Scikit-learn: Machine Learning in Python
Fabián Pedregosaa b, Gael P. Varoquauxa b, Alexandre Gramforta b, Michel, Vincenta b, Bertrand Thiriona b, Olivier Griselc, Mathieu Blondeld, Müller, Andrease, Nothman, Joelf, Louppe, Gillesg, Peter Prettenhoferh, Ron J. Weissi, Dubourg, Vincentj, Jake Vanderplask, Alexandre Passosl, David Cournapeaua, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay
- aLNAO - Laboratoire de Neuroimagerie Assistée par Ordinateur (France)
- bPARIETAL - Modelling brain structure, function and variability based on high-field MRI data (Neurospin, CEA Saclay, Bâtiment 145, 91191 Gif-sur-Yvette Cedex - France)
- cNuxeo (18-20 rue Soleillet 75020 Paris - France)
- dKobe University (Japan)
- eBauhaus-Universität Weimar (Geschwister-Scholl-Straße 8 99423 Weimar - Germany)
- fGoogle Inc (Toronto - Canada)
- gLAMI - Laboratoire de Mécanique et Ingénieries (IFMA. Campus des Cézeaux BP 265 63175 Aubière Cedex - France)
- hUniversity of Washington [Seattle] (Seattle, Washington 98105 - United States)
- iDepartment of Mechanical and Industrial Engineering [UMass] (UMass Amherst College of Engineering Department of Mechanical and Industrial Engineering, 220 ELAB, University of Massachusetts Amherst, MA 01003-2210 - United States)
- jEnthought Inc (515 Congress Avenue Suite 2100 Austin, TX 78701 - United States)
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
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.org.