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Olivier Grisel @ogrisel.bsky.social · 19/08/2025

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-ai.github.io

Imbalanced classification: pitfalls and solutions — Probabilistic calibration of cost-sensitive learning

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glemaitre58.bsky.social @glemaitre58.bsky.social · 06/12/2024

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.

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glemaitre58.bsky.social @glemaitre58.bsky.social · 05/12/2024

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...

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Reposted by @glemaitre58.bsky.social

:probabl. @probabl.ai · 05/12/2024

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

youtu.be

Imbalanced-learn: regrets and onwards - with Guillaume Lemaitre, core-maintainer

YouTube video by probabl

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Reposted by @glemaitre58.bsky.social

Olivier Grisel @ogrisel.bsky.social · 27/11/2024

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...

A high-level summary diagram taken from the slides linked below. It shows the interplay of two main components: a probabilistic model and decision maker or planner.

Probabilistic predictions of an underfitting polynomial classifier on a noisy XOR task and the corresponding under-confident calibration curve.

Probabilistic predictions of an overfitting polynomial classifier and the resulting overconfident calibration curve on the same noisy XOR problem.

Simulation study to show the relative lack of stability of hyperparameter tuning when using hard metrics such as Accuracy or soft yet not probabilistic metrics such as ROC AUC compared to a strictly proper scoring rule such as the log-loss.

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Olivier Grisel @ogrisel.bsky.social · 22/11/2024

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...

github.com

Estimator creating `_more_tags` and inheriting from `BaseEstimator` will not warn about old tag infrastructure · Issue #30257 · scikit-learn/scikit-learn

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...

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Reposted by @glemaitre58.bsky.social

scikit-learn @scikit-learn.org · 22/11/2024

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.

scikit-learn.org

Version 1.6

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...

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:probabl. @probabl.ai · 22/11/2024

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

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