scikit-learn
Sign in to saveAlso known as scikits.learn, sklearn, scikit
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Scikit-learn is a NumFOCUS fiscally sponsored project.
Key facts
- Software.name
- scikit-learn
- Software.logo
- Scikit learn logo small.svg
- Software.author
- David Cournapeau
- Software.developer
- Google Summer of Code project
- Software.programming language
- Python, Cython, C and C++
- Software.operating system
- Linux, macOS, Windows
- Software.genre
- Library for machine learning
- Software.license
- New BSD License
via Wikipedia infobox
Source code
scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license. The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the About us page for a list of core contributors. We welcome new contributors of all experience levels. The scikit-learn community goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation, tests, and more. We've included some basic information in this README. Random number generation can be controlled during testing by setting the SKLEARN SEED environment variable. The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the About us page for a list of core contributors.
Excerpt from the source-code README · 7,519 chars · not written by Vinony