Mailman 3 October 2021 - 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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[Data Umbrella] AFME (Africa & Middle East) scikit-learn open source sprint (scikit-learn)
by Reshama Shaikh Dec. 14, 2021

Dec. 14, 2021

Hello, Data Umbrella is organizing a scikit-learn sprint for this October 23, with a focus on **Africa and the Middle East**. This event is free. A sprint is a 4-hour hands-on hackathon where we work on beginner issues in the scikit-learn GitHub repository. Participants will be paired with another person. There will be core contributors available to answer any questions. Event website is: https://afme2021rc.dataumbrella.org We encourage folks to read the website and then complete the application. The event can be shared in these ways: - Retweet: https://twitter.com/DataUmbrella/status/1435972074842034184 - Share post on LinkedIn: https://www.linkedin.com/feed/update/urn:li:activity:6841738994305294336/ Please feel free to contact me if you have any questions. Cheers, Reshama Shaikh she/her

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Help interpreting decision function plot
by Matemática A3K Nov. 4, 2021

Nov. 4, 2021

Hi! I have been building a tool that integrates statistical engines - specially scikit-learn - with django called django-ai <https://github.com/math-a3k/django-ai/tree/covid-ht>. With that tool, I have built another, covid-ht <https://github.com/math-a3k/covid-ht>, which should showcase the power of those together. That tool is meant to help health professionals with classification tasks based on measurements <https://covid-ht.readthedocs.io/en/latest/beyond_covid19.html>. The tool is heading to its first release as a technology preview, and in this process I have faced a release-blocker issue for which I would like to ask for your help: I can't find a consistent interpretation of the graphs. The graphs are called "conditional decision functions <https://covid-ht.readthedocs.io/en/latest/classification/graphing.html>", where each one is the contour of the decision function of a classifier for an observation in 2 variables while leaving the others fixed. The graphs show classification regions as expected, but my initial interpretation seems wrong (commented out <https://raw.githubusercontent.com/math-a3k/covid-ht/master/docs/classificat…> ). If that explanation was good, I would expect that perturbing one variable in a direction where the graph shows another class should switch the classification, as the remaining variables are fixed and that should be the value that the classifier uses to decide - which is plotted in that plane. That is not happening, as you may check here <http://covid-ht.herokuapp.com/> (the classifier being used is an Histogram-based Gradient Boosting Classification Tree). Any insight about the situation will be highly appreciated and thankful in advance.

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New core dev: Julien Jerphanion
by Guillaume Lemaître Oct. 30, 2021

Oct. 30, 2021

The scikit-learn core development team has welcomed a new member, Julien Jerphanion, who has contributed code, reviews, and documentation since this March (aside from occasional contributions in the past). Congratulation and welcome Julien! On the behalf of the scikit-learn team -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/

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scikit-learn office hours on Friday Oct. 29 2021
by Thomas J. Fan Oct. 28, 2021

Oct. 28, 2021

Hi all, Some of us will be online on the scikit-learn discord this Friday at 11am ET / 15:00 UTC / 17:00 CEST. First time and occasional contributors are welcome to join us to discord using this invitation link: https://discord.gg/YBdN45kD The focus of these office hour sessions is to answer questions about contributing to scikit-learn. We can also split into break out audio/text channels and do pair programming or live reviewing of forgotten pull requests with screen sharing. We can also try to assist you into crafting minimal reproduction cases for bug reports to get a higher likelihood of resolution (e.g. https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports). Please note, our Code of Conduct applies: https://github.com/scikit-learn/scikit-learn/blob/main/CODE_OF_CONDUCT.md If this experiment is successful, we will probably hold this kind of office hours on a regular basis. See you soon on discord! -- Thomas

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[ANN] scikit-learn 1.0.1 is online!
by Guillaume Lemaître Oct. 25, 2021

Oct. 25, 2021

scikit-learn 1.0.1 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/v1.0.html#version-1-0-1 <https://scikit-learn.org/stable/whats_new/v0.24.html#version-0-24-2> 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 October 25th 2021
by Thomas J. Fan Oct. 18, 2021

Oct. 18, 2021

Dear all, The scikit-learn developer monthly meeting will take place on Monday October 25th at 1PM 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=2021&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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(no subject)
by Aco Jugo Oct. 14, 2021

Oct. 14, 2021

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DirtyData and the SuperVectorizer, for non-normalized dataframes
by Gael Varoquaux Oct. 13, 2021

Oct. 13, 2021

Dear scikit-learn community, I would like to announce a new release of dirty-cat, which strives to facilitates machine-learning on non-curated categories: robust to morphological variants, such as typos. The new big feature, which I think is of interest to many, is the "SuperVectorizer", that strives to readily vectorize a pandas dataframe: https://dirty-cat.github.io/stable/auto_examples/01_dirty_categories.html#e… Of course, such an object is full of heuristics. We have tuned them empirically, but we expect more progress in the long term, as we build a bigger databases of dataframes that are difficult to vectorize. We'd love people to join the adventure, it's been fun so far. Cheers, Gaël -- Gael Varoquaux Research Director, INRIA http://gael-varoquaux.info http://twitter.com/GaelVaroquaux

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