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Gradient boosting decision trees (GBDTs) have seen widespread adoption inacademia, industry and competitive data science due to their state-of-the-artperformance in many machine learning tasks. One relative downside to thesemodels is the large number of hyper-parameters that they expose to theend-user. To maximize the predictive power of GBDT models, one must eithermanually tune the hyper-parameters, or utilize automated techniques such asthose based on Bayesian optimization. Both of these approaches aretime-consuming since they involve repeatably training the model for differentsets of hyper-parameters. A number of software GBDT packages have started tooffer GPU acceleration which can help to alleviate this problem. In this paper,we consider three such packages: XGBoost, LightGBM and Catboost. Firstly, weevaluate the performance of the GPU acceleration provided by these packagesusing large-scale datasets with varying shapes, sparsities and learning tasks.Then, we compare the packages in the context of hyper-parameter optimization,both in terms of how quickly each package converges to a good validation score,and in terms of generalization performance.

Benchmarking and Optimization of Gradient Boosting Decision Tree Algorithms