How does Naïve Bayes handle continuous feature transformations like binning or discretization?

Continuous features can be discretized into bins to make them compatible with discrete Naïve Bayes models.

Discretization converts continuous variables into categorical bins, allowing Multinomial or Bernoulli NB to be applied. This is useful when Gaussian assumptions are violated. However, binning introduces information loss and requires careful selection of bin boundaries.