You can adjust the candidate set of tuning parameters via the tuneGrid argument in trian() and the process by which the optimal choice is made (via the 'selectionFunction' argument in trainControl()). Check out the package vignettes.
The latest version also has an update.train() function that lets the user manually specify the tuning parameters after the call to train(). On Thu, Feb 9, 2012 at 7:00 PM, Yang Zhang <yanghates...@gmail.com> wrote: > Usually when using raw glmnet I let the implementation choose the > lambdas. However when training via caret::train the lambda values are > predetermined. Is there any way to have caret defer the lambda > choices to caret::train and thus choose the optimal lambda > dynamically? > > -- > Yang Zhang > http://yz.mit.edu/ > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- Max ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
