  Gradient boosting decision trees (GBDTs) have seen widespread adoption in
academia, industry and competitive data science due to their state-of-the-art
performance in many machine learning tasks. One relative downside to these
models is the large number of hyper-parameters that they expose to the
end-user. To maximize the predictive power of GBDT models, one must either
manually tune the hyper-parameters, or utilize automated techniques such as
those based on Bayesian optimization. Both of these approaches are
time-consuming since they involve repeatably training the model for different
sets of hyper-parameters. A number of software GBDT packages have started to
offer GPU acceleration which can help to alleviate this problem. In this paper,
we consider three such packages: XGBoost, LightGBM and Catboost. Firstly, we
evaluate the performance of the GPU acceleration provided by these packages
using 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.
