What is an intuitive explanation of a naive Bayes classifier?

Naive Bayes Classifier is based upon two important principles: the assumption that all features are independent (the “Naive” part of the classifier) and Bayes’ theorem. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

Bayes’ theorem states the following: refers to the probability that A will be true given that B is true. It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors.