scikit-learn

📍 The place to be next week - 2 days packed with data science in Paris! We’re excited to bring together the scikit-learn community along with leaders from both technology and business for the very first edition of Probability 1.0, a scikit-learn–centric event designed to share insights, best practices, and real-world impact. A huge thank you to the organiser :probabl. and to our partners Quansight, BNP Paribas, CHANEL, and Inria for making this premiere edition possible. 🎉 Looking forward to two days of learning, collaboration, and open-source excellence.

That’s BIG for Open Source AI and our scikit-learn project! 💥 Congrats to :probabl., the community and its incredible dynamism. This milestone shows how far open source machine learning has come, and how much stronger we are when we build together.

🌱 When open source sparks entrepreneurship: scikit-learn as a launchpad As a community-driven open-source library, scikit-learn has become the foundation for a dynamic ecosystem of entrepreneurs and ventures. ✨ :probabl. created by scikit-learn core developers, helping organizations scale their data science, selling data science expertise while contributing back to open source through skrub, skore, scikit-learn and many more... 🔗 https://probabl.ai and very recently: ✨ Skfolio Labs — co-founded by Hugo Delatte and Daniel Farrell, contributing and supporting Skfolio, an open-source library built on top of scikit-learn, specializing in portfolio optimization and risk management. 🔗 https://skfoliolabs.com 📢 Read the official announcements: Hugo’s post 👉 https://lnkd.in/ePwyU75u Daniel’s post 👉 https://lnkd.in/eqXHjP5r We’re excited to see new projects emerging that extend scikit-learn into specialized domains, showing how open-source innovation can spark sustainable ventures and empower whole industries. #scikitlearn #OpenSource #MachineLearning #DataScience #QuantFinance

A bunch of scikit-learn core contributors will attend or speak at PyData Paris 2025 on Tuesday and Wednesday next week. There is still time to grab a seat to attend the main conference talks and maybe even present a lightning talk ;) Ticketing, practical infos and schedule at: https://lnkd.in/epawxTkH We will also participate in the sprint day on Thursday the 2nd, but unfortunately that day has limited seats and there is already a growing dedicated waitlist. Looking forward to meeting you there, exchanging with experts of all the fields of data science, learning more about your use cases and collecting feedback for the future developments of our library and related projects!

scikit-learn was honored to be selected to participate in Cohort 2 of the GitHub Secure Open Source Fund (OSF) Training Program. It was an intense 3-week training program, with over 90 open source maintainers joining the training. There were numerous workshops delivered by experts at the GitHub Security Lab. ⭐️ For many of these workshops, the learning materials are publicly available, and they are shared in the article here. 🙏Thank you to Gregg Cochran, Kevin Crosby, all the instructors at GitHub Security Lab, and the funders for this pivotal opportunity and support of open source projects. #opensource #security #machinelearning https://lnkd.in/emA_yx6a

The temperature scaling PR scikit-learn/scikit-learn/31068 that Christian Lorentzen and I worked on has finally been merged into scikit-learn’s main branch. Probability calibration with temperature scaling will be available in scikit-learn 1.8. I’d like to thank Olivier Grisel, Christian Lorentzen, and Omar Salman for guiding me through the entire process. For every minute I spent working on the PR, they spent hours reviewing it to ensure algorithmic efficiency, numerical stability, correct unit test implementation, and rigorous documentation. I'd also like to thank David Holzmüller for his hard work in convincing the scikit-learn core maintainers that temperature scaling is a valuable addition to the library --- the merge wouldn’t have been possible without him --- and Adrin J. for his confident vote of support for me to open the PR. https://lnkd.in/gmv2K_gc

🔥 Many of you asked for training after we launched the official scikit-learn certifications, so at Probabl, we went further and for FREE. We’re proud to introduce Skolar, a new platform for hands-on, structured and certified training in open-source data science. 📚 All content is created by scikit-learn core contributors — the very people who build and maintain the tools and documentations you use every day. Skolar builds on things we’ve learned from the scikit-learn MOOC with Inria and certifications — and pushes it further. We designed it for people who want practical skills, real expertise, and the ability to go beyond the basics. 🧠 What’s inside Skolar: ✔️ Hands-on exercises and notebooks running directly in the browser ✔️ Quizzes to test your knowledge ✔️ Video lessons and expert-led tutorials ✔️ A growing library of structured, real-world training materials Start today with our free and open-source course: Scikit-learn Associate Practitioner Online Course Create your account now: 👉 skolar.probabl.ai See the main features: 👉 https://lnkd.in/euaPaVbe   Learn more about this project: 👉 https://lnkd.in/epc2TWb4 #datascience #machinelearning #opensource #scikitlearn #education #careerdevelopment #AI

🚀 scikit-learn 1.7 is out 🚀 A big shoutout to the community of contributors who continue to push open-source machine learning forward ❤️ ✨ Key Highlights: ▶️ Improved estimator’s HTML representation ▶️ Custom validation set for histogram-based Gradient Boosting estimators ▶️ Plotting ROC curves from cross-validation results ▶️ Updated Array API support ▶️ Improved API consistency of Multi-layer Perceptron ▶️ Migration toward sparse array 🔗 Check the full release highlights: https://lnkd.in/eZx9DJx7 Discover scikit-learn 1.7 and its: 🟢 9 new features 🔵 2 efficiency improvements & 4 enhancements 🟡 6 API changes 🔴 7 fixes 👥 146 contributors (thank you all!) 📖 More details in the changelog: https://lnkd.in/eySABsYH You can upgrade with pip as usual: pip install -U scikit-learn Using conda-forge builds: conda install -c conda-forge scikit-learn #scikitlearn #MachineLearning #opensource #DataScience #Python #ML

It’s hard to believe that it’s been over six years since I joined the scikit-learn team as a maintainer. As of today, I have 1,374 commits and reviewed 3,179 pull requests. Behind these numbers, I am grateful for all the thoughtful discussions I have had with the community to push scikit-learn forward. Now that I look over my commits, I want to highlight some feature areas I am particularity fond of: 1. Everything Trees 🌲🌲🌲 - Native categorical support in Histogram-based Gradient Boosting Trees - Native missing value support in Random Forest & Trees - Cost complexity pruning In Trees 2. DataFrame interoperability 🖼️ - Pandas and Polars DataFrame output with the set_output API - get_feature_names_out: Mapping input feature names to output feature names 3. Preprocessing 🕰️ - TargetEncoder: Use the target to encode categorical data - Group infrequent categories in OrdinalEncoder and OneHotEncoder - KNN-based missing value imputation 4. Visualizations 📊 - HTML Representation to visualize estimators in Jupyter notebooks - Plotting API for evaluating or inspecting estimators 5. Experimental GPU support 🏎️ - Integrate Array API to run natively with PyTorch or CuPy arrays on a GPU I hope you found some of these features useful or discovered some of them here 😁. https://lnkd.in/ee_t5WRw