Naive Bayes uses the principles of Bayes' Theorem to classify data points by calculating the posterior probability of a class given the features. Mathematically, it is expressed as:
P(C|X) = (P(X|C) * P(C)) / P(X)
Here, P(C|X) is the probability of class C given the features X, P(X|C) is the likelihood of features X given class C, P(C) is the prior probability of class C, and P(X) is the evidence or marginal likelihood of features X.
