  Stochastic gradient-boosted decision trees are widely employed for
multivariate classification and regression tasks. This paper presents a
speed-optimized and cache-friendly implementation for multivariate
classification called FastBDT. FastBDT is one order of magnitude faster during
the fitting-phase and application-phase, in comparison with popular
implementations in software frameworks like TMVA, scikit-learn and XGBoost. The
concepts used to optimize the execution time and performance studies are
discussed in detail in this paper. The key ideas include: An equal-frequency
binning on the input data, which allows replacing expensive floating-point with
integer operations, while at the same time increasing the quality of the
classification; a cache-friendly linear access pattern to the input data, in
contrast to usual implementations, which exhibit a random access pattern.
FastBDT provides interfaces to C/C++, Python and TMVA. It is extensively used
in the field of high energy physics by the Belle II experiment.
