When I train just using glm, everything works, and I don't even come close to exhausting memory. But when I run train(..., method='glm'), I run out of memory.
Is this because train is storing a lot of data for each iteration of the cross-validation (or whatever the trControl procedure is)? I'm looking at trainControl and I can't find how to prevent this...any hints? I only care about the performance summary and maybe the predicted responses.
(I know it's not related to storing data from each iteration of the parameter-tuning grid search because there's no grid for glm's, I believe.)