How does Naïve Bayes interact with feature embedding compression techniques like PCA or autoencoders?

Dimensionality reduction methods can improve or degrade Naïve Bayes depending on whether they preserve class-conditional separability.

PCA transforms data into orthogonal components maximizing variance, not class separation, which may distort NB assumptions. Autoencoders can preserve nonlinear structure but may introduce dependencies between features, violating independence assumptions. However, in practice, reduced dimensionality can sometimes improve NB performance by removing noise.