🌐 Mailman 3 November 2021 - 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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[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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scikit-learn monthly developer meeting: Monday November 29th 2021
by Thomas J. Fan Nov. 20, 2021

Nov. 20, 2021

Dear all, The scikit-learn developer monthly meeting will take place on Monday November 29 at 22:00 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=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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request for feedback on cross-library design proposal
by Ralf Gommers Nov. 17, 2021

Nov. 17, 2021

(Apologies for the cross-posting, if you follow more than one of these lists, you may see this multiple times.) Hi all, I'd like to solicit the feedback of maintainers of NumPy, SciPy, scikit-learn, and scikit-image, and other interested community members on this blog post and proposed design to enable multiple array types in SciPy, scikit-learn, scikit-image and other libraries in this ecosystem: https://labs.quansight.org/blog/2021/11/pydata-extensibility-vision/. Because this design is meant to be shared across projects, it would be great to discuss it in a central place. The new https://discuss.scientific-python.org/ seems like a good venue for that. Here are two separate forum threads for this: - for the big picture: https://discuss.scientific-python.org/t/a-proposed-design-for-supporting-mu… - for the most tricky design decision (user-facing switch & default behavior): https://discuss.scientific-python.org/t/default-dispatching-behavior-for-su… Thanks in advance for any input! Cheers, Ralf

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Fwd:
by 杨哈哈 Nov. 10, 2021

Nov. 10, 2021

---------- 转发的邮件 --------- 发件人：杨哈哈 <yanghaha217(a)gmail.com> 日期：2021年11月10日周三下午10:08 主题：收件人： <scikit-learn(a)python.org> Excuse me, I'm a Chinese student, Yang.I want to cite sklearn package in my paper, because it's very efficient to machine learning. But I'm doubt about references in official webpage. For example, the references in '*https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC <https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#skle…>*' is *LIBSVM:A Library for Support Vector Machines* and *Probabilistic outputs for support vector machines and comparison to regularizedlikelihood methods. * May I ask you what are these references for?For the webpage or the package? Or I just need to cite *Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011 *in* 'https://scikit-learn.org/stable/about.html#citing-scikit-learn <https://scikit-learn.org/stable/about.html#citing-scikit-learn>'?* I would be appreciated if you can reply me.

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scikit-learn office hours on Monday Nov. 8 2021
by Guillaume Lemaître Nov. 7, 2021

Nov. 7, 2021

Hi all, Some of us will be online on the scikit-learn discord this Monday at 11:00 ET / 16:00 UTC / 17:00 CET for about an hour or so. First time and occasional contributors are welcome to join us to discord using this invitation link: https://discord.gg/YBdN45kD <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 <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 <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! -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/

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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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