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 2015, 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

  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". scikit-learn.org. Retrieved 23 March 2015.  External link in |publisher= (help)

External links