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]