深入浅出python机器学习_4.3.2_岭回归的参数调节-2

# 重启显示图加这句代码就好了
%matplotlib inlineimport numpy as npimport matplotlib.pyplot as pltfrom sklearn.linear_model import LinearRegressionfrom sklearn.model_selection import train_test_splitfrom sklearn.datasets import load_diabetesX1,y1=load_diabetes().data,load_diabetes().targetX1_train,X1_test,y1_train,y1_test=train_test_split(X1,y1,random_state=8)lr=LinearRegression().fit(X1_train,y1_train)

from sklearn.model_selection import train_test_splitfrom sklearn.linear_model import Ridgefrom sklearn.datasets import load_diabetesX,y=load_diabetes().data,load_diabetes().targetX_train,X_test,y_train,y_test=train_test_split(X,y,random_state=8)ridge=Ridge().fit(X_train,y_train)

from sklearn.model_selection import learning_curve,KFolddef plot_learning_curve(est,X,y):       training_set_size,train_scores,test_scores=learning_curve(est,X,y,train_sizes=np.linspace(.1,1,20),cv=KFold(20,shuffle=True,random_state=1))estimator_name=est.__class__.__name__line=plt.plot(training_set_size,train_scores.mean(axis=1),'--',label='training'+estimator_name)#     打印line的类型print(type(line))
#     打印lineprint(line)a=[1,2,3,4,5]print(type(a))print(a)#     打印test_scoresprint('test_scores:\n',test_scores)#     打印test_scores.mean(axis=1)print('test_scores.mean(axis=1)',test_scores.mean(axis=1))    plt.plot(training_set_size,test_scores.mean(axis=1),'-',label='test'+estimator_name,c=line[0].get_color())plt.xlabel('Training set size')plt.ylabel('Score')plt.ylim(0,1.1)

plot_learning_curve(Ridge(alpha=1),X,y)plot_learning_curve(LinearRegression(),X,y)plt.legend(loc=(0,1.05),ncol=2,fontsize=11)plt.show()

