I've been working on a GridSearchTransductive which might do what you need
- or be adjustable to do it. You can take a look at #6160 Refactor
model_selection._search to include transductive estimators.
Jamie
On Fri, 5 Feb 2016, 17:42 Vlad Niculae <zephy...@gmail.com> wrote:
> Hi Mamun,
>
> If your cluster labels are known, you can use the LabelShuffleSplit
> ore LeavePLabelOut cross-validation generators.
>
> HTH,
> Vlad
>
> On Fri, Feb 5, 2016 at 10:05 AM, Mamun Rashid <mamunbabu2...@gmail.com>
> wrote:
> > Hi Folks,
> > I have a two class classification problem where the positive labels
> reside in clusters.
> > A traditional cross validation approach is not aware of this issue and
> splits data points from a cluster in to training and test set giving rise
> to strong classification performance.
> > I have written a custom cross validation routine where I hold data
> points from each cluster either in training or in test set (never allowing
> them to split). Finally I retrain the a
> > Random forest classifier using all the positive set.
> >
> > My question is :
> > - Can I somehow tune the parameters for a RFC for train the final
> classifier using these tuned parameters.
> >
> > I do understand that GridSearchCV or Randomised parameter optimisation
> allows to do this but it follows a traditional CV and splits the clusters I
> mentioned earlier.
> >
> >
> > Thanks in advance.
> >
> > Mamun
> >
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