论文标题
亚组鲁棒性在树上生长：经验基线调查
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation
论文作者
论文摘要
研究人员提出了许多公平和健壮的机器学习方法，但是缺乏对其亚组鲁棒性的全面经验评估。在这项工作中，我们在表格数据的背景下解决了这一差距，在表格数据的上下文中，敏感的亚组是明确定义的，现实世界中的公平问题比比皆是，而先前的工作通常不会与基于最新的树模型作为基准相提并论。我们对几种以前提供的方法进行了经验比较，以与最先进的基于树木的方法和其他基线相同。通过在八个数据集上的$ 340 {，} 000 $模型配置的实验中，我们表明基于树的方法具有强大的亚组鲁棒性，即使与鲁棒性和公平性增强方法相比。此外，最好的基于树的模型倾向于在一系列指标上显示出良好的性能，而稳健或组的模型可以显示出脆弱性，并且在不同指标之间对于固定模型而言，性能差异很大。我们还证明，基于树的模型对高参数配置的敏感性较小，并且训练成本较低。我们的工作表明，基于树的整体模型是表格数据的有效基线，并且在需要亚组鲁棒性时是明智的默认值。有关相关的代码和详细结果，请参见https://github.com/jpgard/subgroup-robustness-grows-on-trees。
Researchers have proposed many methods for fair and robust machine learning, but comprehensive empirical evaluation of their subgroup robustness is lacking. In this work, we address this gap in the context of tabular data, where sensitive subgroups are clearly-defined, real-world fairness problems abound, and prior works often do not compare to state-of-the-art tree-based models as baselines. We conduct an empirical comparison of several previously-proposed methods for fair and robust learning alongside state-of-the-art tree-based methods and other baselines. Via experiments with more than $340{,}000$ model configurations on eight datasets, we show that tree-based methods have strong subgroup robustness, even when compared to robustness- and fairness-enhancing methods. Moreover, the best tree-based models tend to show good performance over a range of metrics, while robust or group-fair models can show brittleness, with significant performance differences across different metrics for a fixed model. We also demonstrate that tree-based models show less sensitivity to hyperparameter configurations, and are less costly to train. Our work suggests that tree-based ensemble models make an effective baseline for tabular data, and are a sensible default when subgroup robustness is desired. For associated code and detailed results, see https://github.com/jpgard/subgroup-robustness-grows-on-trees .