<class 'list'>
[<matplotlib.lines.Line2D object at 0x00000238993AEBE0>]
<class 'list'>
[1, 2, 3, 4, 5]
test_scores:[[ 0.10795253  0.02972947  0.19302853 -0.00422833  0.15905869  0.179216120.12196172  0.11534682  0.07092603  0.14664989  0.07998143  0.08069740.04384173  0.15520807  0.16298464  0.11435203  0.20506466  0.030450470.18026715  0.08096485][ 0.10309377  0.00912415  0.20452825  0.08648943  0.14790277  0.186602690.1458832   0.14723052  0.04103251  0.16622759  0.01918216  0.064917410.03177244  0.1740541   0.18896318  0.15352229  0.24170876 -0.01071260.21967471  0.06760484][ 0.11363622  0.0118143   0.21023011  0.10134561  0.17843619  0.215544150.16082394  0.16306555  0.07750599  0.18136157  0.04929009  0.087390830.05766683  0.17880956  0.21313887  0.17674989  0.25678653 -0.006134940.23659649  0.03048177][ 0.12510019  0.00171877  0.22696679  0.14144448  0.1924482   0.248078670.17779261  0.18214403  0.10587865  0.19900381  0.07329823  0.115462120.07252828  0.18537418  0.24094045  0.19241369  0.294295    0.039119650.26618013  0.0679123 ][ 0.21263285  0.11248701  0.29443796  0.12946097  0.25620346  0.321502670.2015427   0.19835345  0.16470455  0.27510444  0.15505756  0.243433060.14573158  0.24696796  0.27771139  0.21029325  0.36284445  0.141527170.32199449  0.19112395][ 0.27556845  0.1853955   0.35538797  0.12864119  0.33184474  0.362349040.21543396  0.22120758  0.229822    0.32946675  0.27223672  0.326669130.22565932  0.31964353  0.31710642  0.21772447  0.43934319  0.219323530.36713576  0.29864997][ 0.29107832  0.18557704  0.38517624  0.15657818  0.34842802  0.385047160.229705    0.2313514   0.23868913  0.34406487  0.30588147  0.347262640.23890853  0.34931081  0.35798313  0.23011852  0.46468395  0.221372450.39313135  0.31573689][ 0.30158368  0.19497811  0.39302863  0.17014826  0.36803144  0.395374290.23140835  0.24903966  0.24629816  0.36439809  0.33541317  0.364185570.2457484   0.36637272  0.37252448  0.23966859  0.4823136   0.221301980.41759626  0.34577107][ 0.30876874  0.18732281  0.39108314  0.19230288  0.35845282  0.401347770.23976515  0.24702286  0.24716477  0.37248646  0.3274112   0.363218590.25459421  0.36390316  0.38292132  0.24722654  0.49238752  0.210241970.42900775  0.33973672][ 0.3167403   0.19839591  0.41177481  0.18742224  0.38050481  0.407964670.23613467  0.25359658  0.25662336  0.37623464  0.37265494  0.381481080.26989907  0.38554345  0.39843226  0.25411776  0.50522622  0.238942260.44246581  0.35865853][ 0.32685246  0.21303189  0.4332695   0.19436403  0.40081015  0.417453720.24012874  0.26184324  0.26567624  0.38627799  0.40693344  0.410235690.28772116  0.41125732  0.4203108   0.25768155  0.52665492  0.251478170.4667227   0.38813771][ 0.34801915  0.20874726  0.45616779  0.21653912  0.40783633  0.429510150.24138305  0.28327634  0.29514896  0.40666187  0.42519032  0.437904190.30181662  0.4263959   0.44167003  0.26760199  0.54641259  0.275603740.48936924  0.40930156][ 0.34819509  0.21484523  0.46871031  0.21189855  0.41572608  0.435151360.23942826  0.2757989   0.29665107  0.41760651  0.43551928  0.439327370.30968735  0.44354215  0.46075164  0.26365914  0.55638834  0.288758450.48797545  0.42700824][ 0.34912526  0.21561362  0.47362741  0.22250707  0.42058878  0.440743890.24523323  0.27563962  0.29950461  0.43210367  0.43153647  0.434483520.31230613  0.44866261  0.46998984  0.2727713   0.56212221  0.289226310.49907916  0.43201255][ 0.35177028  0.21693351  0.47351018  0.22448503  0.42353564  0.439788870.24145714  0.27905894  0.30237688  0.43357536  0.44454637  0.441342640.3188898   0.4513815   0.47435339  0.27277479  0.56601197  0.290129590.50029181  0.43706271][ 0.35780511  0.21670539  0.48272072  0.2259406   0.42816539  0.4429370.24488984  0.2767898   0.308994    0.43972813  0.44650234  0.445215940.32197966  0.46350097  0.48276091  0.27526774  0.5714136   0.293424410.50750486  0.44669251][ 0.3647064   0.22779211  0.48212192  0.22591344  0.43079055  0.441931170.23783711  0.28474367  0.30822171  0.44791747  0.46434577  0.45291840.33137857  0.46782671  0.48254555  0.26719848  0.57654832  0.290465840.51057518  0.45879888][ 0.36550067  0.22201656  0.49037327  0.23462562  0.4363792   0.445398870.23804564  0.28402787  0.31634014  0.4493449   0.47220622  0.452365390.33327633  0.4705657   0.48878053  0.27169239  0.58299342  0.299649350.51637679  0.45669247][ 0.37416081  0.23131163  0.49682939  0.23967552  0.44144824  0.448122240.24550776  0.2989942   0.32362068  0.46351968  0.48180351  0.466318390.34268567  0.4781097   0.4988057   0.26794749  0.5885358   0.306123560.51620836  0.468966  ][ 0.3764309   0.2281871   0.50096331  0.25323413  0.44300876  0.452806930.25152748  0.30789188  0.3300216   0.46868205  0.48117286  0.467420290.34513193  0.48280853  0.5085762   0.27578049  0.59223128  0.307517960.52179572  0.47353058]]
test_scores.mean(axis=1) [0.1126727  0.11944011 0.13472698 0.15740501 0.22315575 0.281930460.30100425 0.31525923 0.31781832 0.33164067 0.34834207 0.365727810.37183144 0.37634386 0.37916382 0.38394695 0.38772886 0.391332570.39893472 0.403436  ]
<class 'list'>
