|Original author(s)||David Cournapeau|
|Initial release||June 2007|
|Written in||Python, Cython, C and C++|
|Operating system||Linux, macOS, Windows|
|Type||Library for machine learning|
|License||New BSD License|
Scikit-learn (formerly scikits.learn and also known as sklearn) is a free software 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.
The scikit-learn project started as scikits.learn, a Google Summer of Code project by David Cournapeau. Its name stems from the notion that it is a "SciKit" (SciPy Toolkit), a separately-developed and distributed third-party extension to SciPy. The original codebase was later rewritten by other developers. In 2010 Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent Michel, all from the French Institute for Research in Computer Science and Automation in Rocquencourt, France, took leadership of the project and made the first public release on February the 1st 2010. Of the various scikits, scikit-learn as well as scikit-image were described as "well-maintained and popular" in November 2012[update]. Scikit-learn is one of the most popular machine learning libraries on GitHub.
Scikit-learn is largely written in Python, and uses numpy extensively for high-performance linear algebra and array operations. Furthermore, some core algorithms are written in Cython to improve performance. Support vector machines are implemented by a Cython wrapper around LIBSVM; logistic regression and linear support vector machines by a similar wrapper around LIBLINEAR. In such cases, extending these methods with Python may not be possible.
Scikit-learn was initially developed by David Cournapeau as a Google summer of code project in 2007. Later Matthieu Brucher joined the project and started to use it as a part of his thesis work. In 2010 INRIA, the French Institute for Research in Computer Science and Automation, got involved and the first public release (v0.1 beta) was published in late January 2010.
- May 2019. scikit-learn 0.21.0
- September 2018. scikit-learn 0.20.0
- July 2017. scikit-learn 0.19.0
- September 2016. scikit-learn 0.18.0
- November 2015. scikit-learn 0.17.0
- March 2015. scikit-learn 0.16.0
- July 2014. scikit-learn 0.15.0
- August 2013. scikit-learn 0.14
- "scikit-learn release history".
- "scikit-learn 0.21.1". Python Package Index.
- Fabian Pedregosa; Gaël Varoquaux; Alexandre Gramfort; Vincent Michel; Bertrand Thirion; Olivier Grisel; Mathieu Blondel; Peter Prettenhofer; Ron Weiss; Vincent Dubourg; Jake Vanderplas; Alexandre Passos; David Cournapeau; Matthieu Perrot; Édouard Duchesnay (2011). "Scikit-learn: Machine Learning in Python". Journal of Machine Learning Research. 12: 2825–2830.
- Dreijer, Janto. "scikit-learn".
- "About us — scikit-learn 0.20.1 documentation". scikit-learn.org.
- Eli Bressert (2012). SciPy and NumPy: an overview for developers. O'Reilly. p. 43.
- "The State of the Octoverse: machine learning". The GitHub Blog. GitHub. 2019-01-24. Retrieved 2019-10-17.
- "Release History - 0.21.0 documentation". scikit-learn. Retrieved 5 May 2019.
- "Release History - 0.20.0 documentation". scikit-learn. Retrieved 6 November 2018.
- "Release history — scikit-learn 0.19.dev0 documentation". scikit-learn.org. Retrieved 2017-02-27.