Yes. This is a form of model selection. It would be plausible to run the cross-folds and learning in parallel. Cross validation would only give small parallelism, but if you have several hundred variable sets, that becomes plausible.
This raises the question of what the right map-reduce architecture would be for this sort of architecture. Should there be a special input format that reads input records with a test/train/fold# key or column? The thought would be that normal sequential learning could be done in the reducer, or the folded data could be passed to separate learning algorithms. On 3/31/08 9:08 AM, "Karl Wettin" <[EMAIL PROTECTED]> wrote: > Paul Elschot skrev: >> Op Monday 31 March 2008 15:43:03 schreef Karl Wettin: >>> Paul Elschot skrev: >>>> Parallelizing cross validation may also be trivial, but it would be >>>> quite useful. >>> I know it can be used for feature selection. What else is there? >> >> Actually, I meant no more than K-fold cross validation: >> >> http://en.wikipedia.org/wiki/Cross-validation >> >> It nicely parallelizes to a factor of K. > > Ah, OK. > > I mean that many feature selection algorithms more or less is a series > of cross fold validations using some classifier on either a single or > subset of available attributes. > > > > karl
