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scikit-learn

scikit-learn

Software Development

Open Source library for Machine Learning in Python.

About us

scikit-learn is an Open Source library for machine learning in Python.

Website
https://scikit-learn.org
Industry
Software Development
Company size
2-10 employees
Type
Nonprofit

Employees at scikit-learn

Updates

  • scikit-learn reposted this

    I’m thrilled to announce that I’m stepping up as :probabl.’s CSO (Chief Science Officer) to supercharge scikit-learn and its ecosystem, pursuing my dreams of tools that help go from data to impact. scikit-learn is central to data-scientists’ work. It has grown over more than a decade, supported by volunteers’ time, donations, and grant funding, with a central role of Inria. With :probabl.’s recent seed funding, we have a unique opportunity to accelerate scikit-learn’s development. Our analysis is that to build best on scikit-learn, enterprises need dedicated tooling and partners. Part of scikit-learn’s success has always been to nurture an ecosystem, for instance via its simple API that has become a standard. :probabl. is not only consolidating scikit-learn, but also this ecosystem: the skops project, to put scikit-learn based models in production, the skrub project, that facilitates data preparation. We have an amazing team at :probabl.. Many old-time scikit-learn contributors (Adrin J., Guillaume Lemaitre, Jérémie Du Boisberranger -not on Linkedin-, Loïc Estève, Olivier Grisel) are joined by new contributors (David Arturo Amor Quiroz, François Goupil, Stefanie Senger, and more to come). Working directly with businesses gives us an acute understanding of where the ecosystem can be improved, and I profoundly enjoy working with our top management, François MÉRO and Yann Lechelle, whose intimate understanding of business is very complementary to my background, and our broader :probabl. team, bringing a score of new skills and thinking. As CSO at :probabl., my role will be to nourish our development strategy with understanding of machine learning, data science and open source. Making sure that scikit-learn and its ecosystem are enterprise ready, will bring resources for scikit-learn’s sustainability, enabling its ecosystem to grow into a standard-setting platform for the industry.

  • scikit-learn reposted this

    A new version of scikit-learn is out! And I'm proud that my PR #32100 is among the key highlights of this release 😁 (faster decision trees when using the absolute error). I'd like to thank the reviewers once again for their very thorough reviews! Thanks to them (and thanks to the tests too!), I'm confident that I delivered reliable and maintainable code. The PR: https://lnkd.in/eeEahkv9 The technical report: https://lnkd.in/eWXcSNFv You’ll find a detailed analysis of the different algorithms I considered to address the efficiency challenges of fitting decision trees with the absolute error.

    View organization page for scikit-learn

    122,884 followers

    🚀 scikit-learn 1.8 is out 🚀 A big shoutout to the community of contributors who continue to push open-source machine learning forward ❤️ ✨ Key Highlights: ▶️ Expanded Array API support (including PyTorch & CuPy) to run more estimators and metrics on GPUs ▶️ Free-threaded CPython 3.14 support for better multi-threaded performance ▶️ Probability calibration with temperature scaling in CalibratedClassifierCV ▶️ Major efficiency boosts in linear models (Lasso / ElasticNet with gap safe screening) ▶️ Much faster and more robust DecisionTreeRegressor with criterion="absolute_error" ▶️ New manifold.ClassicalMDS implementation for classical multidimensional scaling 🔗 Check the full release highlights: https://lnkd.in/gkXQSbmZ Discover scikit-learn 1.8 and its: 🟢 28 new features 🔵 12 efficiency improvements & 13 enhancements 🟡 9 API changes 🔴 34 fixes 👥 193 contributors (thank you all!) 📖 More details in the release notes: https://lnkd.in/gCrs42se 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

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  • View organization page for scikit-learn

    122,884 followers

    scikit-learn in numbers: 🟠 Downloads: 3.5 billions 🔵 GitHub: 26.5K forked repos; 64.3K stars: https://lnkd.in/dwncfBb7 🟠 Kaggle State of Data Science: scikit-learn consistently ranks as the top machine learning framework 🔵 Monthly website visitors: 1.1 Million unique visitors 🤩 Please consider sponsoring us via GitHub Sponsors ➡️ https://lnkd.in/eYwBG9Yq #GitHub #datascience #machinelearning #opensource

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  • 🚀 scikit-learn 1.8 is out 🚀 A big shoutout to the community of contributors who continue to push open-source machine learning forward ❤️ ✨ Key Highlights: ▶️ Expanded Array API support (including PyTorch & CuPy) to run more estimators and metrics on GPUs ▶️ Free-threaded CPython 3.14 support for better multi-threaded performance ▶️ Probability calibration with temperature scaling in CalibratedClassifierCV ▶️ Major efficiency boosts in linear models (Lasso / ElasticNet with gap safe screening) ▶️ Much faster and more robust DecisionTreeRegressor with criterion="absolute_error" ▶️ New manifold.ClassicalMDS implementation for classical multidimensional scaling 🔗 Check the full release highlights: https://lnkd.in/gkXQSbmZ Discover scikit-learn 1.8 and its: 🟢 28 new features 🔵 12 efficiency improvements & 13 enhancements 🟡 9 API changes 🔴 34 fixes 👥 193 contributors (thank you all!) 📖 More details in the release notes: https://lnkd.in/gCrs42se 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

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  • 📍 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.

