API design for machine learning software: experiences from the scikit-learn project - Archive ouverte HAL Access content directly
Conference Papers Year : 2013

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

(1) , (2) , (3) , (4) , (5) , (6) , (7) , (8) , (4, 9) , (4) , (10) , (11) , (2) , (12) , (4)
1
2
3
4
5
6
7
8
9
10
11
12

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.
Fichier principal
Vignette du fichier
paper.pdf (162.8 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00856511 , version 1 (01-09-2013)

Identifiers

Cite

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⟩
1921 View
2160 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More