Читать книгу Artificial Intelligence and Quantum Computing for Advanced Wireless Networks - Savo G. Glisic - Страница 23

An animal may be considered to be a tiger if it has four legs, weighs about 250 pounds, and has yellow fur with black strips. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this animal is a tiger, and that is why it is known as “Naive.”

Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods. Bayes’ theorem provides a way of calculating posterior probability P(c|x) from P(c), P(x) and P(x|c). Here we start with

(2.6)

where

 P(c|x) is the posterior probability of class (c, target) given predictor (x, attributes).

 P(c) is the prior probability of the class.

 P(x|c) is the likelihood, which is the probability of a predictor given the class.

 P(x) is the prior probability of the predictor.