Am 30.11.2012 13:56, schrieb Gael Varoquaux: > On Fri, Nov 30, 2012 at 01:33:42PM +0100, Philipp Singer wrote: >> I decided to stick around Leave-one-Out for now and Im doing grid search >> with cross validation using Leave-one-out. > > Don't. This is not a good model selection strategy, and it is very > costly. Use a stratified kfold with k between 5 or 10.
Well, I only have a few samples and I am explicitely interested in seeing the performance of each sample alone trained on the rest. > > With regards to your question, I don't know, as I haven't used the scikit > in this setting. Well, I think the standard accuracy is fine for this, as it always is 0 or 1 anyways. > > G > > ------------------------------------------------------------------------------ > Keep yourself connected to Go Parallel: > TUNE You got it built. Now make it sing. Tune shows you how. > http://goparallel.sourceforge.net > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > ------------------------------------------------------------------------------ Keep yourself connected to Go Parallel: TUNE You got it built. Now make it sing. Tune shows you how. http://goparallel.sourceforge.net _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
