Scikit-learn: Machine Learning in Python
Fabián Pedregosa(Commissariat à l'Énergie Atomique et aux Énergies Alternatives), Gaël Varoquaux(Commissariat à l'Énergie Atomique et aux Énergies Alternatives), Alexandre Gramfort(Commissariat à l'Énergie Atomique et aux Énergies Alternatives), Vincent Michel(Commissariat à l'Énergie Atomique et aux Énergies Alternatives), Bertrand Thirion(Commissariat à l'Énergie Atomique et aux Énergies Alternatives), Olivier Grisel(Nuxe (France)), Mathieu Blondel(Kobe University), Müller, Andreas(Bauhaus-Universität Weimar), Nothman, Joel(Google (Canada)), Louppe, Gilles, Peter Prettenhofer(University of Washington), Ron J. Weiss(Amherst College), Vincent Dubourg(Enthought (United States)), Jake Vanderplas(Total (France)), Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay
Cited by 63,665Open Access
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.
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