not even kfold does that. the train sets overlap. what exactly is the motivation for this?
On Thursday, August 14, 2014, Pagliari, Roberto <[email protected]> wrote: > Here is a note from the documentation: > > Note: contrary to other cross-validation strategies, random splits do not > guarantee that all folds will be different, although this is still very > likely for sizeable datasets. > > So I guess it is not possible to enforce all sets to be different. > > Thank you > > > -----Original Message----- > From: Gael Varoquaux [mailto:[email protected] <javascript:;>] > Sent: Thursday, August 14, 2014 2:01 PM > To: [email protected] <javascript:;> > Subject: Re: [Scikit-learn-general] split function with non repeated sets > > > With k-fold you can shuffle the data, but let’s say you want to run > > k-fold partition multiple times. Can you avoid repetition of the same > > subset of samples? > > We call that 'shuffle split' > > http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.ShuffleSplit.html > > Examples of use: > http://scikit-learn.org/stable/modules/cross_validation.html > > Gaël > > > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > [email protected] <javascript:;> > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > [email protected] <javascript:;> > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >
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