P(dlt) x P(t)
P(tld) P(d) (2)
. A Naive Bayes’ classifier naively assumes that each of the descriptive features in a domain ¡s conditionally independent of all of the other descriptive features, given the state of the target feature.
. This assumption, although often wrong, enables the Naive Bayes’ model to maximally factorise the representation that it uses of the domain.
. Surprisingly, given the naivety and strength of the assumption it depends upon, a Naive Bayes’ model often performs reasonably well.