Yggdrasil Decision Forests is a library for the training, serving and interpretation of decision forest models, targeted both at research and production work, implemented in C++, and available in C++, command line interface, Python (under the name TensorFlow Decision Forests), JavaScript, and Go. The library has been developed organically since 2018 following a set of four design principles applicable to machine learning libraries and frameworks: simplicity of use, safety of use, modularity and high-level abstraction, and integration with other machine learning libraries. In this paper, we describe those principles in detail and present how they have been used to guide the design of the library. We then showcase the use of our library on a set of classical machine learning problems. Finally, we report a benchmark comparing our library to related solutions.
翻译：Yggsil决定森林是一个针对研究和生产工作的森林决策模型的培训、服务和解释图书馆,在C+++实施,在C++、指挥线接口、Python(以TensorFlow决定森林的名称)、JavaScript和Go中提供。该图书馆自2018年以来按照一套适用于机器学习图书馆和框架的四项设计原则,即简单使用、安全使用、模块化和高层次抽象化以及与其他机器学习图书馆的整合,进行了有机开发。我们在本文件中详细描述了这些原则,并介绍了如何利用这些原则来指导图书馆的设计。然后,我们展示了我们图书馆在一套古典机器学习问题上的使用情况。最后,我们报告了一个基准,将图书馆与其他相关解决方案进行比较。
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