On 07/20/2016 01:31 PM, Guillaume Lemaître wrote:
Hi Gael,
I was wondering if you could elaborate on the problem of
hyper-parameter tuning and why the imbalanced-learn would not benefit
from it.
Since that we used the identical pipeline of scikit-learn and add the
part to handle the sampler, I would have think that we could use it.
However this is true that I did not play to much with this part of the
API, so I should probably missed something.
The assumption is that hyper-parameter tuning uses Pipelines, I think.
You want to select all steps in your processing, which is rarely just a
single model.
However, Pipeline can currently not change the number of samples (see
the enhancement proposal Gael linked to).
So you can not use your methods in the standard scikit-learn pipeline.
Best,
Andy
_______________________________________________
scikit-learn mailing list
[email protected]
https://mail.python.org/mailman/listinfo/scikit-learn
