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

FastBDT: A speed-optimized and cache-friendly implementation of stochastic gradient-boosted decision trees for multivariate classification