Mailman 3 October 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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Pandas DataFrame output is now available for all sklearn transformers
by Reshama Shaikh Nov. 2, 2022

Nov. 2, 2022

Hello, Pandas DataFrame output is now available for all sklearn transformers (in dev version 1.2)! This will make running pipelines on data frames much easier, and provides better ways to track feature names. There is a 14-minute video with examples, some more information and some FAQs answered at the end [a]. This is one of the biggest improvements in scikit-learn in a long time and we'd love your feedback! Please try out the nightly built and give it a go. We'd love to hear both about whether this helps your use cases and any bugs you find. A special thanks to the maintainers: Thomas J. Fan, Guillaume LeMaitre, Christian Lorentzen ! [a] video https://youtu.be/J4KCu9WWDTo [b] example https://scikit-learn.org/dev/auto_examples/miscellaneous/plot_set_output.ht… [c] LinkedIn post https://www.linkedin.com/feed/update/urn:li:activity:6987027021608460289/?a… --- Reshama Shaikh she/her

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Inquiry on Genetic Algorithm
by Ellarizza Fredeluces Oct. 30, 2022

Oct. 30, 2022

Dear Scikit-Learn developers, First of all, thank you for your brilliant work. I would like to ask if a genetic algorithm is available in scikit-learn. I tried to search, but I only found this one <https://pypi.org/project/sklearn-genetic/#:~:text=sklearn-genetic%20is%20a%….>. I also checked your website but there seems to be no genetic algorithm yet. Your reply will be highly appreciated. Thank you again. Sincerely, Ella

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Discrepancy in "Feature importances with a forest of trees" documentation
by Serafeim Loukas Oct. 28, 2022

Oct. 28, 2022

Dear Scikit-learn community, I have been reading some examples in https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importan… about the permutation importance that can be assessed after fitting a tree-based model (e.g. RandomForestClassifier). However, I have noticed a discrepancy that I would like to mention. If a one-hot-encoding step is used before model fitting, the `.feature_importances_` attribute includes importances for all the levels of the transformed categorical features (e.g. for gender, we get 2 importances for males & females, respectively. When I apply the `permutation_importance<https://scikit-learn.org/stable/modules/generated/sklearn.inspection.permut…>` functions though, the outputs correspond to the non-transformed data. To illustrate this, I include a toy example in .py format. Best, Makis

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Decision function for kernel method SVM not reproducible.
by Riko Naka Oct. 17, 2022

Oct. 17, 2022

Hi, guys, the decision function calculation problem of the kernel method SVM has been bothering me for a long time. I calculated this decision function manually, but the return result is different from the function provided in sklearn. This is my issue on github, did I calculate anything wrong? https://github.com/scikit-learn/scikit-learn/issues/24663

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scikit-learn monthly developer meeting: October 31, 2022
by Thomas J. Fan Oct. 12, 2022

Oct. 12, 2022

Dear all, The scikit-learn developer monthly meeting will take place on October 31, 2022 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=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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is Sci_kiet-Learn the right choice for my project
by Mike Oliver Oct. 9, 2022

Oct. 9, 2022

Dear Sirs, I am evaluating SciKit-Learn for a new project. I am hoping to find a AI Machine Learning package that can take a large dataset of objects that have various object types and attributes. These objects are typically related to other objects, such as a server to a Wifi device, or two network routers to each other, etc. When these objects are setup data is gathered about where they are located, what settings there are, the device type, etc. With large organizations there can be thousands of these objects and tens of thousands of relationships, descriptions, settings, etc. My hope is that with machine learning we can detect when an object is missing, or configured in error, or duplicates. The question is, will SciKit-Learn help with this problem? I understand that we will have to train it to identify what to look for and then act on what was found and predicted to be the solution algorithm. Or instructions. Thanks for your help, Great looking product and already have the tutorial up and running and have installed it in my Django platform. Mike

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