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Efficient Implementation of Decision Forests

J. Shotton, D. Robertson, and T. Sharp


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 breadth first order;
  • Optimizations, such as cheaply evaluating multiple thresholds for a given feature;
  • Designing for multi-core, GPU, and distributed computing environments; and
  • Various `tricks of the trade', including tuning parameters and dealing with unbalanced training sets.


Publication typeInbook
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