Hello everyone,
I am running an undersampling algorithm from imblearn, and at the back I want
to train several gbms (catboost, xgb, lgbm, etc) with successivehalving to
optimize the hyperparams.
Based on imblearn docs, I need to set up the undersampler within a pipeline,
but that's innefficient, because in theory, I could undersample all 3 folds of
the training data, store the left out folds with the original distribution, and
then on those, train the various models with SH.
So my question is: how can I pass data folds (undersampled for training and
original for the left out) to successive halving (or any hyperparam search for
that matter)?
I posted a [question on
stackoverflow](https://stackoverflow.com/questions/79748461/how-to-pass-pre-computed-folds-to-successivehalving-in-sklearn)
with this issue. Sharing it here in case someone knows.
Thanks a lot!
Best
Sole
Soledad Galli
https://www.trainindata.com/
_______________________________________________
scikit-learn mailing list -- scikit-learn@python.org
To unsubscribe send an email to scikit-learn-le...@python.org
https://mail.python.org/mailman3//lists/scikit-learn.python.org
Member address: arch...@mail-archive.com
