Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library
This library provides a fast and extensible tool for researchers and practitioners working with decision forests, but it is incremental as it builds on existing methods rather than introducing new algorithmic paradigms.
The authors introduced Yggdrasil Decision Forests, a library for decision forest models that emphasizes simplicity, safety, modularity, and integration, and demonstrated its performance through benchmarks against related solutions.
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.