Oliver Rausch

I don't see a section for it yet, so I'll make a request here: It would be really helpful if the C++ runtime could also be documented. I often find...

Exciting! Can't wait to try this

> I wonder because of this [4ecc51b#diff-fa5683f6b9de871ce2af02905affbdaaR80](https://github.com/scikit-learn/enhancement_proposals/commit/4ecc51bc00f113f8be9b6c4aad8ee477e3c9a02c#diff-fa5683f6b9de871ce2af02905affbdaaR80) Sorry, that is a mistake on my part. They should not be applied on transform. EDIT: and accordingly, during `fit_transform`, resamplers are only...

Currently, my PR applies resamplers on `transform` (and so does `imblearn`). It seems that it is not trivial to change the behavior so that, for `fit_transform`, resamplers are only applied...

> We are skipping the resampler during `transform`. Sorry, I missed that. I think there is a problem regardless. > When calling `fit_transform` on a pipeline, we will call all...

Some options to address this: - Forbid calling `fit_transform` on a pipeline containing resamplers. (like we do with `fit_predict`). - Define the API so that `transform` applies resamplers (then the...

> I'm not sure if we want to support this use-case - in the end you could always just add another class to the main problem that is "outlier". Although...

Sorry, I was referring to implementing novelty rejection under the resampler API (usecase 2 of @adrinjalali's comment)

Let me rephrase to clarify: > 2. I get periodic data from a bunch of sensors installed in a factory, and I need to do predictive maintenance. Sometimes the data...

I have time to help out with this. > I think this deserves a little bit of ironing out, then some beefing up with examples. What kinds of examples are...