A Family of Divergence Measures for Evaluating the Reconstruction Quality of Explainable Ensemble Trees

ArXi:2605.19618v1 Announce Type: new Validating interpretable surrogate models for ensemble learners requires measuring agreement between the ensemble's internal representation and its surrogate approximation, rather than mere association. Correlation-based approaches are scale-invariant and fail to detect systematic discrepancies in co-occurrence structure. We propose a statistical framework grounded in the agreement-association distinction, centered on the normalized Loss of Interpretability (nLoI.