论文标题
MLJ：朱莉娅（Julia）的可组合机器学习包
MLJ: A Julia package for composable machine learning
论文作者
论文摘要
MLJ（Julia中的机器学习）是一个开源软件包，它提供了一个通用接口，用于与用Julia和其他语言编写的机器学习模型进行交互。它提供了用于选择，调整，评估，构图和比较这些模型的工具和元算法，重点是灵活的模型组成。在此设计概述中，我们详细介绍了该框架的首席新颖性，以及朱莉娅（Julia）对主要的多语言替代方案的明显好处。
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.
