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This research establishes a rigorous framework for quantifying the robustness of predictions in naive Bayes classifiers and generative forests by measuring their sensitivity to distributional perturbations within Probabilistic Graphical Models. By evaluating metrics such as epsilon-contamination and chi-squared divergence, the study provides actionable indicators for assessing the trustworthiness of these specific generative models on benchmark datasets. The work advances the theoretical understanding of model stability in probabilistic machine learning contexts.
Local Robustness Quantification for Naive Bayes Classifiers and Generative Forests: a General Approach
arXiv:2609.11366v1 Announce Type: new Abstract: We provide methods for calculating the robustness of the predictions of two types of generative classifiers whose underlying distribution is a Probabilistic Graphical Model (PGM): naive Bayes classifiers and generative forests (a probabilistic extension of random forests). Following the paradigm of robustness quantification, we define the robustness of a prediction as the extent to which the distribution of the classifier can be perturbed without changing this prediction. We consider perturbations obtained by varying the local models of the PGMs within general neighborhoods and focus in particular on epsilon-contamination, total variation distance and chi-squared divergence balls. We test our methods on benchmark datasets, demonstrate that the robustness value of a prediction serves as an indicator for its trustworthiness and compare our approach with other such indicators.
Abstract:We provide methods for calculating the robustness of the predictions of two types of generative classifiers whose underlying distribution is a Probabilistic Graphical Model (PGM): naive Bayes classifiers and generative forests (a probabilistic extension of random forests). Following the paradigm of robustness quantification, we define the robustness of a prediction as the extent to which the distribution of the classifier can be perturbed without changing this prediction. We consider perturbations obtained by varying the local models of the PGMs within general neighborhoods and focus in particular on epsilon-contamination, total variation distance and chi-squared divergence balls. We test our methods on benchmark datasets, demonstrate that the robustness value of a prediction serves as an indicator for its trustworthiness and compare our approach with other such indicators.
Machine Learning (cs.LG)
arXiv:2609.11366 [cs.LG]
(or arXiv:2609.11366v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.11366 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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