A shared toolkit for machine learning

A consistent software interface made established learning methods easier to use and compare.

Fabian Pedregosa and colleagues

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The contribution

The scikit-learn paper describes a Python library that brings together supervised and unsupervised learning tools with an emphasis on usability, documentation, and integration with the scientific Python ecosystem.

What this does not establish

This milestone concerns research software and access to methods, rather than a new universal learning algorithm.

Why this date?

2011 is the journal paper date. It is not the software project start date.

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The original work

Scikit-learn: Machine Learning in Python

Authors: Fabian Pedregosa and colleagues
Publication: Journal of Machine Learning Research, 12, 2825–2830
Source type: Journal article

Open-access JMLR article

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Cited By

Atlas entries whose reference lists cite this work or the publication version noted below.

Learning Transferable Visual Models From Natural Language Supervision (2021)
Listed in the source bibliography. Check the reference list.

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Read the indexed bibliography

Reference section 1 (PDF pages 5, 6, 7)

D. Albanese, G. Merler, S.and Jurman, and R. Visintainer. MLPy: high-performance python package for predictive modeling. In NIPS, MLOSS Workshop, 2008. C.C. Chang and C.J. Lin. LIBSVM: a library for support vector machines. http://www.csie. ntu.edu.tw/cjlin/libsvm, 2001. P.F. Dubois, editor. Python: Batteries Included, volume 9 of Computing in Science & Engineering. IEEE/AIP, May 2007. 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. J. Friedman, T. Hastie, and R. Tibshirani. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1):1, 2010. I Guyon, S. R. Gunn, A. Ben-Hur, and G. Dror. Result analysis of the NIPS 2003 feature selection challenge, 2004. 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. 2829 PEDREGOSA, VAROQUAUX, GRAMFORT ET AL. T. Hastie and B. Efron. Least Angle Regression, Lasso and Forward Stagewise. http://cran. r-project.org/web/packages/lars/lars.pdf, 2004. 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. doi: 10.1016/j.patcog.2011.04.006. K.J. Milmann and M. Avaizis, editors. Scientiﬁc Python, volume 11 of Computing in Science & Engineering. IEEE/AIP, March 2011. S.M. Omohundro. Five balltree construction algorithms. ICSI Technical Report TR-89-063, 1989. 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. T. Schaul, J. Bayer, D. Wierstra, Y. Sun, M. Felder, F. Sehnke, T. Ru¨ckstieß, and J. Schmidhuber. PyBrain. The Journal of Machine Learning Research, 11:743–746, 2010. S. Sonnenburg, G. Ra¨tsch, 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. 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. 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. 2830

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