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
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
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...
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-...
:probabl.
:probabl.
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
Olivier Grisel
Olivier Grisel
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-...
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...
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...
