MLJ: A Julia package for composable machine learning
This package addresses the need for composable machine learning tools in the Julia ecosystem, offering an alternative to multi-language frameworks.
The authors introduced MLJ, a Julia package offering a unified interface for machine learning models, focusing on flexible model composition and providing tools for model selection, tuning, and evaluation.
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.