# 重啟顯示圖加這句代碼就好了 %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的類型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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