Models p(x|y), p(y) and p(x) then computes p(y|x) using Bayes rule. Learns data distribution.

Estimate p(y|x) directly from data. Learns the boundary between classes.

Has a fixed number of parameters, which does not change no matter how many samples you have.

Has an internal structure which will depend on training data.