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
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-...
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.
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...
Legend for changelogs something big that you couldn’t do before., something that you couldn’t do before., an existing feature now may not require as much computation or memory., a miscellaneous min...
While making the code of skrub compatible with scikit-learn 1.6, I found that the following is really surprising: # %% import numpy as np from sklearn.base import BaseEstimator, RegressorMixin clas...
With Artefact, we are delighted to invite data leaders to an exclusive Paris masterclass: ✨Aligning Probabilistic Classification with Business Decisions using @scikit-learn.bsky.social ✨ 🚨Limited seats available! Secure your spot now 👉🏻 lu.ma/fopoglzo #MachineLearning #Advanced #AI #Masterclass
scikit-learn
Video
:probabl.
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
At Probabl, together with the wider community, we continue our dedicated efforts to support and enhance @scikit-learn.org and its ecosystem. In this post, we provide a retrospective on the work accomplished over the last 6 months and the roadmap for the next 6:
papers.probabl.ai/open-source-...
