Table 2.

Machine learning

Title	CatBoost: unbiased boosting with categorical features
Description	Paper introducing CatBoost algorithm
Performance metric	logloss, zero-one loss
Winner	CatBoost
Reference	[2]
Title	Benchmarking and optimization of gradient boosting decision tree algorithms
Description	Compare CatBoost, LightGBM, and XGBoost run on GPU’s, using four benchmark tasks
Performance metric	AUC ROC and Normalized discounted cumulative gain ($\text{NDCG}$)
Winner	CatBoost wins AUC for Epsilon DataSet, LightGBM wins AUC for the Higgs dataset, XGBoost wins (NDCG) for Microsoft and Yahoo Datasets
Reference	[8]