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
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
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
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