Scikit-learn is on the @linuxfoundation.org Open Source Insights board, alongside with many other central projects:
insights.linuxfoundation.org/project/scik...
What an honor to contribute to the open-source tissue that cements the world!
Looking forward to meeting you there, exchanging with experts of all the fields of data science, learning more about your use cases and collecting feedback for the future developments of our library and related projects!
There is still time to grab a seat to attend the main conference talks and maybe even present a lightning talk ;)
We will also participate in the sprint day on Thursday the 2nd, but unfortunately that day has limited seats and there is already a growing dedicated waitlist.
A bunch of scikit-learn core contributors will attend or speak at @pydataparis.bsky.social 2025 on Tuesday and Wednesday next week.
Ticketing, practical infos and schedule at: pydata.org/paris2025
SKADA is a beautiful software for Domain Adaptation in python with many shallow and deep methods implemented, it is 100% compatible with @scikit-learn.org models and pipelines and with @pytorch.org for deep learning methods.
❄️ The Christmas release is here! ❄️
Introducing scikit-learn 1.6 with:
🟢 2 major features & 34 improvements
🔵 5 efficiency boosts & 21 enhancements
🟡 14 API changes
🔴 30 fixes
👥 160 amazing contributors
youtu.be/7wiHChpwJe8
We are working on a small package to ease developer life: github.com/glemaitre/sk.... The idea is that recurrent work could be centralized in a single package. Once we have a minimal version, we will do a first release to support scikit-learn 1.2 to 1.6
Have you ever wanted to unpickle a @scikit-learn.bsky.social model you trained with version X while using a newer version X+1? If yes, why? When? How? I'd be interested to hear about your use cases to see if we can make it less painful
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.
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...
