Practical and Efficient Rashomon Set Sampling for Model Interpretability

摘要

Explaining a single model can be misleading when many near-optimal models (a Rashomon set) yield different feature attributions. We frame this as a Rashomon set sampling problem and propose two practical axioms that any Rashomon sampler should satisfy: generalizability (meaning it must accept arbitrary reference models and loss functions) and Implementation Sparsity (meaning it should return a small, attribution-diverse subset of valid models). These two axioms are not satisfied by most known attribution methods, which we consider to be a fundamental weakness. Building on these axioms, we propose an ϵ-subgradient-based sampling framework and quantify effectiveness with Search Efficiency Ratio (SER) and Functional Explanation Range (FER). Experiments on a synthetic quadratic task and five real-world datasets show that our sampler achieves comparable or higher FER with up to ∼100× fewer models than exhaustive baselines such as TreeFARMS, while remaining agnostic to model class and loss. Even when the reference model is sub-optimal in practice, the resulting attributions align with ground truth and accepted domain knowledge.