Gradient boosted decision trees (GBDTs) have seen widespread adoption in academia, industry and competitive data science due to their state-of-the-art performance in a wide variety of machine learning tasks. In this paper, we present an extensive empirical comparison of XGBoost, LightGBM and CatBoost, three popular GBDT algorithms, to aid the data science practitioner in the choice from the multitude of available implementations. Specifically, we evaluate their behavior on four large-scale datasets with varying shapes, sparsities and learning tasks, in order to evaluate the algorithms' generalization performance, training times (on both CPU and GPU) and their sensitivity to hyper-parameter tuning. In our analysis, we first make use of a distributed grid-search to benchmark the algorithms on fixed configurations, and then employ a state-of-the-art algorithm for Bayesian hyper-parameter optimization to fine-tune the models.

本文研究了三个软件Gradient Boosting Decision Trees (GBDTs) packages: XGBoost, LightGBM和Catboost的GPU加速性能以及在超参数优化方面的比较，并注意到GBDTs的许多超参数需手动调整或自动优化，以达到最佳预测能力。

梯度提升决策树算法的基准测试和优化