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arXiv AI By Vincent Lemaire, Fabrice Cl\'erot

From Log-Odds to Shapley Values: An Explanatory Geometry for the Weighted Naive Bayes Classifier

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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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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