arXiv:2603. 22235v3 Announce Type: replace-cross Abstract: Decision Boundary Maps (DBMs) are an effective tool for visualising machine learning classification boundaries.
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The paper develops an explanatory framework for weighted naive Bayes classifiers by transforming the traditional supervised distance based on conditional log-likelihoods into a discriminative log‑odds representation. It demonstrates that this new representation yields a distance identical to the α1 distance between vectors of analytical Shapley values, thereby linking model geometry to local explanations. The authors also empirically evaluate various supervised distances derived from these representations using a k‑nearest neighbors classifier.
The paper introduces an imprecise Dirichlet model (IDM) approach to evaluate the robustness of Shapley values in tree-based models such as decision trees and random forests. By quantifying interval-valued Shapley values—using pessimistic and averaging principles—when a few unannotated instances are added to tree leaves, the authors derive efficient computational methods and extend the framework to Banzhaf values. Experiments and case studies demonstrate how these interval-valued explanations can be applied to debias uninformative features.
arXiv:2607. 03675v1 Announce Type: new Abstract: Shapley values are widely used to attribute value to training data based on their marginal contribution to performance on a validation set.
arXiv:2408. 01382v3 Announce Type: replace Abstract: Originating in game theory, Shapley values are widely used for explaining a machine learning model's prediction by quantifying the contribution of each feature's value to the prediction.
arXiv:2507. 07156v2 Announce Type: replace-cross Abstract: Supervised machine learning pipelines trained on features derived from persistent homology have been experimentally observed to ignore much of the information contained in a persistence diagram.
arXiv:2410. 01917v3 Announce Type: replace-cross Abstract: Originally introduced in game theory, Shapley values have emerged as a central tool in explainable machine learning, where they are used to attribute model predictions to specific input features.