MLJ: A Julia package for composable machine learning
| Autor: | Blaom, Anthony D., Kiraly, Franz, Lienart, Thibaut, Simillides, Yiannis, Arenas, Diego, Vollmer, Sebastian J. |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Journal of Open Source Software, 2020, vol. 5(55), p. 2704 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.21105/joss.02704 |
| Popis: | 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. Comment: Shortened version of previous version |
| Databáze: | arXiv |
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