Scikit-learn: Machine Learning in Python

Scikit-learn: машинное обучение на Python
Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron J. Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay, Brucher, Matthieu, Perrot, Matthieu, Duchesnay, Édouard, Müller, Andreas, Nothman, Joel, Louppe, Gilles, Vincent Dubourg
2012-01-02
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API consistencyScikit-learnmachine learning in Pythonsimplified BSD licensesupervised and unsupervised learning
Abstract (AI)
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.
Key Findings
1
Design priorities of scikit-learn include ease of use, performance, thorough documentation, and consistent API.
2
Scikit-learn has minimal dependencies and is distributed under the simplified BSD license, facilitating academic and commercial use.
3
Scikit-learn provides a Python module that integrates a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
4
Source code, binaries, and documentation for scikit-learn are publicly available from http://scikit-learn.org.
5
The package is designed to make machine learning accessible to non-specialists by using a general-purpose high-level language (Python).
Research Object

Scikit-learn Python machine learning library

Research Subject

Integration and provision of a wide range of state-of-the-art supervised and unsupervised machine learning algorithms for medium-scale problems, emphasizing ease of use, performance, documentation, API consistency, and minimal dependencies

Publication Details
Publication Date
2012-01-02
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Authors
Fabián Pedregosa
Gaël Varoquaux
Alexandre Gramfort
Vincent Michel
Bertrand Thirion
Olivier Grisel
Mathieu Blondel
Peter Prettenhofer
Ron J. Weiss
Vincent Dubourg
Jake Vanderplas
Alexandre Passos
David Cournapeau
Matthieu Brucher
Matthieu Perrot
Édouard Duchesnay
Brucher, Matthieu
Perrot, Matthieu
Duchesnay, Édouard
Müller, Andreas
Nothman, Joel
Louppe, Gilles
Vincent Dubourg
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