As input data distributions evolve, the predictive performance of machine learning models tends to deteriorate. In practice, new input data tend to come without target labels. Then, state-of-the-art techniques model input data distributions or model prediction distributions and try to understand issues regarding the interactions between learned models and shifting distributions. We suggest a novel approach that models how explanation characteristics shift when affected by distribution shifts. We find that the modeling of explanation shifts can be a better indicator for detecting out-of-distribution model behaviour than state-of-the-art techniques. We analyze different types of distribution shifts using synthetic examples and real-world data sets. We provide an algorithmic method that allows us to inspect the interaction between data set features and learned models and compare them to the state-of-the-art. We release our methods in an open-source Python package, as well as the code used to reproduce our experiments.

翻译：随着输入数据分布的变化，机器学习模型的预测性能往往会下降。在实践中，新的输入数据通常缺乏目标标签。当前最先进的技术通过建模输入数据分布或模型预测分布，试图理解学习模型与分布漂移之间的交互问题。我们提出了一种新颖的方法，用于建模解释特征如何因分布漂移而发生偏移。我们发现，相比于现有最先进的技术，建模解释偏移能更有效地检测超出分布范围的模型行为。我们通过合成样本和真实数据集分析了不同类型的分布漂移。我们还提供了一种算法方法，用于检查数据集特征与学习模型之间的交互，并与现有最先进技术进行比较。我们将所提出的方法以及用于复现实验的代码以开源Python包的形式发布。