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

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.
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Submitted on : Sunday, September 1, 2013 - 5:31:15 PM
Last modification on : Monday, December 13, 2021 - 9:16:02 AM
Long-term archiving on: : Monday, December 2, 2013 - 8:55:29 AM


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  • HAL Id : hal-00856511, version 1
  • ARXIV : 1309.0238


Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, et al.. API design for machine learning software: experiences from the scikit-learn project. European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases, Sep 2013, Prague, Czech Republic. ⟨hal-00856511⟩



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