Yes, I know how to statically specify lambda choices. I was asking about whether there's a way to let glmnet guide that, instead of specifying it in advance. Again, sorry if I could've been more clear.
On Thu, Feb 9, 2012 at 7:15 PM, Max Kuhn <mxk...@gmail.com> wrote: > 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 -- 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.