Exploring and Interacting with the Set of Good Sparse Generalized Additive Models
This work addresses the practical challenge of model interpretability and expert interaction in machine learning, though it is incremental in extending Rashomon set methods to specific model types.
The paper tackles the problem of limited interaction between machine learning models and domain experts by approximating the Rashomon set of sparse generalized additive models, enabling tasks like variable importance analysis and constraint-based model selection, with experiments showing effective results.
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.