Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library

TL;DR

Yggdrasil Decision Forests is a versatile, high-performance library for decision forest models, emphasizing simplicity, safety, modularity, and integration, supporting multiple programming languages and used for research and production.

Contribution

The paper introduces the design principles and implementation of Yggdrasil Decision Forests, a flexible and extensible library for decision forests across various platforms.

Findings

Library supports multiple languages including C++, Python, JavaScript, Go, and Google Sheets.

Demonstrates effectiveness on classical machine learning problems.

Benchmark shows competitive performance compared to related solutions.

Abstract

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.