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From the discussion during the technical committee, the scikit-learn Consortium at Inria defined the following list of priorities for the coming year: High priority: Continue effort helping with project maintenance to keep the target to release twice a year (+ bugfix releases). 1.2 in progress (planned for november 2022) Refactor common tests to avoid missing estimators unintentionally and split the hard constraints from the scikit-learn internal conventions High…

Hugging Face is happy to announce that we're partnering with scikit-learn to further our support of…

Did you know that, on a rough estimation, only 6% of open source contributors were women?!…

From the discussion during the technical committee of December 3rd 2021, the scikit-learn Consortium defined the following list of priorities for 2022: High priority: Continue effort helping with project maintenance to keep the target to release twice a year (+ bugfix releases). Bugfix release 1.0.2 end of December 2021 (or early January 2022) with macOS/arm64 support for the first time 1.1 planned the first quarter of 2022 Refactor…

Contributing to the whole python ecosystem is crucial and has always been a strong will for…

Technical Committee June 2, 2021 Agenda of the day 9.10 am - 10 am Presentation of the technical achievements and ongoing work by O. Grisel 10 am - 12 pm Feedback and exposition of each partner of the consortium 1 pm - 2 pm Collaborative drafting session of the updated roadmap 2 pm - 5 pm Afternoon discussions on Discord Attendees Scikit-learn @ Fondation Inria Alexandre Gramfort (Inria,…

The scikit-learn Consortium @ Inria defines a roadmap every six to eight months during its Technical Committee. Previous roadmaps are available here. Why a roadmap? The members of the Consortium provide their financial support without any service counterpart. The definition of a development and general activities roadmap is an important step in building trust between the members of the Consortium. It represents our effort to focusing  together on…

While scikit-learn already had some Generalized Linear Models (GLM) implemented, e.g. LogisticRegression, other losses than mean squared error and log-loss were missing. As the world is almost (surely) never normally distributed, regression tasks might benefit a lot from the new PoissonRegressor, GammaRegressor and TweedieRegressor estimators: using those GLMs for positive, skewed data is much more appropriate than ordinary least squares and might lead to more adequate models. Starting…

Presentation of the activities of the Consortium during the last year (C. Marmo, G. Varoquaux): Questions and comments: Fujitsu Fujitsu actively participates in the Consortium remote events. Fujitsu would be glad to increase Japan contributions to scikit-learn. Fujitsu suggests organizing a sprint for Japan time zone, and starting a discussion about good practices to organize online sprints with the team there. Microsoft More information about the MOOC are…

The scikit-learn team at Inria is looking for a engineer to speed-up machine learning with scikit-learn by improving parallel computing. The work will be spread across scikit-learn and its parallel-computing dependencies (joblib, threadpoolctl and possibly CPython). The goal will be to address the performance boottlenecks of scikit-learn with many CPUs. Cython+ is a development project funded by the French government to build core infrastructure for parallel computing in…