scikit-learn
| File:Scikit-learn logo.png | |
| Original author(s) | David Cournapeau |
|---|---|
| Initial release | June 2007 |
| Stable release | 0.17 / May 11, 2015[1] |
| 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 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[update].[4]
As of 2015[update], scikit-learn is under active development and is sponsored by INRIA, Telecom ParisTech and occasionally Google (through the Google Summer of Code).[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.
See also
References
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