How does Naïve Bayes behave under Bayesian model averaging interpretations?

Naïve Bayes can be seen as a single model approximation to Bayesian model averaging over feature independence structures.

Bayesian model averaging integrates over multiple models weighted by posterior probabilities. Naïve Bayes implicitly assumes a single factorized structure instead of averaging over dependency structures. This simplifies inference but ignores uncertainty in model structure, leading to overconfident predictions in ambiguous cases.