Efficient Implementation of Decision Forests

Abstract

This chapter describes a variety of techniques for writing efficient,scalable, and general-purpose decision forest software. It will cover:- Algorithmic considerations, such as how to train in depth first or breadthfirst order; Optimizations, such as cheaply evaluating multiple thresholds for a givenfeature; Designing for multi-core, GPU, and distributed computing environments;and Various `tricks of the trade', including tuning parameters and dealing withunbalanced training sets.