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Scikit-learn: Machine Learning in Python

Authors:

Fabian Pedregosa,

Gaël Varoquaux,

Alexandre Gramfort,

Vincent Michel,

Bertrand Thirion,

Olivier Grisel,

Mathieu Blondel,

Peter Prettenhofer,

Vincent Dubourg,

Jake Vanderplas,

Alexandre Passos,

David Cournapeau,

Matthieu Brucher,

Matthieu Perrot,

Édouard DuchesnayAuthors Info & Claims

The Journal of Machine Learning Research, Volume 12

Pages 2825 - 2830

Published: 01 November 2011 Publication History

Abstract

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

References

[1]

D. Albanese, G. Merler, S.and Jurman, and R. Visintainer. MLPy: high-performance python package for predictive modeling. In NIPS, MLOSS Workshop, 2008.

[2]

C.C. Chang and C.J. Lin. LIBSVM: a library for support vector machines. http://www.csie. ntu.edu.tw/cjlin/libsvm, 2001.

[3]

P.F. Dubois, editor. Python: Batteries Included, volume 9 of Computing in Science & Engineering. IEEE/AIP, May 2007.

[4]

R.E. Fan, K.W. Chang, C.J. Hsieh, X.R. Wang, and C.J. Lin. LIBLINEAR: a library for large linear classification. The Journal of Machine Learning Research, 9:1871-1874, 2008.

[5]

J. Friedman, T. Hastie, and R. Tibshirani. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1):1, 2010.

[6]

I. Guyon, S. R. Gunn, A. Ben-Hur, and G. Dror. Result analysis of the NIPS 2003 feature selection challenge, 2004.

[7]

M. Hanke, Y.O. Halchenko, P.B. Sederberg, S.J. Hanson, J.V. Haxby, and S. Pollmann. PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7(1):37-53, 2009.

[8]

T. Hastie and B. Efron. Least Angle Regression, Lasso and Forward Stagewise. http://cran. r-project.org/web/packages/lars/lars.pdf, 2004.

[9]

V. Michel, A. Gramfort, G. Varoquaux, E. Eger, C. Keribin, and B. Thirion. A supervised clustering approach for fMRI-based inference of brain states. Patt Rec, page epub ahead of print, April 2011.

[10]

K.J. Milmann and M. Avaizis, editors. Scientific Python, volume 11 of Computing in Science & Engineering. IEEE/AIP, March 2011.

[11]

S.M. Omohundro. Five balltree construction algorithms. ICSI Technical Report TR-89-063, 1989.

[12]

V. Rokhlin, A. Szlam, and M. Tygert. A randomized algorithm for principal component analysis. SIAM Journal on Matrix Analysis and Applications, 31(3):1100-1124, 2009.

[13]

T. Schaul, J. Bayer, D. Wierstra, Y. Sun, M. Felder, F. Sehnke, T. Rückstieß, and J. Schmidhuber. PyBrain. The Journal of Machine Learning Research, 11:743-746, 2010.

[14]

S. Sonnenburg, G. Rätsch, S. Henschel, C.Widmer, J. Behr, A. Zien, F. de Bona, A. Binder, C. Gehl, and V. Franc. The SHOGUN machine learning toolbox. Journal of Machine Learning Research, 11:1799-1802, 2010.

[15]

S. Van der Walt, S.C Colbert, and G. Varoquaux. The NumPy array: A structure for efficient numerical computation. Computing in Science and Engineering, 11, 2011.

[16]

T. Zito, N. Wilbert, L. Wiskott, and P. Berkes. Modular toolkit for data processing (MDP): A Python data processing framework. Frontiers in Neuroinformatics, 2, 2008.

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cover image The Journal of Machine Learning Research

The Journal of Machine Learning Research Volume 12, Issue

2/1/2011

3426 pages

ISSN:1532-4435

EISSN:1533-7928

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JMLR.org

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Published: 01 November 2011

Published in JMLR Volume 12

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