MLJ: A Julia package for composable machine learning
2020-07-23Code Available2· sign in to hype
Anthony D. Blaom, Franz Kiraly, Thibaut Lienart, Yiannis Simillides, Diego Arenas, Sebastian J. Vollmer
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/alan-turing-institute/MLJ.jlnone★ 1,904
Abstract
MLJ (Machine Learing in Julia) is an open source software package providing a common interface for interacting with machine learning models written in Julia and other languages. It provides tools and meta-algorithms for selecting, tuning, evaluating, composing and comparing those models, with a focus on flexible model composition. In this design overview we detail chief novelties of the framework, together with the clear benefits of Julia over the dominant multi-language alternatives.