Every model in the "library" must be a separate train object. For
example, if you wish to combine a random forests with several different
values of mtry, you must build a model for each value of mtry. If you
use several values of mtry in one train model, (e.g. tuneGrid =
expand.grid(.mtry=2:5)), caret will select the best value of mtry
before we get a chance to include it in the ensemble. By default,
RMSE is used to ensemble regression models, and AUC is used to ensemble
Classification models. This function does not currently support multi-class
problems