On 07/23/2011 11:43 AM, fongchun wrote:
I was also thinking of a bootstrapping approach where I would actually run cv.glmnet say 100 times and then take the mean/median lambda across all the cv.glmnet runs. This way I generate a confidence interval for my optimal lambda I woud use in the end.
A simpler approach is to increase the number of folds. If you set the number of folds equal to n ("leave-one-out" cross validation), the outcome will no longer be random, as there is only one way of choosing the fold partitions. The main reason people settle for 10-fold CV is computational convenience when n is large, which is not a large problem in your case.
-- Patrick Breheny Assistant Professor Department of Biostatistics Department of Statistics University of Kentucky ______________________________________________ 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.
