这篇论文提出了一个衡量模型相似性的新方法，从几何角度看模型决策边界，在90多个数据集上验证了效果，适合需要做模型选型或集成的朋友参考。
Rashomon Alignment (RA) 从几何视角衡量两个模型的功能相似性，不依赖于特定数据分布。RA 通过均匀采样计算几何对齐，在超过90个数据集上进行了实验。实验结果表明，几何对齐与分布对齐提供互补的相似性信息。RA 可用于模型选择、集成构建和模型可解释性。
Rashomon Alignment
We propose Rashomon Alignment (RA), a new measure to assess functional similarity between two models. Existing functional similarity measures are distributional, quantifying differences between outputs of models applied to real-world data. However, these measures can be regarded as ecologically valid only for regions in the input space represented by the available data. We introduce a geometrical perspective on functional model similarity, which estimates it across the entire data space, offering a comprehensive view of decision boundary alignment independent of any specific data distribution. We also propose geometric Rashomon Alignment as a measure of geometrical similarity, which is computed using data uniformly sampled from the instance space. We perform an experimental analysis on more than 90 datasets, examining critical cases where model alignment diverges from predictive accuracy. Our results show that geometrical and distributional alignment provide different and complementary perspectives on the similarity between models and algorithms. RA can be used for multiple purposes, including model selection, ensemble construction, and enhanced interpretability of machine learning models and algorithms.