Rashomon Set

A Rashomon set encompasses all nearly equally-performing models for a given machine learning task, highlighting the inherent multiplicity of solutions. Current research focuses on efficiently exploring these sets, particularly for interpretable models like rule sets and sparse generalized additive models, using techniques such as dropout and $\epsilon$-subgradient-based sampling to overcome computational challenges. Understanding and characterizing Rashomon sets is crucial for improving model explainability, mitigating biases stemming from model selection, and enhancing the robustness and fairness of machine learning applications. This exploration also aids in analyzing feature interactions and variable importance across multiple optimal models, rather than relying on a single solution.