Imbalanced-learn: regrets and onwards - with Guillaume Lemaitre, core-maintainer скачать в хорошем качестве

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

Imbalanced learn is one of the most popular scikit-learn projects out there. It has support for resampling techniques which historically have always been used for imbalanced classification use-cases. However, now that we are a few years down the line, it may be time to start rethinking the library. As it turns out, other techniques may be preferable. We talk to the maintainer, Guillaume Lemaitre, to discuss the lessons that have been learned over the last decade. The imbalanced learn docs can be found here: https://imbalanced-learn.org/stable/ Scikit-learn has a great guide on calibrating a classifier, which can be found here: https://scikit-learn.org/dev/auto_exa... 00:00 Introduction 01:14 How imbalanced-learn started 15:32 Resampling API 21:32 Scikit-learn 25:41 Lessons learned 34:50 Fix in sklearn 43:28 More reflections 51:17 Wrapup We have a Discord these days, feel free to discuss the podcast with us there! https://discord.probabl.ai This podcast is part of the open efforts over at probabl. To learn more you can check out website or reach out to us on social media. Website: https://probabl.ai/ Bluesky: https://bsky.app/profile/probabl.bsky... LinkedIn:   / probabl   Twitter: https://x.com/probabl_ai #probabl