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Computer Science > Machine Learning

arXiv:1309.0238 (cs)
[Submitted on 1 Sep 2013]

Title:API design for machine learning software: experiences from the scikit-learn project

Authors:Lars Buitinck (ILPS), Gilles Louppe, Mathieu Blondel, Fabian Pedregosa (INRIA Saclay - Ile de France), Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort (INRIA Saclay - Ile de France, LTCI), Jaques Grobler (INRIA Saclay - Ile de France), Robert Layton, Jake Vanderplas, Arnaud Joly, Brian Holt, Gaël Varoquaux (INRIA Saclay - Ile de France)
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Abstract:Scikit-learn is an increasingly popular machine learning li- brary. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and processing units in the library and then discuss its advantages in terms of composition and reusability. The paper also comments on implementation details specific to the Python ecosystem and analyzes obstacles faced by users and developers of the library.
Subjects: Machine Learning (cs.LG); Mathematical Software (cs.MS)
Cite as: arXiv:1309.0238 [cs.LG]
  (or arXiv:1309.0238v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.1309.0238
Journal reference: European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases (2013)

Submission history

From: Gael Varoquaux [view email] [via CCSD proxy]
[v1] Sun, 1 Sep 2013 16:22:48 UTC (28 KB)
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