looks like the difference is that it can group several labels into one fold.
not everybody works with "subjects" - the proper name would contain the
word Label or Group, or it should be incorporated in a LeaveLabelsOut which
could have several modes, among which LeaveOneLabelOut and "balanced" mode
which is the present contribution.
can be useful if labels strongly imbalanced or if validation must be able
to handle several labels.
the LFW dataset would be such an example.
Jean, does what I said make sense?
Michael
On Tuesday, March 24, 2015, Alexandre Gramfort <
alexandre.gramf...@telecom-paristech.fr> wrote:
> hi jean,
>
> how different is it from
>
> http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.LeaveOneLabelOut.html
> ?
>
> A
>
> On Tue, Mar 24, 2015 at 4:49 PM, Jean K <jean.kossa...@gmail.com
> <javascript:;>> wrote:
> > Hi all,
> >
> > I recently needed to perform some subject independent KFold
> > cross-validation. To my knowledge this feature isn't in the scikit-learn
> > yet, so I created a pull-request with a simple implementation.
> >
> > It is similar the original Fold except that it takes as parameter an
> array
> > of subjects (similarly to the StratifiedKFold that takes an array of
> labels
> > as a parameter) and separate these into K Folds, approximately
> equilibrated,
> > where each subject appears only in one fold.
> >
> > Do you think this would be useful?
> >
> > Cheers,
> >
> > Jean
> >
> >
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