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 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 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.
