Performance of machine learning models may differ between training and deployment for many reasons. For instance, model performance can change between environments due to changes in data quality, observing a different population than the one in training, or changes in the relationship between labels and features. These manifest as changes to the underlying data generating mechanisms, and thereby result in distribution shifts across environments. Attributing performance changes to specific shifts, such as covariate or concept shifts, is critical for identifying sources of model failures, and for taking mitigating actions that ensure robust models. In this work, we introduce the problem of attributing performance differences between environments to shifts in the underlying data generating mechanisms. We formulate the problem as a cooperative game and derive an importance weighting method for computing the value of a coalition (or a set) of distributions. The contribution of each distribution to the total performance change is then quantified as its Shapley value. We demonstrate the correctness and utility of our method on two synthetic datasets and two real-world case studies, showing its effectiveness in attributing performance changes to a wide range of distribution shifts.
翻译:机器学习模型的性能可能由于许多原因在培训和部署之间有所不同。例如,模型性能可能因数据质量的变化、观察到与培训中不同的人口、或标签和特征之间关系的变化而改变环境。这些变化表现为基本数据生成机制的变化,从而导致环境之间的分布变化。将性能变化归因于具体的变化,例如共变或概念转变,对于确定模型失败的来源和采取确保稳健模型的缓解行动至关重要。在这项工作中,我们提出了将环境之间的性能差异归因于基本数据生成机制的变化的问题。我们将这一问题作为一种合作游戏加以阐述,并得出计算分布联盟(或一组)价值的重要权重方法。然后,将每个分布对总体性能变化的贡献量化为其微值。我们展示了我们在两个合成数据集和两个真实世界案例研究中的方法的正确性和实用性,显示了将性能变化归因于广泛的分布变化。