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 <yanghatespam at 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/
>
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--
Max