🌐 Mailman 3 May 2022 - scikit-learn - python.org

[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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Kernels in logistic regression model
by Serafeim Loukas May 31, 2022

May 31, 2022

Dear sklearn community, More and more people seem to be interested in using kernels in the logistic regression models. Initially, I saw this post here<https://stackoverflow.com/questions/53199141/kernel-in-a-logistic-regressio…>, and I proposed a solution<https://stackoverflow.com/a/53208223/5025009> for the ANOVA and Nyström kernel methods. Would it make sense to add these in the next sklearn version? Best, Makis

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View full sized k_means.labels_
by Mahmood Naderan May 29, 2022

May 29, 2022

Hi, I have used the following lines of codes k_means = KMeans(n_clusters=i, random_state=4).fit(principalComponents_dataFrame) print(k_means.labels_) But the problem is for large vectors of labels, I see shortened version like this: [4 4 0 ... 0 0 0] How can I force it to print the full length vector? Regards, Mahmood

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Announcement of EuroSciPy 2022
by Guillaume Lemaître May 25, 2022

May 25, 2022

Dear all, The following conference could be of interest. Please find the CfP announcement of EuroSciPy 2022 below. --- We are happy to announce that EuroSciPy, the 14th European Conference on Python in Science, will be back as an in-person event. EuroSciPy 2022 will take place from Monday, August 29 to Friday, September 2 in Basel, Switzerland. The Call for Proposals will be open until Monday, 6 June Here's your chance to present a talk, a tutorial or a poster and share your thoughts at EuroSciPy 2022: https://pretalx.com/euroscipy-2022/cfp About EuroSciPy 2022 The EuroSciPy meeting is a cross-disciplinary gathering focused on the use and development of the Python language in scientific research. This event strives to bring together both users and developers of scientific tools, as well as academic research and state-of-the-art industry. Conference layout: - Mon-Tue, Aug 29-30: Tutorials. - Wed-Thu, Aug 31-Sep 1: Talks. - Fri, Sep 2: Sprints. Tickets Ticket sales will open beginning of June. This year we will offer both in-person and remote-tickets., having limited in-person attendance to 300 people in total. The remaining tickets are reserved for speakers, sponsors and volunteers. Any questions? Check out our website <https://www.euroscipy.org/2022/>. All the best, -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/

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[ANN] scikit-learn 1.1.1 is online!
by Guillaume Lemaître May 19, 2022

May 19, 2022

scikit-learn 1.1.1 is out on pypi.org and conda-forge! This is a small maintenance release that fixes a couple of regressions: https://scikit-learn.org/dev/whats_new/v1.1.html#version-1-1-1 Notably, if you are using tree-based models (i.e. decision tree, random forest, gradient boosting), we corrected a computational performance regression introduced in 1.1.0. 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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Experience with black formatting in scikit-learn for astropy
by Tom Aldcroft May 19, 2022

May 19, 2022

Hi - The astropy core is currently considering implementing code formatting with black, much as scikit-learn did in 2020 ( https://github.com/scikit-learn/scikit-learn/pull/18948). Not surprisingly there are a wide range of opinions and concerns. My goal in writing is to request feedback from the scikit-learn community about how this transition has impacted development and community engagement in the time since the transition. Has there been specific feedback from contributors (both experienced and new) related to black autoformatting? - Ease of actually running black and meeting the black-format standard for PRs. Have there been contributor problems that required assistance? - Style considerations? - Technical problems like backports? Are there any regrets or things you would change? Do you have any advice for the astropy project? Any feedback from your experience would be most appreciated, especially coming from a more science-oriented project like scikit-learn (as opposed to other large projects like django or pytest that have also adopted black). Cheers, Tom (@taldcroft on GitHub)

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ANN: imbalanced-learn 0.9.1 released
by Guillaume Lemaître May 16, 2022

May 16, 2022

Hi all, We are happy to announce the 0.9.1 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 1.1.0 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 <https://imbalanced-learn.org/> -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/

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Multiplicative Weights Update Algorithm Contribution
by Jonathan Li May 15, 2022

May 15, 2022

Dear the Scikit-learn community, I am a researcher at UChicago working on the Multiplicative Weights Update method, a widely used, effective meta-algorithm with broad applications that fits nicely with ensemble methods. The following well cited paper presents the algorithm neatly: http://www.theoryofcomputing.org/articles/v008a006/ I would like to propose adding this algorithm to Scikit-learn, which would be of great benefit to the machine learning research community. It would integrate nicely with the existing `VotingClassifier` and `VotingRegressor` algorithms. Please let me know if you need more information and what you think. I would be more than happy to take charge of this contribution given my experience with the algorithm and existing code built in Python. Best, Jonathan

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scikit-learn monthly developer meeting: Monday May 30, 2022
by Thomas J. Fan May 7, 2022

May 7, 2022

Dear all, The scikit-learn developer monthly meeting will take place on Monday May 30, 2022 at 13:00 UTC. - Video call link: https://meet.google.com/ews-uszu-djs - Meeting notes / agenda: https://hackmd.io/0yokz72CTZSny8y3Re648Q - Local times: https://www.timeanddate.com/worldclock/meetingdetails.html?year=2022&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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