MLJ: A Julia package for composable machine learning
Machine Learning
2020-12-01 v2 Machine Learning
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.
Cite
@article{arxiv.2007.12285,
title = {MLJ: A Julia package for composable machine learning},
author = {Anthony D. Blaom and Franz Kiraly and Thibaut Lienart and Yiannis Simillides and Diego Arenas and Sebastian J. Vollmer},
journal= {arXiv preprint arXiv:2007.12285},
year = {2020}
}
Comments
Shortened version of previous version