神经网络何时在表格数据上优于增强树？（英文版）

英文标题：When Do Neural Nets Outperform Boosted Trees on Tabular Data?中文摘要：该研究分析了在标签数据上使用神经网络（NN）和梯度增强决策树（GBDT）的性能差异，发现针对不同数据集，NN 和 GBDT 的表现各有千秋，需要根据数据集来选择合适的算法并进行超参数调优。英文摘要：Tabular data is one of the most commonly used types of data in machinelearning. Despite recent advances in neural nets (NNs) for tabular data, thereis still an active discussion on whether or not NNs generally outperformgradient-boosted decision trees (GBDTs) on tabular data, with several recentworks arguing either that GBDTs co