Hi Alexandre,
My problem was that each sample was obtained from a specific subject (a
same subject possibly produced several samples) and I wanted to train and
test on different subjects.
As I understand it, LeaveOneLabelOut (and more generally LeavePLabelOut)
can be used to leaves one subject (or K subjects) out.
However it does not maintain a balance in the folds. (For instance, if we
use a LeaveOneLabelOut, we could have a testing set with only one sample
and another one with 100 if one subject appear only in 1 sample and another
in 100 samples).
Here I wanted to keep the folds approximately balanced while making sure
that a same subject was not used for both training and testing.
Cheers,
Jean
2015-03-24 15:58 GMT+00:00 Alexandre Gramfort <
alexandre.gramf...@telecom-paristech.fr>:
> 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> 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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