What is the role of entropy and information gain in Naïve Bayes feature understanding?

Entropy and information gain help evaluate how informative features are for class separation in Naïve Bayes contexts.

Although Naïve Bayes does not explicitly use entropy, feature importance can be analyzed using information gain, which measures reduction in uncertainty about class labels. Features with higher information gain contribute more strongly to likelihood separation.