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

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.

preprint2013arXivOpen access
Lars BuitinckGilles LouppeMathieu BlondelFabian PedregosaAndreas MuellerOlivier GriselVlad NiculaePeter PrettenhoferAlexandre GramfortJaques GroblerRobert LaytonJake VanderPlasArnaud JolyBrian HoltGaël Varoquaux
Mathematical SoftwareMachine Learning
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Open access15 authors2 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Lars BuitinckGilles LouppeMathieu BlondelFabian PedregosaAndreas MuellerOlivier GriselVlad NiculaePeter PrettenhoferAlexandre GramfortJaques GroblerRobert LaytonJake VanderPlasArnaud JolyBrian HoltGaël Varoquaux

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