  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.
