scikit-learn
 |
|
| Original author(s) | David Cournapeau |
|---|---|
| Initial release | June 2007 |
| Stable release |
0.18.2 / 20 June 2017[1]
|
| Repository | github |
| Written in | Python, Cython, C and C++ |
| Operating system | Linux, macOS, Windows |
| Type | Library for machine learning |
| License | BSD License |
| Website | scikit-learn |
Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language.[2] 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.
Overview
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.[3] The original codebase was later rewritten by other developers. Of the various scikits, scikit-learn as well as scikit-image were described as "well-maintained and popular" in November 2012.[4]
As of 2017, scikit-learn is under active development.[5]
Implementation
Scikit-learn is largely written in Python, with some core algorithms written in Cython to achieve 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.
Version History
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[6] got involved and the first public release (v0.1 beta) was published in late January 2010.
- September 2016. scikit-learn 0.18.0
- November 2015. scikit-learn 0.17.0[7]
- March 2015. scikit-learn 0.16.0[7]
- July 2014. scikit-learn 0.15.0[7]
- August 2013. scikit-learn 0.14[7]
See also
References
- ^ "scikit-learn 0.18.2". 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".
- ^ Eli Bressert (2012). SciPy and NumPy: an overview for developers. O'Reilly. p. 43.
- ^ "About Us". Retrieved 23 November 2016.
- ^ "French Institute for Research in Computer Science and Automation". Wikipedia. 2017-01-21.
- ^ a b c d "Release history — scikit-learn 0.19.dev0 documentation". scikit-learn.org. Retrieved 2017-02-27.
External links
- Official website
- scikit-learn on GitHub
- Introduction to Machine Learning with Python. Book based in Scikit-learn
