How does Naïve Bayes behave in feature interaction nonlinearity regimes?

Naïve Bayes fails to model nonlinear feature interactions due to its independence assumption.

When features interact nonlinearly, joint distributions cannot be decomposed into independent marginals. NB ignores these interactions, leading to underfitting in complex structured data. This is why NB performs poorly in image and speech tasks where interactions are essential.