Scikit-learn: Difference between revisions
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{{As of|2015}}, scikit-learn is under active development and is sponsored by [[INRIA]] and occasionally [[Google]] (through the Google Summer of Code).<ref>{{cite web|title=About Us|url=http://scikit-learn.org/0.13/about.html#funding|publisher=http://scikit-learn.org|accessdate=23 March 2015}}</ref> |
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Among its users are [[Evernote]], which uses the library to distinguish recipes from other user posts through a naive Bayes classifier,<ref>{{cite web|title=Stay classified|author=Mark Ayzenshtat|date=22 January 2013|accessdate=4 May 2013|url=http://blog.evernote.com/tech/2013/01/22/stay-classified/|website=Evernote Techblog}}</ref> |
Among its users are [[Evernote]], which uses the library to distinguish recipes from other user posts through a naive Bayes classifier,<ref>{{cite web|title=Stay classified|author=Mark Ayzenshtat|date=22 January 2013|accessdate=4 May 2013|url=http://blog.evernote.com/tech/2013/01/22/stay-classified/|website=Evernote Techblog}}</ref> |
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and [[Mendeley]], which builds [[recommender system]]s from scikit-learn's [[Stochastic gradient descent|SGD]] regression algorithm.<ref>{{cite conference |title=Efficient Top-N Recommendation by Linear Regression |url=http://www.slideshare.net/MarkLevy/efficient-slides |author=Mark Levy |year=2013 |conference=ACM RecSys Large Scale Recommender System workshop}}</ref> |
and [[Mendeley]], which builds [[recommender system]]s from scikit-learn's [[Stochastic gradient descent|SGD]] regression algorithm.<ref>{{cite conference |title=Efficient Top-N Recommendation by Linear Regression |url=http://www.slideshare.net/MarkLevy/efficient-slides |author=Mark Levy |year=2013 |conference=ACM RecSys Large Scale Recommender System workshop}}</ref> |
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Revision as of 15:54, 23 March 2015
| File:Scikit-learn logo.png | |
| Original author(s) | David Cournapeau |
|---|---|
| Initial release | June 2007 |
| Stable release | 0.15.2
/ September 4, 2014[1] |
| Repository | |
| Written in | Python, Cython, C and C++ |
| Operating system | Linux, Mac OS X, Microsoft Windows |
| Type | Library for machine learning |
| License | BSD License |
| Website | scikit-learn |
scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.[2] It features various classification, regression and clustering algorithms including support vector machines, logistic regression, naive Bayes, 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 extensively 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[update].[4]
As of 2015[update], scikit-learn is under active development and is sponsored by INRIA and occasionally Google (through the Google Summer of Code).[5] Among its users are Evernote, which uses the library to distinguish recipes from other user posts through a naive Bayes classifier,[6] and Mendeley, which builds recommender systems from scikit-learn's SGD regression algorithm.[7]
The scikit-learn API has been adopted by wise.io, who offer a proprietary implementation of random forests called wiseRF.[8][9] wise.io's business partner Continuum IO claimed data throughput of up to 7.5 times that of scikit-learn's implementation;[10] since then, the scikit-learn developers claim to have optimized their implementation to be competitive with wise.io's, except in terms of memory use.[11]
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.
See also
References
- ^ Andreas Müller. "scikit-learn 0.15.2". 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 (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". http://scikit-learn.org. Retrieved 23 March 2015.
{{cite web}}: External link in|publisher= - ^ Mark Ayzenshtat (22 January 2013). "Stay classified". Evernote Techblog. Retrieved 4 May 2013.
- ^ Mark Levy (2013). Efficient Top-N Recommendation by Linear Regression. ACM RecSys Large Scale Recommender System workshop.
- ^ "wiserf". wise.io. Retrieved 22 January 2014.
- ^ API design for machine learning software: experiences from the scikit-learn project. ECML PKDD Workshop on Languages for Machine Learning. 2013.
{{cite conference}}: Cite uses deprecated parameter|authors=(help) - ^ Joseph W. Richards (27 November 2012). "wiseRF Use Cases and Benchmarks". Continuum IO. Retrieved 22 January 2014.
- ^ Gaël Varoquaux (8 August 2013). "Scikit-learn 0.14 release: features and benchmarks". Retrieved 22 January 2014.