  Shapley-related techniques have gained attention as both global and local
interpretation tools because of their desirable properties. However, their
computation using conditional expectations is computationally expensive.
Approximation methods suggested in the literature have limitations. This paper
proposes the use of a surrogate model-based tree to compute Shapley and SHAP
values based on conditional expectation. Simulation studies show that the
proposed algorithm provides improvements in accuracy, unifies global Shapley
and SHAP interpretation, and the thresholding method provides a way to
trade-off running time and accuracy.
