Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. 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. 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.
As of 2017, scikit-learn is under active development.
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 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
- March 2015. scikit-learn 0.16.0
- July 2014. scikit-learn 0.15.0
- August 2013. scikit-learn 0.14
See also
- mlpy
- NLTK
- Orange
References
External links
- Official website
- scikit-learn on GitHub
- Introduction to Machine Learning with Python. Book based in Scikit-learn
