# Optimal parameter selection by repeated k-fold

I am working on Lasso problem and the selection of the optimal tuning parameter with $k$-fold procedure, say $k=10$. Since this procedure relies on random subsampling, value of the optimal parameter will change each time I repeat the procedure. As an example, it can be 0.32, then 0.41, then 0.29, etc.

Two questions:

1. Can I use repeated $k$-fold and average the results?
2. How do I compute the standard error in order to use one standard rule?
• Is it a linear model? – Dikran Marsupial Mar 19 '12 at 12:42
• It would be good to register your account, grant. Also, I'd like to remind you that you can accept responses when you feel they directly answer your question. Registering will further allow you to get system wide notification and let you vote on Q&As, which is a sensible way to point out good replies on this site. – chl Mar 19 '12 at 13:12
• yes it is classic linear regression with norm constraint – grant Mar 19 '12 at 22:45
• @grant I've merged your two accounts. You can now safely use the last registered one. – chl Mar 20 '12 at 22:02