MLJ: A Julia package for composable machine learning
Anthony Blaom, Franz Kiraly, Thibaut Lienart, Yiannis Simillides, Diego Arenas, Sebastian Vollmer
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.
Topics & Concepts
Computer scienceFocus (optics)SoftwareSoftware packageInterface (matter)Open sourceR packageProgramming languageOpen source softwareArtificial intelligenceSoftware toolSoftware engineeringMachine learningHuman–computer interactionUser interfaceTheoretical computer scienceMachine Learning and Data ClassificationComputational Physics and Python ApplicationsMachine Learning and Algorithms