  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.
