Here's a quick demo of how to fit a binary classification model with caretEnsemble. Please note that I haven't spent as much time debugging caretEnsemble for classification models, so there's probably more bugs than my last post. Also note that multi class models are not yet supported.
Right now, this code fails for me if I try a model like a nnet or an SVM for stacking, so there's clearly bugs to fix.
The greedy model relies 100% on the gbm, which makes sense as the gbm has an AUC of 1 on the training set. The linear model uses all of the models, and achieves an AUC of .5. This is a little weird, as the gbm, rf, SVN, and knn all achieve an AUC of close to 1.0 on the training set, and I would have expected the linear model to focus on these predictions. I'm not sure if this is a bug, or a failure of my stacking model.