Method	Train	GSE47908	GSE206285
Lasso+Stepglm[both]	1	0.827407407407407	0.46989898989899
SVM	0.999620829120324	0.92	0.614242424242424
glmBoost+SVM	0.998356926188069	0.897777777777778	0.601313131313131
Ridge	0.999241658240647	0.902222222222222	0.537272727272727
Lasso+SVM	1	0.906666666666667	0.583131313131313
glmBoost+Ridge	0.997345803842265	0.875555555555556	0.576666666666667
Enet[alpha=0.1]	1	0.908148148148148	0.488989898989899
glmBoost+Enet[alpha=0.1]	0.998356926188069	0.882962962962963	0.553636363636364
Enet[alpha=0.2]	1	0.906666666666667	0.490909090909091
Enet[alpha=0.3]	1	0.906666666666667	0.491414141414141
glmBoost+Enet[alpha=0.3]	0.998356926188069	0.882962962962963	0.553939393939394
glmBoost+Enet[alpha=0.2]	0.998356926188069	0.882962962962963	0.553232323232323
Enet[alpha=0.4]	1	0.905185185185185	0.495656565656566
glmBoost+Enet[alpha=0.4]	0.998483316481294	0.884444444444444	0.551717171717172
Lasso+glmBoost	0.995829120323559	0.911111111111111	0.616161616161616
Enet[alpha=0.5]	1	0.905185185185185	0.497878787878788
glmBoost	0.996208291203236	0.899259259259259	0.592727272727273
glmBoost+Enet[alpha=0.5]	0.998230535894843	0.882962962962963	0.556262626262626
Enet[alpha=0.6]	1	0.903703703703704	0.502525252525252
glmBoost+Enet[alpha=0.6]	0.998356926188069	0.882962962962963	0.553535353535354
glmBoost+Enet[alpha=0.7]	0.998356926188069	0.882962962962963	0.556161616161616
glmBoost+Enet[alpha=0.8]	0.998356926188069	0.884444444444444	0.552626262626263
Enet[alpha=0.8]	1	0.905185185185185	0.50040404040404
Enet[alpha=0.9]	1	0.911111111111111	0.520707070707071
Lasso	1	0.911111111111111	0.524444444444444
Enet[alpha=0.7]	1	0.911111111111111	0.490909090909091
glmBoost+Enet[alpha=0.9]	0.998356926188069	0.881481481481482	0.552626262626263
glmBoost+Lasso	0.998356926188069	0.881481481481482	0.552828282828283
Lasso+plsRglm	1	0.777777777777778	0.514848484848485
glmBoost+plsRglm	0.998356926188069	0.884444444444444	0.543535353535354
glmBoost+Stepglm[forward]	0.998988877654196	0.856296296296296	0.472121212121212
Lasso+Stepglm[forward]	1	0.877037037037037	0.455252525252525
RF+SVM	0.999494438827098	0.894814814814815	0.57989898989899
Stepglm[forward]	1	0.935555555555556	0.46959595959596
plsRglm	0.998988877654196	0.882962962962963	0.47010101010101
RF+Ridge	0.997345803842265	0.878518518518519	0.461414141414141
RF+Enet[alpha=0.1]	0.998862487360971	0.891851851851852	0.53
RF+plsRglm	0.998230535894843	0.89037037037037	0.519191919191919
RF+Stepglm[forward]	1	0.731111111111111	0.501616161616162
RF+Enet[alpha=0.2]	0.998862487360971	0.891851851851852	0.531414141414141
RF+Enet[alpha=0.3]	0.999115267947422	0.899259259259259	0.532424242424242
RF+Enet[alpha=0.6]	0.999241658240647	0.902222222222222	0.536666666666667
RF+Lasso	0.998862487360971	0.902222222222222	0.553535353535354
RF+Enet[alpha=0.7]	0.998988877654196	0.900740740740741	0.537777777777778
RF+Enet[alpha=0.5]	0.998988877654196	0.897777777777778	0.536363636363636
RF+glmBoost	0.996208291203236	0.900740740740741	0.593737373737374
RF+Enet[alpha=0.9]	0.999368048533873	0.908148148148148	0.541717171717172
RF+Enet[alpha=0.4]	0.999115267947422	0.900740740740741	0.533939393939394
RF+Enet[alpha=0.8]	0.999115267947422	0.900740740740741	0.541111111111111
RF+Stepglm[both]	1	0.742962962962963	0.503131313131313
RF+Stepglm[backward]	1	0.742962962962963	0.503131313131313
Stepglm[both]+Ridge	0.996840242669363	0.939259259259259	0.549090909090909
Stepglm[backward]+Ridge	0.996840242669363	0.939259259259259	0.549090909090909
Stepglm[both]+plsRglm	0.99860970677452	0.946666666666667	0.508787878787879
Stepglm[backward]+plsRglm	0.99860970677452	0.946666666666667	0.508787878787879
Stepglm[both]+Enet[alpha=0.9]	1	0.940740740740741	0.475454545454545
Stepglm[backward]+Enet[alpha=0.9]	1	0.939259259259259	0.474444444444444
Stepglm[both]+Enet[alpha=0.1]	1	0.939259259259259	0.471919191919192
Stepglm[backward]+Enet[alpha=0.1]	1	0.939259259259259	0.471919191919192
Stepglm[both]+Enet[alpha=0.8]	1	0.940740740740741	0.470808080808081
Stepglm[backward]+Enet[alpha=0.8]	1	0.939259259259259	0.473939393939394
Stepglm[both]+Enet[alpha=0.2]	1	0.939259259259259	0.473838383838384
Stepglm[backward]+Enet[alpha=0.2]	1	0.939259259259259	0.473838383838384
