🌐 Mailman 3 March 2023 - 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

5 12

[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.

13 19

CFP: GitHub copilot for PRs
by Adrin March 25, 2023

March 25, 2023

What do we think of GitHub copilot for PRs? Had anybody tried it? Is it something we think is a good idea at this point? I'm gonna try it on some smaller repos and see what it does. https://github.com/features/preview/copilot-x Cheers, Adrin

3 2

VOTE: Governance update: elevating voting rights
by Adrin March 14, 2023

March 14, 2023

Hi, Since SLEP020 <https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep020…> updates to the governance model don't require a separate SLEP, and can be done through a pull request on the repo. This PR <https://github.com/scikit-learn/scikit-learn/pull/25753> introduces certain changes to the governance, which in effect is elevating voting rights of two existing groups: Contributor experience team and the communication team. It also renames "core developers" team to "maintainers", and puts them with the above two teams in a "Core contributors" group. According to our governance, we need to call a vote for any such changes, hence I'm calling for a vote. Please vote on the pull request <https://github.com/scikit-learn/scikit-learn/pull/25753/files>. The vote will conclude in a month, and we need a 2/3 majority of the cast vote to pass the motion. Regards, Adrin

1 0

classification model that can handle missing values w/o learning from missing values
by Martin Gütlein March 10, 2023

March 10, 2023

Hi, I am looking for a classification model in python that can handle missing values, without imputation and "without learning from missing values", i.e. without using the fact that the information is missing for the inference. Explained with the help of decision trees: * The algorithm should NOT learn whether missing values should go to the left or right child (like the HistGradientBoostingClassifier). * Instead it could built the prediction for each child node and aggregate these (like some Random Forest implementations do). If that is not possible in sci-kit learn, maybe you have already discussed this? Or you know of a fork of sci-kit learn that is able to do this, or some other python library? Any help would be really appreciated, kind regards, Martin P.S. Here is my use-case, in case you are interested: I have a binary classification problem with a positive and a negative class, and two types of features A and B. In my training data, I have a lot more data (90%) where B is missing. In my test data, I always have B, which is good because the B features are better than the A features. In the cases where B is present in the training data, the ratio of positive examples is much higher than when its missing. So what HistGradientBoostingClassifier does, it uses the fact that B is not missing in the test data, and predicts way too many positives. (Additionally, some feature values of type A are also often missing)

3 6

[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/

3 3

New core developer: Tim Head
by Christian Lorentzen March 8, 2023

March 8, 2023

Dear all I'm very excited to announce that Tim Head, https://github.com/betatim, is joining scikit-learn as core developer. Congratulations and a warm welcome Tim! on behalf of the scikit-learn team Christian

5 4

scikit-learn monthly developer meeting: Monday March 27, 2023
by Thomas J. Fan March 7, 2023

March 7, 2023

Dear all, The scikit-learn developer monthly meeting will take place on Monday March 27 at 11:00 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=2023&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

1 0

obtaining intervals from the decision tree struture
by Sole Galli March 7, 2023

March 7, 2023

Hello, I would like to obtain final intervals from the decision tree structure. I am not interested in every node, just the limits that take a sample to a final decision /leaf. For example, if the tree structure is this one: |--- feature_0 <= 0.08 | |--- class: 0 |--- feature_0 > 0.08 | |--- feature_0 <= 8.50 | | |--- feature_0 <= 1.50 | | | |--- class: 1 | | |--- feature_0 > 1.50 | | | |--- class: 1 | |--- feature_0 > 8.50 | | |--- feature_0 <= 60.25 | | | |--- class: 0 | | |--- feature_0 > 60.25 | | | |--- class: 0 Then, I would like to obtain these limits: 0-0.08 ; 0.08-1.50; 1.50-8.50 ; 8.50-60; >60 Potentially as the following numpy array: [-np.inf, 0.08, 1.5, 8.5, 60, np.inf] Is it possible? I have a stackoverflow question here for more details and code https://stackoverflow.com/questions/75663472/how-to-obtain-the-interval-lim… Thank you! Sole Sent with [Proton Mail](https://proton.me/) secure email.

2 1