    View organization page for :probabl.

    12,935 followers

    Announcing Probability 1.0 — A New Flagship Event for Open-Source Data Science and scikit-learn 🎟️ “We’re giving away 10 FREE tickets! Comment “probability 1.0” below to enter the draw. As part of AdoptAI, we are thrilled to share that :probabl. and scikit-learn are launching the very first edition of Probability 1.0, our annual event dedicated to the future of open-source machine learning featuring scikit-learn and its entire ecosystem. 📅 November 25–26, 2025 📍 Le Grand Palais, Paris, booth T2 Tech Demo Zone As part of Adopt AI — 25,000+ attendees, 500+ speakers, 10 stages, 250+ exhibitors https://lnkd.in/eDz5MzSE What to expect: 🎤 Talks from leading contributors to scikit-learn and the broader ecosystem, see the schedule: https://lnkd.in/eDz5MzSE 🤝 Partner sessions, collaborations, and high-value networking opportunities 🟧 A dedicated meeting area with table tops for deeper exchanges 🟦 A speaker zone Thanks to our partners for this event Inria, CHANEL, BNP Paribas, Quansight #ProbabilityOne #DataScience #MachineLearning #OpenSource #scikitlearn #Probabl #OwnYourDataScience

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  • scikit-learn reposted this

    Machine Learning doesn’t have to be hard. ML Model of the Week: DBSCAN Unlike K-Means, which forces clusters into predefined shapes, DBSCAN finds clusters of any shape by focusing on density. Points in sparse areas are marked as outliers. Applications include: - Geospatial clustering (traffic hotspots, disease mapping) - Anomaly detection (fraud, rare behaviors) - Recommendation systems (e.g., grouping movies into “cult classics,” “blockbusters,” or niche indie films, while marking truly unique ones as outliers) Visualization 1: DBSCAN detects clusters by density: dark points are cores, lighter points are borders, and gray points are noise. No predefined cluster shapes needed. Visualization 2: Density Map (KDE) Highlights areas of high point density that DBSCAN interprets as clusters, with low-density regions naturally marked as noise. It’s a clear view of the “landscape” DBSCAN sees. Visualization 3: k-Distance Plot Shows the distance to each point’s 5th nearest neighbor. The sharp increase (elbow) suggests the optimal ε value — the radius DBSCAN uses to define dense neighborhoods. Takeaway: DBSCAN doesn’t just cluster — it identifies shape and density, making it ideal for real-world, messy, or non-linear data. #matplotlib #sklearn #ml #python #jupyterlab scikit-learn

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  • 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.

    View organization page for :probabl.

    12,935 followers

    🚀 We are proud to announce a €13M #seed round to raise the bar on enterprise AI adoption, starting with open source machine learning As Inria’s spin-off and official operator of scikit-learn (2.5 billion downloads), we aim to turn artisanal data science into industrial-grade infrastructure — reliable, traceable, and sovereign. ✨🌟 This round was co-led by Serena and Capital Fund Management (CFM), with renewed support from Mozilla Ventures and #FrenchTechSouveraineté operated by Bpifrance under the #France2030 plan. Welcome onboard and thank you for binding with our vision. 💪 And of course, here's the glimpse at the hearts and souls building core open source AI technology and value adding enterprise software. 🙏👇

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  • View organization page for scikit-learn

    122,884 followers

    🌱 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

    I’m very excited to share that, together with Hugo Delatte, we have co-founded Skfolio Labs!   Skfolio Labs is the company behind skfolio, the open source portfolio optimization and risk management library. We provide enterprise support, bespoke extensions and implementation guidance for teams integrating skfolio or building with it.   We strongly believe that open source is a strategic advantage for users and developers. It removes vendor lock-in, promotes best practices and enables rapid iteration.   Our aim is to build a company around skfolio whilst staying true to the open source principles that have allowed the library to thrive since its release in 2024. It’s been exciting to see skfolio already being adopted across a wide variety of applications and I’m looking forward to helping more teams put it to work in production.   Over the years I’ve been fortunate to work with some exceptional people and to help build trading desks at some of the most ambitious firms in our space. I’m incredibly grateful for the lessons, support and guidance I’ve received along the way. As long as I can remember all the smart advice I've been given(!), I’m confident that Hugo and I can build something to be proud of.   Very excited to reconnect with my network and explore new opportunities together!

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