Today at #EuroScipy2025, @glemaitre58.bsky.social and I presented a tutorial on pitfalls of machine learning for imbalanced classification problems.

We discussed what (not) to do when fitting a classifier and obtaining degenerate precision or recall values.

probabl-ai.github.io/calibration-...

A small update on the retrospective and future priorities of the open source team at @probabl.bsky.social for the next 6 months or so.

Sometimes you think you are right by doing everything "by the book." But sometimes the book is just a tiny part of the full story. Keep digging and writing a new chapter with more insights is actually fun...

New podcast episode! This one is about imbalanced-learn and how the maintainer looks back with some lessons learned.

If you are dealing with imbalanced classification use-cases, like fraud, you'll want to listen in on this one!

youtu.be/npSkuNcm-Og

I recently shared some of my reflections on how to use probabilistic classifiers for optimal decision-making under uncertainty at @pydataparis.bsky.social 2024.

Here is the recording of the presentation:

www.youtube.com/watch?v=-gYn...

3rd-party library maintainers might find it cumbersome to handle the transition to the new estimator tags while keeping backward compatibility with older scikit-learn versions. We will devise a way to smooth out the transition before releasing 1.6.0 final:

github.com/scikit-learn...

Please help us test the first release candidate for scikit-learn 1.6: pip install scikit-learn==1.6.0rc1

Changelog: scikit-learn.org/1.6/whats_ne...

In particular, if you maintain a project with a dependency on
scikit-learn, please let us know about any regression.

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