Ensembles

At this point, we have trained five different models. The predictions are stored in two data frames, one for training and the other for the validation samples:

head(summary_models_train)
 ##    ID_RSSD Default          GLM RF            GBM              deep
 ## 4       37       0 0.0013554364  0 0.000005755001 0.000000018217172
 ## 21     242       0 0.0006967876  0 0.000005755001 0.000000002088871
 ## 38     279       0 0.0028306028  0 0.000005240935 0.000003555978680
 ## 52     354       0 0.0013898732  0 0.000005707480 0.000000782777042
 ## 78     457       0 0.0021731695  0 0.000005755001 0.000000012535539
 ## 81     505       0 0.0011344433  0 0.000005461855 0.000000012267744
 ##             SVM
 ## 4  0.0006227083
 ## 21 0.0002813123
 ## 38 0.0010763298
 ## 52 0.0009740568
 ## 78 0.0021555739
 ## 81 0.0005557417

Let's summarize the accuracy of the previously trained models...