In real applications, interaction between machine learning models and domain experts is critical; however, the classical machine learning paradigm that usually produces only a single model does not facilitate such interaction. Approximating and exploring the Rashomon set, i.e., the set of all near-optimal models, addresses this practical challenge by providing the user with a searchable space containing a diverse set of models from which domain experts can choose. We present algorithms to efficiently and accurately approximate the Rashomon set of sparse, generalized additive models with ellipsoids for fixed support sets and use these ellipsoids to approximate Rashomon sets for many different support sets. The approximated Rashomon set serves as a cornerstone to solve practical challenges such as (1) studying the variable importance for the model class; (2) finding models under user-specified constraints (monotonicity, direct editing); and (3) investigating sudden changes in the shape functions. Experiments demonstrate the fidelity of the approximated Rashomon set and its effectiveness in solving practical challenges.

翻译：在现实应用中，机器学习模型与领域专家之间的交互至关重要；然而，传统机器学习范式通常仅产生单一模型，无法促进此类交互。近似并探索Rashomon集合（即所有接近最优模型的集合）通过向用户提供包含多样化模型的可搜索空间，使领域专家能够从中选择，从而解决了这一实践难题。我们提出算法，针对固定支持集，用椭球体高效精确地近似稀疏广义加性模型的Rashomon集合，并利用这些椭球体逼近多种不同支持集的Rashomon集合。该近似Rashomon集合作为基石，可解决以下实践挑战：(1) 研究模型类中变量重要性；(2) 在用户指定约束（单调性、直接编辑）下寻找模型；(3) 探究形状函数中的突变点。实验验证了近似Rashomon集合的保真度及其在解决实践挑战中的有效性。