What is the connection between Naïve Bayes and entropy minimization in classification?

Naïve Bayes implicitly reduces entropy of class distribution by maximizing posterior certainty.

Classification using Naïve Bayes selects the class with maximum posterior probability, effectively reducing uncertainty (entropy). From an information-theoretic perspective, NB performs implicit entropy minimization over class labels conditioned on features. However, feature independence limits how much entropy reduction is achievable.