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Scikit-learn: Machine Learning in Python

Journal of Machine Learning Research 12 (2011) 2825-2830 Submitted 3/11; Revised 8/11; Published 10/11 scikit - learn : Machine Learning in PythonFabian el INRIA SaclayNeurospin, B at 145, CEA Saclay91191 Gif sur Yvette FranceOlivier rue Soleillet75 020 Paris FranceMathieu University1-1 Rokkodai, NadaKobe 657-8501 JapanPeter at WeimarBauhausstr. 1199421 Weimar GermanyRon Inc76 Ninth AvenueNew York, NY 10011 USAV incent Universit e, IFMA, EA 3867, LaMIBP 10448, 63000 Clermont-Ferrand FranceJake DepartmentUniversity of Washington, Box 351580 Seattle, WA 98195 USAA lexandre LabUMass AmherstAmherst MA 01002 USAD avid Thompson AvenueCambridge, CB3 0FA UKc 2011 Fabian Pedregosa, Ga el Varoquaux, Alexandre Gramfort, Vincent Michel, BertrandThirion, Olivier Grisel, Mathieu Blondel,Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher,Matthieu Perrot and Edouard DuchesnayPEDREGOSA, VAROQUAUX, GRAMFORT ET SA, CSTJF avenue Larribau64000 Pau FranceMatthieu Edouard B at 145, CEA Saclay91191 Gif sur Yvette FranceEditor.

Keywords: Python, supervised learning, unsupervised learning, model selection 1. Introduction The Python programming language is establishing itself as one of the most popular languages for scientific computing. Thanks to its high-level interactive nature and its maturing ecosystem of sci-

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