论文标题
Yggdrasil决策森林：一个快速而可扩展的决策森林图书馆
Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library
论文作者
论文摘要
Yggdrasil决策森林是一个培训，服务和解释决策森林模型的图书馆，针对研究和生产工作，在C ++中实施，在C ++，命令行界面，Python，Python（以张力量张力量决策林的名称），JavaScript，GO，GO，GO和Google Sheaets（for Sheets for Sheets）。自2018年以来，该库是有机开发的，此前四个设计原理适用于机器学习库和框架：使用的简单性，使用安全性，模块化和高级抽象以及与其他机器学习库的集成。在本文中，我们详细描述了这些原则，并介绍了它们如何用于指导图书馆的设计。然后，我们在一组古典机器学习问题上展示了库的使用。最后，我们报告了将我们的库与相关解决方案进行比较的基准。
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, Go, and Google Sheets (under the name Simple ML for Sheets). 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.
