Scikit-learn: Difference between revisions
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The original codebase was later rewritten by other developers. In 2010 Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent Michel, all from [[INRIA]] took leadership of the project and made the first public release |
The original codebase was later rewritten by other developers. In 2010 Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent Michel, all from [[INRIA]] took leadership of the project and made the first public release on February the 1st 2010<ref> http://scikit-learn.org/stable/about.html#history</ref>. Of the various scikits, scikit-learn as well as [[scikit-image]] were described as "well-maintained and popular" {{As of|2012|11|alt=in November 2012}}.<ref>{{cite book |
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|author=Eli Bressert |
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|title=SciPy and NumPy: an overview for developers |
|title=SciPy and NumPy: an overview for developers |
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Revision as of 13:19, 25 July 2017
| File:Scikit-learn logo.png | |
| Original author(s) | David Cournapeau |
|---|---|
| Initial release | June 2007 |
| Stable release | 0.18.2
/ 20 June 2017[1] |
| Repository | |
| 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. In 2010 Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent Michel, all from INRIA took leadership of the project and made the first public release on February the 1st 2010[4]. Of the various scikits, scikit-learn as well as scikit-image were described as "well-maintained and popular" in November 2012[update].[5]
As of 2017[update], scikit-learn is under active development.[6]
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[7] 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[8]
- March 2015. scikit-learn 0.16.0[8]
- July 2014. scikit-learn 0.15.0[8]
- August 2013. scikit-learn 0.14[8]
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".
- ^ http://scikit-learn.org/stable/about.html#history
- ^ 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