We can fit more than one model in Super Learner and it will tell which is best from all the applied models. It also creates a weighted average for all the models.
You have completed all the recipes, and you have dataset X
,Y
, X_Hold
, and Y_Hold
created from the previous recipes.
Perform the following steps in R:
> install.packages("SuperLearner") > library(SuperLearner) > sl_models = c("SL.xgboost", "SL.randomForest", "SL.glmnet", "SL.nnet", "SL.rpartPrune", "SL.lm", "SL.mean") > superlearner = SuperLearner(Y = Y, X = X, family = gaussian (), SL.library = sl_models) > superlearner Output Call: SuperLearner(Y = Y, X = X, family = gaussian(), SL.library = sl_lib) Risk Coef SL.xgboost_All 7.606564e+00 0.000000e+00 SL.randomForest_All 1.027187e+01 2.907956e-16 SL.glmnet_All 8.641940e-02 0.000000e+00 SL.nnet_All 8.442002e+01 0.000000e...