Welcome to our new sponsor @probabl.ai !

There is a subtle, but important, difference between metrics and scorers in scikit-learn. Understanding this difference really matters, especially when you want to apply ML in business with custom metrics. So we made a new tutorial on the topic, viewable here: youtu.be/67KMDSitw1M

The difference between metrics and scorers

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The page for the `train_test_split` function is the most visited page on the scikit-learn documention. It's because people always forget the order of the output. So we figured it might make sense to make a video that helps you remember: youtu.be/wb4bKSHiDik

Remembering the order of the train_test_split

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When you cross-validate, you're usually using stratified sampling. It's a default that comes with scikit-learn, and it certainly has an upside ... but also a downside. We try to explain why in our latest whiteboard video on YouTube. www.youtube.com/watch?v=EpK9...

The ups and downs of stratified cross-validation

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We introduced a new tool last week that helps you immediately grab all the right metrics and charts for your pipeline. It comes with many sensible defaults and can be customised too. If you're keen to save some time, feel free to check the demo on YT: youtu.be/2rjkrR7CjHc

Introducing the EstimatorReport

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We will be going live in a bit, if you're keen to learn about prefect, feel free to join! youtube.com/live/hkm5Ofi...

Exploring prefect for scheduling data workloads

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We have a new whiteboard video! This one is all about the classification report and we explain how it works in detail by leveraging a custom widget to change some of the values by hand. You can watch it on our YT channel here: youtu.be/765qaIk30Rs

Playing with the classification report

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🚀 Meet skore—the @scikit-learn.org sidekick! 🔹 Offers guidance on modeling 🔹 Automated reports & key metrics 💡 Built by scikit-learn maintainers. Open-source & ready to use! Try it 👉 tinyurl.com/bdhszwtn

Another week, another livestream. This time we will have a look at prefect. It's a modern scheduling tool for Python that seems to have some nice features for ML training. Join us here on Friday: youtube.com/live/hkm5Ofi...

Exploring prefect for scheduling data workloads

YouTube video by probabl

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We are going live in a bit. If you're keen to see some parallel techniques in Python, or want to learn about Modal/joblib, this one is for you. www.youtube.com/live/0SXyIcs...

More Python parallel stuff with joblib-modal

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Looking forward to speaking on the #OpenSourceAI panel at the sold out EU Open Source Policy Summit by @openforumeurope.org in two weeks. Rather than discussing the definition, we’ll focus on how #openness contributes to European competitiveness—or perhaps, how it might help Europe catch up.

The livestream this week will be about joblib and some big time cloud parallelism that it can support these days via a Modal integration. If you want to learn about Modal or joblib, this should be a super fun one. www.youtube.com/live/0SXyIcs...

We're going live in a bit with a new livestream. In this one we're giving ModernBERT a spin. It is very early days for the project, but there are some interesting milestones already. Join here at 12:30 CET: youtube.com/live/ZWo6Q85...

The first whiteboard video of the year talks about dummy models. The name might not imply it, but using these "dumb" models can be a very smart thing to do at times. youtu.be/0553mUt3O-E

Dumb models can be very smart

YouTube video by probabl

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We are back with livestreams this week and this Friday we will explore ModernBERT, a new pragmatic BERT model. It is very early for the project, which seems like a fun time to dip our toes! Join us on Friday during lunch here: youtube.com/live/ZWo6Q85...

Rather than predictions, here are my professional wishes for 2025—shaped by a year of profound learning, building mission-driven @probabl.ai and data science commons with and around @scikit-learn.org, and engaging with brilliant minds, both enthusiastic and concerned about AI: #openness

Our Probablrs Starter Pack is up! go.bsky.app/JZXryZd

Our final whiteboard video of the year is about a detail, a tiny tiny detail, that relates to the precision and recall metrics. These two metrics are typically different, but sometimes they are equal. And something interesting is happening when that is the case! youtu.be/Zn2xrf4ijSY

When precision equals recall

YouTube video by probabl

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We are going offline during the holiday weeks, which is also why we are going to have a special edition of the livestream to wrap up the year. Feel free to ask us anything about ML as we also review some certification preparations. www.youtube.com/watch?v=f9G7...

Ask me Anything[tm] and a certification review!

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Our new handle is @probabl.ai :)

Going live in a bit! www.youtube.com/watch?v=U3Fi...

Making a recommender by just using Polars!

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We're going to dive into some metrics in the next few whiteboard videos. After all, if you are going to use them to judge machine learning models, it is really important to have a good intuition for them. Let's start with precision, recall and F1: youtu.be/3M2Gmuh5mtI

Precision, recall and F1-score

YouTube video by probabl

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Join us this Friday if you're eager to see what it can be like to design a recommender while limiting ourselves to just a DataFrame API. It is somewhat unconventional, but a great excuse to show off a Polars trick or two. www.youtube.com/watch?v=U3Fi...

Making a recommender by just using Polars!

YouTube video by probabl

youtube.com

At Probabl, together with the wider community, we continue our dedicated efforts to support and enhance @scikit-learn.org and its ecosystem. In this post, we provide a retrospective on the work accomplished over the last 6 months and the roadmap for the next 6: papers.probabl.ai/open-source-...

We're going live later today with a livestream that is all about trying to use Rust to speed up scikit-learn utilities. Feel free to join if you want to ask questions here: youtube.com/live/O6beIxz...

New podcast episode! This one is about imbalanced-learn and how the maintainer looks back with some lessons learned. If you are dealing with imbalanced classification use-cases, like fraud, you'll want to listen in on this one! youtu.be/npSkuNcm-Og

Imbalanced-learn: regrets and onwards - with Guillaume Lemaitre, core-maintainer

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We're back with a livestream on Friday! This time around we will explore Rust as a tool in an attempt to make faster scikit-learn utilities. Feel free to join live here: www.youtube.com/live/O6beIxz...

We have a new whiteboard video! This one is all about the optimisers curse, which is a phenomenon that can take place when you have many (!) hyperparameters. youtu.be/vC9sAD-ymhk

The optimisers curse

YouTube video by probabl

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Please help us test the first release candidate for scikit-learn 1.6: pip install scikit-learn==1.6.0rc1 Changelog: scikit-learn.org/1.6/whats_ne... In particular, if you maintain a project with a dependency on scikit-learn, please let us know about any regression.

Version 1.6

Legend for changelogs something big that you couldn’t do before., something that you couldn’t do before., an existing feature now may not require as much computation or memory., a miscellaneous min...

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