Mailman 3 December 2023 - scikit-learn

[ANN] scikit-learn 0.24.0rc1 is online!
by Olivier Grisel Jan. 19, 2024

Jan. 19, 2024

Please help us test the first release candidate for scikit-learn 0.24.0: pip install scikit-learn==0.24.0rc1 Changelog: https://scikit-learn.org/0.24/whats_new/v0.24.html In particular, if you maintain a project with a dependency on scikit-learn, please let us know about any regression. Feel free to also retweet the announcement to get more people to test it before the final release (probably in 1 week or 2): https://twitter.com/scikit_learn/status/1334562221498753026 Thanks to anybody who helped make this happen! -- Olivier

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Announcing skrub: Prepping tables for machine learning
by Gael Varoquaux Dec. 19, 2023

Dec. 19, 2023

Hi everyone, We are very happy to announce the first release of a new package called "skrub". It's goal is to facilitate data preparation from tables to machine learning with an API similar to that of scikit-learn. https://skrub-data.org The most useful tool in the short term is the "TableVectorizer", which applies a bunch of heuristics to turn a complex into a good data representation for learning (for instance encoding dates, or strings). Combined with scikit-learn HistGradientBoosting, it gives a strong baseline for most tabular learning settings without data massaging: from sklearn.ensemble import HistGradientBoostingRegressor from sklearn.pipeline import make_pipeline from skrub import TableVectorizer pipeline = make_pipeline(TableVectorizer(), HistGradientBoostingRegressor()) pipeline.fit(X, y) In the longer term, skrub will enable assembling full data processing pipelines across multiple tables that can be cross-validated with scikit_learn and one day put in production: Joining, Aggregation, transformation to build models directly from the original tables and database. One example of such pipeline can be seen here: https://skrub-data.org/stable/auto_examples/08_join_aggregation.html#chaini… But there is a lot that remains to be done, and the questions are quite open. In my eyes, the dream is to bridge scikit-learn's API, that separates fit/transform (because it helps making robust and valid predictive pipelines) with dataframe/database operations. The goal is not to provide something as flexible as SQL or pandas, but the cover the most frequent usecases in machine learning, as explained here https://skrub-data.org/stable/vision.html Of course, skrub will be developed in the open, with an eye to quality, staying as lightweight as possible while still providing powerful tool. I hope that many will join this adventure! Cheers, Gaël

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video: scikit-learn's Past, Present and Future — with scikit-learn co-founder Dr. Gaël Varoquaux
by Reshama Shaikh Dec. 7, 2023

Dec. 7, 2023

Hello, In this episode, Gaël details: • The genesis, present capabilities and fast-moving future direction of scikit-learn. • How to best apply scikit-learn to your particular ML problem. • How ever-larger datasets and GPU-based accelerations impact the scikit-learn project. • How (whether you write code or not!) you can get started on contributing to a mega-impactful open-source project like scikit-learn yourself. • Hugely successful social-impact data projects his Soda lab has had recently. • Why statistical rigor is more important than ever and how software tools could nudge us in the direction of making more statistically sound decisions. VIDEO interview: https://www.jonkrohn.com/posts/2023/12/5/scikit-learns-past-present-and-fut… ---- Best, Reshama Shaikh

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