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

• URL: http://arxiv.org/abs/2605.19618v1
• Date: Tue, 19 May 2026 09:56:04 GMT
• Title: A Family of Divergence Measures for Evaluating the Reconstruction Quality of Explainable Ensemble Trees
• Abstract summary: Correlation-based approaches fail to detect systematic discrepancies in co-occurrence structure.<n>We propose a statistical framework grounded in the agreement-association distinction.<n>The framework incorporates four complementary measures capturing distinct structural facets of approximation quality.
• Score: 0.41292255339309664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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). Rooted in the Cressie-Read power divergence family with lambda equal to 2, the nLoI admits a closed-form decomposition into within-node and between-node components, providing a unique diagnostic capability to identify precisely where and why reconstruction fails. The framework incorporates four complementary measures capturing distinct structural facets of approximation quality. A unified permutation testing procedure delivers valid inference for all measures within a single resampling pass. Theoretical properties, including boundedness and symmetry, are established for each metric. Monte Carlo simulations and empirical evaluations confirm exact Type I error control and demonstrate that these measures detect reconstruction fidelity gradients invisible to correlation-based alternatives. The framework is developed and illustrated in the context of Explainable Ensemble Trees (E2Tree), and empirical evaluation on three benchmark datasets illustrates the practical utility of the framework.

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