Reference : API design for machine learning software: experiences from the scikit-learn project
Scientific congresses and symposiums : Unpublished conference/Abstract
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/154357
API design for machine learning software: experiences from the scikit-learn project
English
Buitinck, Lars mailto []
Louppe, Gilles mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Blondel, Mathieu mailto []
Pedregosa, Fabian []
Müller, Andreas []
Grisel, Olivier []
Niculae, Vlad []
Prettenhofer, Peter []
Gramfort, Alexandre []
Grobler, Jaques []
Layton, Robert []
Vanderplas, Jake []
Joly, Arnaud mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Holt, Brian []
Varoquaux, Gaël []
23-Sep-2013
15
Yes
No
International
ECML/PKDD 2013 Workshop: Languages for Data Mining and Machine Learning
23 September 2013
Prague
Czech Republic
[en] machine learning ; data mining ; api design ; object-oriented programming ; programming library ; scientific software
[en] scikit-learn is an increasingly popular machine learning library. 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.
Researchers ; Professionals
http://hdl.handle.net/2268/154357

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
paper.pdfAuthor preprint266.44 kBView/Open

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.