What are the limitations of Naïve Bayes in modern machine learning systems?

Naïve Bayes struggles with feature dependence, poor probability calibration, and complex decision boundaries.

While efficient, Naïve Bayes cannot capture feature interactions, making it weaker than deep learning models in complex domains. It also produces poorly calibrated probabilities and assumes fixed distributions. It is less effective for structured or continuous correlated data.