Among the new features, this new release introduces the TunedThresholdClassifierCV class that can adjust the decision threshold of any binary classifier to assign custom costs or gains to true/false

Hello, Following up on the recent release, there is a 12-minute video with scikit-learn v1.5.0 Highlights available on the YouTube channel: https://youtu.be/mOpU-zremz4 Video major topics include: a) TunedThresholdClassifierCV class positives/negatives. This is especially powerful to implement cost-sensitive learning, in particular when used in conjunction of per-individual side-metadata used in the cost function, thanks to our meta-data routing infrastructure. ( https://scikit-learn.org/stable/auto_examples/model_selection/plot_cost_sens... ) b) new pydata sphinx website theme highlights --- Reshama Shaikh On Tue, May 21, 2024 at 3:04 PM Jérémie du Boisberranger < jeremie.du-boisberranger@inria.fr> wrote:

Hi everyone,

We're happy to announce the 1.5.0 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_releas... and the long list of the changes under https://scikit-learn.org/stable/whats_new/v1.5.html

This release also comes with a new theme for the https://scikit-learn.org website !

This version supports Python versions 3.9 to 3.12.

Thanks to all contributors who helped on this release !

Jérémie, On behalf of the scikit-learn maintainers team.

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