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

Title:
API design for machine learning software: experiences from the scikit-learn project
Authors:
Buitinck, Lars, Louppe, Gilles, Blondel, Mathieu
Contributors:
Pedregosa, Fabian, Müller, Andreas, Grisel, Olivier, Niculae, Vlad, Prettenhofer, Peter, Gramfort, Alexandre, Grobler, Jaques, Layton, Robert, Vanderplas, Jake, Joly, Arnaud, Holt, Brian, Varoquaux, Gaël
Source:
ECML/PKDD 2013 Workshop: Languages for Data Mining and Machine Learning, Prague, Czechia [CZ], 23 September 2013
Publication Year:
2013
Subject Terms:
machine learning, data mining, api design, object-oriented programming, programming library, scientific software, Engineering, computing & technology, Computer science, Ingénierie, informatique & technologie, Sciences informatiques
Document Type:
Konferenz conference paper not in proceedings<br />http://purl.org/coar/resource_type/c_18cp<br />conferencePaper
File Description:
15
Language:
English
Access URL:
https://orbi.uliege.be/handle/2268/154357
Rights:
open access
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
Accession Number:
edsorb.154357
Database:
ORBi

Weitere Informationen

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.