Mailman 3 December 2022 - 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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[ANNOUNCEMENT] scikit-learn 1.0 release
by Adrin June 30, 2023

June 30, 2023

Hi everyone, We're happy to announce the 1.0 release which you can install via pip or conda: pip install -U scikit-learn or conda install -c conda-forge scikit-learn You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_relea… and the long list of the changes under https://scikit-learn.org/stable/whats_new/v1.0.html New major features include: mandatory keyword arguments in many places, Spline Transformers, Quantile Regressor, Feature Names Support, a more flexible plotting API, Online One-Class SVM, and much more! This version supports Python versions 3.7 to 3.9. A big thanks to all contributors for making this release possible. Regards, Adrin, On the behalf of the scikit-learn maintainer team.

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[ANN] scikit-learn 1.1.3 is online!
by Guillaume Lemaître March 9, 2023

March 9, 2023

scikit-learn 1.1.3 is out on pypi.org and conda-forge! This bugfix release only includes fixes for compatibility with the latest SciPy release >= 1.9.2 and wheels for Python 3.11. Note that support for 32-bit Python on Windows has been dropped in this release. This is due to the fact that SciPy 1.9.2 also dropped the support for that platform. Windows users are advised to install the 64-bit version of Python instead. More details in the changelog: https://scikit-learn.org/dev/whats_new/v1.1.html#version-1-1-3 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn Thanks again to all the contributors. On behalf of the scikit-learn maintainer team. -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/

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scikit-learn monthly developer meeting: Monday January 9, 2023
by Thomas J. Fan Dec. 16, 2022

Dec. 16, 2022

Dear all, The scikit-learn developer monthly meeting will take place on Monday January 9 at 15:00 UTC. - Video call link: https://meet.google.com/gmn-acub-mrr - Meeting notes / agenda: https://hackmd.io/0yokz72CTZSny8y3Re648Q - Local times: https://www.timeanddate.com/worldclock/meetingdetails.html?year=2023&month=… The goal of this meeting is to discuss ongoing development topics for the project. Everybody is welcome. As usual, please follow the code of conduct of the project: https://github.com/scikit-learn/scikit-learn/blob/main/CODE_OF_CONDUCT.md Regards, Thomas

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Query regarding svm function
by vaibhavi patel Dec. 14, 2022

Dec. 14, 2022

Dear developers, This is vaibhavi pursuing my Ph.D. in data science, writing this mail with one question how does the library function of SVC calculate the intercept_ value of the hyperplane? As I am exploring the SVC function with kernel linear. I am getting the same coef_ as your library function giving. But when I am calculating intercept, it is different. I am modifying the SVM function to incorporate the data imbalance in the dataset as a part of my Ph.D. work. Kindly help me in this regard. This will give me another direction. Waiting for your response. -- Regards, Vaibhavi Patel Ph:- 8866022190

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(no subject)
by yoshizawa ryota Dec. 13, 2022

Dec. 13, 2022

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ANN: imbalanced-learn 0.10.0 released
by Guillaume Lemaître Dec. 9, 2022

Dec. 9, 2022

Hi all, We are happy to announce the 0.10.0 version of imbalanced-learn. imbalanced-learn is a toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced data sets frequently encountered in machine learning and data mining. This release makes sure to be fully compatible with scikit-learn 1.0, 1.1, and 1.2 pip install -U imbalanced-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge imbalanced-learn For more information, examples, and documentation, please visit: https:// imbalanced-learn.org -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/

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