Scikit-learn: Machine Learning in Python

Fabian Pedregosa; Gaël Varoquaux; Alexandre Gramfort; Vincent Michel; Bertrand Thirion; Olivier Grisel; Mathieu Blondel; Peter Prettenhofer; Ron Weiss; Vincent Dubourg; Jake Vanderplas; Alexandre Passos; David Cournapeau; Matthieu Brucher; Matthieu Perrot; Édouard Duchesnay

Scikit-learn: Machine Learning in Python

Fabian Pedregosa ^{1, 2} Gaël Varoquaux ^{1, 2} Alexandre Gramfort ^{1, 2} Vincent Michel ^{1, 2} Bertrand Thirion ^{1, 2} Olivier Grisel ³ Mathieu Blondel ⁴ Peter Prettenhofer ⁵ Ron Weiss ⁶ Vincent Dubourg ⁷ Jake Vanderplas ⁸ Alexandre Passos ⁹ David Cournapeau ¹⁰ Matthieu Brucher ¹¹ Matthieu Perrot ¹² Édouard Duchesnay ²

1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data

NEUROSPIN - Service NEUROSPIN, Inria Saclay - Ile de France

2 LNAO - Laboratoire de Neuroimagerie Assistée par Ordinateur

3 Nuxeo

4 Kobe University

5 Bauhaus-Universität Weimar

6 Google Inc

7 LAMI - Laboratoire de Mécanique et Ingénieries

8 University of Washington [Seattle]

9 Department of Mechanical and Industrial Engineering [UMass]

10 Enthought Inc

11 TOTAL S.A.

12 LNAO

LNAO - Laboratoire de Neuroimagerie Assistée par Ordinateur

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.

Keywords : Python supervised learning unsupervised learning model selection

Document type :

Journal articles

Domain :

Computer Science [cs] / Machine Learning [cs.LG]

Computer Science [cs] / Mathematical Software [cs.MS]

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https://hal.inria.fr/hal-00650905
Contributor : Fabian Pedregosa <>
Submitted on : Saturday, March 2, 2013 - 9:58:17 PM
Last modification on : Tuesday, January 7, 2020 - 4:26:06 PM
Long-term archiving on: Friday, May 31, 2013 - 3:54:50 AM

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