ProxySHAP makes it practical to explain complex feature interactions in ML models by using proxy models and residual correction, achieving state-of-the-art accuracy while remaining computationally efficient even with thousands of features.
ProxySHAP is a new method for computing Shapley and Banzhaf interactions—measures that explain how features work together in machine learning models. It combines fast tree-based approximations with mathematical corrections to achieve both speed and accuracy, outperforming existing methods on large datasets.