Naive Bayes assumes that features are conditionally independent, which the effect of a predictor(x) to class (c) is independent of the effect of other predictors to class(c). It computes the posterior probability, P(c|x), as the following formula:
Where P(x|c) is called likelihood, p(x) is called the marginal likelihood, and p(c) is called the prior probability. If there are many predictors, we can formulate the posterior probability as follows:
The advantage of Naïve Bayes is that it is relatively simple and straightforward ...
