Published May 19, 2020
| Version 0.23.1
Software
Open
scikit-learn/scikit-learn: scikit-learn 0.23.1
Creators
- Olivier Grisel1
- Andreas Mueller2
- Lars
- Alexandre Gramfort1
- Gilles Louppe3
- Peter Prettenhofer
- Mathieu Blondel4
- Vlad Niculae5
- Joel Nothman6
- Arnaud Joly
- Jake Vanderplas7
- manoj kumar8
- Hanmin Qin
- Thomas J Fan
- Nelle Varoquaux9
- Robert Layton10
- Loïc Estève
- Jan Hendrik Metzen
- Nicolas Hug
- Noel Dawe
- Guillaume Lemaitre11
- Adrin Jalali12
- (Venkat) Raghav, Rajagopalan13
- Johannes Schönberger14
- Roman Yurchak
- Wei Li15
- Clay Woolam7
- Kemal Eren
- Tom Dupré la Tour16
- Eustache
- 1. Inria
- 2. Columbia University Data Science Institute
- 3. ULiège
- 4. Google Research, Brain team
- 5. Instituto de Telecomunicações
- 6. Sydney Informatics Hub, University of Sydney
- 7. Google
- 8. Google Brain
- 9. TIMC-IMAG
- 10. dataPipeline
- 11. Scikit-learn @ Inria foundation
- 12. Anaconda Inc.
- 13. Télécom Paristech
- 14. Microsoft
- 15. Tsinghua, Google, Facebook
- 16. UC Berkeley
Description
We're happy to announce the 0.23.1 release which fixes a few issues affecting many users, namely: K-Means should be faster for small sample sizes, and the representation of third-party estimators was fixed.
You can check this version out using:
pip install -U scikit-learn
You can see the changelog here: https://scikit-learn.org/stable/whats_new/v0.23.html#version-0-23-1 The conda-forge builds will be available shortly, which you can then install using:
conda install -c conda-forge scikit-learn
Files
scikit-learn/scikit-learn-0.23.1.zip
Files
(7.9 MB)
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Additional details
Related works
- Is supplement to
- https://github.com/scikit-learn/scikit-learn/tree/0.23.1 (URL)