Stepglm[both]+Lasso	1	0.942222222222222	0.467474747474747
Stepglm[backward]+Lasso	1	0.940740740740741	0.471919191919192
Stepglm[both]+Enet[alpha=0.6]	1	0.939259259259259	0.474242424242424
Stepglm[backward]+Enet[alpha=0.6]	1	0.939259259259259	0.474848484848485
glmBoost+GBM	1	0.945185185185185	0.630808080808081
Stepglm[both]+Enet[alpha=0.7]	1	0.939259259259259	0.473333333333333
Stepglm[backward]+Enet[alpha=0.7]	1	0.939259259259259	0.473333333333333
Lasso+Stepglm[backward]	1	0.827407407407407	0.46989898989899
Stepglm[both]	1	0.888888888888889	0.464545454545455
Stepglm[backward]	1	0.888888888888889	0.464545454545455
glmBoost+Stepglm[both]	0.998736097067745	0.873333333333333	0.514040404040404
glmBoost+Stepglm[backward]	0.998736097067745	0.873333333333333	0.514040404040404
Stepglm[both]+Enet[alpha=0.4]	1	0.939259259259259	0.475
Stepglm[backward]+Enet[alpha=0.4]	1	0.939259259259259	0.472424242424242
Stepglm[both]+Enet[alpha=0.3]	1	0.939259259259259	0.474747474747475
Stepglm[backward]+Enet[alpha=0.3]	1	0.939259259259259	0.474747474747475
Stepglm[both]+glmBoost	0.989256825075834	0.934814814814815	0.563333333333333
Stepglm[backward]+glmBoost	0.989256825075834	0.934814814814815	0.563333333333333
Stepglm[both]+Enet[alpha=0.5]	1	0.939259259259259	0.475252525252525
Stepglm[backward]+Enet[alpha=0.5]	1	0.939259259259259	0.473282828282828
glmBoost+RF	0.999873609706775	0.937777777777778	0.640151515151515
RF	1	0.946666666666667	0.64050505050505
Lasso+GBM	1	0.934814814814815	0.607676767676768
RF+GBM	0.999873609706775	0.934814814814815	0.637373737373737
GBM	1	0.945185185185185	0.628383838383838
Stepglm[both]+SVM	0.999494438827098	0.918518518518519	0.573030303030303
Stepglm[backward]+SVM	0.999494438827098	0.918518518518519	0.573030303030303
Lasso+RF	1	0.922962962962963	0.643989898989899
Stepglm[both]+GBM	1	0.948148148148148	0.604646464646465
Stepglm[backward]+GBM	1	0.948148148148148	0.606262626262626
Stepglm[both]+RF	0.999747219413549	0.948148148148148	0.652171717171717
LDA	0.998104145601618	0.906666666666667	0.621414141414141
glmBoost+LDA	0.995449949443883	0.865185185185185	0.602626262626263
RF+LDA	0.995702730030334	0.878518518518518	0.584242424242424
Stepglm[both]+LDA	0.996587462082912	0.925925925925926	0.541212121212121
Stepglm[backward]+LDA	0.996587462082912	0.925925925925926	0.541212121212121
Lasso+LDA	0.998736097067745	0.863703703703704	0.582828282828283
Stepglm[backward]+RF	0.999747219413549	0.943703703703704	0.65520202020202
XGBoost	1	0.9	0.641060606060606
Lasso+XGBoost	1	0.921481481481481	0.630050505050505
glmBoost+XGBoost	1	0.925185185185185	0.606414141414141
RF+XGBoost	0.981610212335693	0.657777777777778	0.438939393939394
Stepglm[both]+XGBoost	0.975227502527806	0.935555555555556	0.416767676767677
Stepglm[backward]+XGBoost	0.999873609706775	0.948148148148148	0.553989898989899
NaiveBayes	0.964863498483316	0.899259259259259	0.611212121212121
Lasso+NaiveBayes	0.969034378159757	0.922962962962963	0.584040404040404
glmBoost+NaiveBayes	0.988751263902932	0.887407407407407	0.592676767676768
RF+NaiveBayes	0.983442871587462	0.882962962962963	0.573939393939394
Stepglm[both]+NaiveBayes	0.965369059656218	0.934814814814815	0.598636363636364
Stepglm[backward]+NaiveBayes	0.965369059656218	0.934814814814815	0.598636363636364
Stepglm[both]+RF+NaiveBayes	0.999873609706775	0.945185185185185	0.673181818181818
Lasso+GBM+RF	1	0.933333333333333	0.613737373737374
Stepglm[both]+Enet[alpha=0.8]+XGBoost	1	0.939259259259259	0.474040404040404
Stepglm[backward]+Enet[alpha=0.8]+glmBoost	1	0.939259259259259	0.475
Stepglm[both]+Enet[alpha=0.2]+GBM	1	0.939259259259259	0.473838383838384
Stepglm[backward]+Enet[alpha=0.2]+XGBoost	1	0.939259259259259	0.473838383838384
Stepglm[both]+Lasso+GBM	1	0.939259259259259	0.474545454545455
Stepglm[backward]+Lasso+RF	1	0.942222222222222	0.472424242424242
Stepglm[both]+Enet[alpha=0.6]+GBM	1	0.939259259259259	0.473939393939394
Stepglm[backward]+Enet[alpha=0.6]+GBM	1	0.939259259259259	0.473838383838384
glmBoost+GBM+Lasso	1	0.945185185185185	0.632424242424242
Stepglm[both]+Enet[alpha=0.7]+GBM	1	0.939259259259259	0.473333333333333
Stepglm[backward]+Enet[alpha=0.7]+NaiveBayes	1	0.939259259259259	0.472222222222222
Lasso+Stepglm[backward]+Enet[alpha=0.5]	1	0.827407407407407	0.46989898989899