Hi,
I have a problem of getting GridSearchCV working with kpls for fine tunning
its parameters. I have reviewed the documentation for how to get a new
estimator work with GridSearchCV but I did not find enough information
there. I pursued myself on this but finally I got stuck with a problem
related to multi-class labels and the cross validation. As cross-validation
randomly subsample from all the data, it may take distinct number of
classes at each iteration, but I need the sampler to get random samples for
each class so that all classes are represented at each iteration. Is there
a way to go around this problem?
Thanks
Eweiwi
On Wed, Dec 4, 2013 at 4:05 PM, Olivier Grisel <[email protected]>wrote:
> Thanks! Could you also run a quick grid search to fine tune gamma and
> C for all models independently?
>
> Another question: what is the time complexity for KPLS w.r.t.
> n_samples? quadatratic, cubic?
>
> Could you please extend your gist to randomly subsample the digits
> dataset to only keep 50% and re-run CV for the 3 models on that?
>
> Thanks again.
>
> --
> Olivier
>
>
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