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
In the age of LLMs and foundation models, I still admire the timeless strength of scikit-learn. Clean APIs, solid mathematics, and dependable performance — it proves that great machine learning doesn’t always need to be flashy to be effective.
One of the best library to learn, understand and build the foundation of Machine Learning concepts. Kudos to the scikit team and all individual contributors. 🫡
Foundational to ML!
Its such straightforward tool! Anyone using it will be loving it...
I got to check this out!
scikit-learn models give solid results quickly and tuned logistic regression or random forest itself covers most of the performance, and it avoids unnecessary complexity and cost of deep learning unless really required. Huge respect to the maintainers for sustaining such impact.