What is Naive Bayes?
In my blog post Bayes and Binomial Theorem i talk about Bayes' theorem and how it is used to determine, or estimate rather, a conditional probability by turning the conditions around. ... In other words, you can use P(B|A) and prior probabilities P(A) and P(B) to calculate P(A|B). This is very powerful because often we have information on the former three probabilities but not on the latter. Naive Bayes is a classification algorithm that does this with features of a dataset. In non-math words: We calculate the probability of belonging to class A given feature vector B by multiplying the proportion of feature vector B in the population of class A with the proportion of class A and then divide the whole thing by the proportion of vector B in the population. This is in principle a very straight forward calculation, but you can probably tell when it will be hard or impossible to do: If we have many features, it becomes more and more unlikely that a specific feature vector