0.954710 0.681821 0.063226 70.000000 0.00000 0.000000 2.797514 -0.000000 0.000000 0.000000 0.848412 0.581613 0.073747 80.000000 0.00000 0.000000 2.807843 -0.000000 0.000000 0.000000 0.742529 0.481711 0.084268 90.000000 0.00000 0.000000 2.818184 -0.000000 0.000000 0.000000 0.636632 0.381799 0.094789 100.000000 0.00000 0.000000 2.828630 -0.000000 0.000000 0.000000 0.530615 0.281801 0.105311 110.000000 0.00000 0.000000 2.838944 -0.000000 0.000000 0.000000 0.424750 0.181912 0.115832 120.000000 0.00000 0.000000 2.849325 -0.000000 0.000000 0.000000 0.318807 0.081967 0.126353 130.000000 0.00000 0.000000 2.851851 -0.000000 0.000000 0.000000 0.225024 0.000000 0.136874 140.000000 0.00000 0.000000 2.819079 -0.000000 0.000000 0.000000 0.186157 0.000000 0.147395 150.000000 0.00000 0.000000 2.786307 -0.000000 0.000000 0.000000 0.147290 0.000000 0.157916 160.000000 0.00000 0.000000 2.753535 0.000000 0.000000 0.000000 0.108422 0.000000 0.168437 170.000000 0.00000 0.000000 2.720762 0.000000 0.000000 0.000000 0.069555 0.000000 0.178958 180.000000 0.00000 0.000000 2.687990 0.000000 0.000000 0.000000 0.030688 0.000000 0.189479 190.000000 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
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
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
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')
總結(jié)
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