[<matplotlib.lines.Line2D object at 0x000002389735AEF0>]
<class 'list'>
[1, 2, 3, 4, 5]
test_scores:[[ 0.22993234 -0.09442203  0.32382643  0.23729681  0.32702779 -0.808410980.02847367  0.38348811  0.3358116   0.41338098  0.4444908  -0.113406830.31832041  0.04851563  0.57001936  0.09865547  0.47336953  0.360991360.51414717  0.58489261][ 0.31479342  0.05834502  0.45757131  0.34479816  0.37170185 -0.112937330.17044256  0.43747304  0.37185466  0.60569207  0.45550772 -0.069751610.33835679  0.24601382  0.59340561  0.26596577  0.63380753  0.329570080.48509032  0.66547209][ 0.33176274  0.12529377  0.45073023  0.24651138  0.49026762 -0.374962930.16767273  0.4401526   0.40699931  0.61097385  0.46272906 -0.007293640.33172915  0.21731561  0.53698141  0.23497772  0.66589256  0.391573650.52807613  0.65364632][ 0.32712922  0.02285502  0.46017464  0.27762194  0.51648973  0.035969010.03324218  0.44180665  0.48610805  0.56736756  0.48676939  0.072358280.36746891  0.2546355   0.57914133  0.2924383   0.67116122  0.421540020.59045615  0.62438296][ 0.39248242  0.13883458  0.49645226  0.19820292  0.52662099  0.310072670.06294176  0.4307292   0.46016782  0.55594782  0.46419193  0.23441890.37080868  0.26990743  0.57195911  0.31161511  0.69132432  0.440622630.59642949  0.65832772][ 0.41061912  0.18933888  0.5125802   0.18724187  0.5524889   0.300474060.09465032  0.43698504  0.45200779  0.54303425  0.49377665  0.303974290.36800213  0.27166439  0.57987427  0.27743822  0.7004638   0.427626510.58179398  0.67811036][ 0.42738565  0.17790824  0.51515827  0.23248794  0.53408353  0.380198360.08390061  0.42700501  0.42856019  0.55645628  0.48771918  0.345822480.37958183  0.30110836  0.59226318  0.27092477  0.70038414  0.392471060.59265339  0.66565939][ 0.43113425  0.19321619  0.50636169  0.34006532  0.52523141  0.377079250.09473072  0.43918636  0.41324483  0.53739712  0.50789616  0.367552070.41939093  0.34447458  0.59642162  0.27827503  0.7034421   0.333549660.60135346  0.62795662][ 0.44810894  0.18500055  0.49550236  0.37888306  0.49449954  0.4010160.10108328  0.4105427   0.42099955  0.55511035  0.51971573  0.450322960.40762885  0.38522178  0.58235762  0.28208038  0.70272647  0.335993860.61656118  0.59039155][ 0.44357694  0.18693882  0.51949637  0.35659187  0.50684635  0.392243820.09882464  0.40607575  0.42277411  0.54160182  0.53923795  0.442596240.40503953  0.40279765  0.58790021  0.29489464  0.69469155  0.365103640.61909666  0.60725893][ 0.44210108  0.1864201   0.53447213  0.35604101  0.48823338  0.382234370.09588114  0.41010183  0.40072674  0.52102591  0.54907936  0.470545480.40621796  0.39598527  0.588052    0.28903629  0.68963064  0.352170490.62952796  0.61494561][ 0.44205633  0.15182448  0.55106459  0.35488584  0.45950964  0.372234020.06353226  0.42767966  0.43970985  0.51818975  0.5557914   0.461130320.39568383  0.38931026  0.59991152  0.28308613  0.67765824  0.377453350.63031516  0.61134158][ 0.43372011  0.15870281  0.56835773  0.34109053  0.46523715  0.39309960.10572041  0.42370441  0.4305649   0.53437772  0.56294877  0.434362130.40101321  0.43319138  0.63233065  0.26225635  0.68672499  0.393569450.60508242  0.62397125][ 0.43182514  0.17170455  0.55710199  0.34219867  0.48492464  0.40401530.14515802  0.41172044  0.43050516  0.55468805  0.55840356  0.416418430.40156981  0.43974081  0.63498105  0.26669077  0.69105425  0.398492050.61348327  0.62092191][ 0.43399615  0.18409067  0.55088914  0.33694503  0.48584548  0.422305040.14932138  0.41471743  0.43208354  0.55528267  0.55815974  0.446734480.41761361  0.45307663  0.62925624  0.27432567  0.68812329  0.381378190.5941933   0.60439147][ 0.43182766  0.18537856  0.56042298  0.32890257  0.47987683  0.411820330.14237664  0.40454232  0.4419729   0.5531299   0.55936618  0.492951630.42113061  0.45820705  0.63319259  0.2689892   0.68453507  0.386354450.59529415  0.60495889][ 0.43454867  0.21449319  0.5447244   0.32818962  0.47511757  0.407536240.15391813  0.41426615  0.42493996  0.55001891  0.56548215  0.507346920.42533901  0.45970534  0.6246312   0.25775288  0.68621545  0.352361070.59363025  0.60064255][ 0.43557105  0.19661961  0.55689834  0.34227431  0.47997863  0.408346080.1531836   0.3991245   0.43202096  0.54787604  0.56778494  0.498696220.42507143  0.46389452  0.62746014  0.26149368  0.67867223  0.376761230.59947279  0.58912709][ 0.4327702   0.21289886  0.55893878  0.34023652  0.4846821   0.400219440.16944599  0.40378911  0.4293147   0.55266989  0.56672291  0.500775550.42602113  0.46252837  0.63168033  0.25808418  0.67650447  0.379079630.58801857  0.59784251][ 0.42807328  0.2061773   0.55915826  0.34792744  0.48410788  0.40361210.16816017  0.40771497  0.43532023  0.55367933  0.56170075  0.49389510.42256408  0.4602313   0.63785247  0.26322541  0.67518651  0.383663270.58951605  0.60450819]]
test_scores.mean(axis=1) [0.23382001 0.34815864 0.34555146 0.3764558  0.40910289 0.418107250.42458659 0.43189797 0.43818734 0.44167937 0.44012144 0.438118410.4445013  0.44877989 0.45063646 0.45226153 0.45104298 0.452016370.45361116 0.4543137 ]

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