I do understand the concept of Naive Bayesian classification, as it tries to calculate the probability of an outcome of a class given multiple evidences. It comes from the Bayes theorem and it is called naive as it tries to each of piece of evidence as independent. This approach is why this is called naive Bayes.
However, I wonder why in the derivation of the mathematics behind Naive Bayesian Classificaiton, we take logs and odds.
So my question is basically the following:
Why odds and log odds transformations are meaningful ?