From Log-Odds to Shapley Values: An Explanatory Geometry for the Weighted Naive Bayes Classifier
The study reformulates supervised distance for a weighted Naive Bayes classifier by using log-odds instead of conditional log-likelihoods. This new representation induces a distance equivalent to the L1 distance between analytical Shapley values, providing a formal, explanatory geometry linking model predictions directly to feature contributions.