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TECHNICAL COMMITTEE / June 2, 2021

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,…

How are the priorities of the consortium defined?

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…

Generalized Linear Models have landed in scikit-learn

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…

Advisory Committee / February 8th 2021

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…

Implementing a faster KMeans in scikit-learn 0.23

The 0.23 version of scikit-learn was released a few days ago, bringing new features, bug fixes and optimizations. In this post we will focus on the rework of KMeans, a long going work started almost two years ago. Better scalability on machines with many cores was the main objective of this journey. It forced us to touch core challenges of low-level parallelism. KMeans clustering Before describing the optimization…

Time to come out! scikit-learn 0.22

A new look and many new features for this 0.22 scikit-learn release. Just a bit earlier than Santa visiting, this past month some special Elves have worked really hard to keep the target of releasing scikit-learn twice a year. Come take a look at some of the many surprises this remarkable package contains. With big data come big responsibilities New features for plotting and interpretability Models fitted by…

Fujitsu joins the Consortium

Fujitsu Laboratories join the Consortium. Fujitsu will thereby contribute to the sustainability of the scikit-learn development community. More information is available via the press releases published by Inria and Fujitsu.

Scikit-learn sprint in Paris

Three weeks ago, we organized a scikit-learn sprint in the AXA’s offices in Paris. No less than 37 persons attended the sprint during the week. Such effort is equivalent to a 6 man-month! While the sprint was organized by the scikit-learn fondation @ inria, it united a much wider group of contributors and it was funded by other organizations (see below). Improvements to scikit-learn This sprint saw the…

ADVISORY COMMITTEE / NOVEMBER 8, 2018

Minutes of the scikit-learn Advisory Committee meeting (November 8th 2018)   Where present: Chaouki Boutharouite - AXA Sébastien Conort - BNP Paribas Cardif Sylvain Duranton - BCG David Margery - Inria Foundation Olivier Trébucq - Inria Gaël Varoquaux - Inria Excused: Josh Patterson and Guillaume Barat - Nvidia Laurent Duhem - Intel 1- The current administrative and financial situation of the consortium Two engineers have already been recruited…