scikit-learn

scikit-learn
Original author(s) David Cournapeau
Initial release June 2007 (2007-06)
Stable release
0.18 / September 28, 2016 (2016-09-28)[1]
Repository github.com/scikit-learn/scikit-learn
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.org

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 2016, 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.

See also

References

  1. ↑ "scikit-learn 0.18". Python Package Index.
  2. ↑ 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.
  3. ↑ Dreijer, Janto. "scikit-learn".
  4. ↑ Eli Bressert (2012). SciPy and NumPy: an overview for developers. O'Reilly. p. 43.
  5. ↑ "About Us". Retrieved 23 November 2016.
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