It is important to understand Bayes' theorem before diving into the classifier. Let A and B denote two events. Events could be that it will rain tomorrow; 2 kings are drawn from a deck of cards; or a person has cancer. In Bayes' theorem, P(A |B) is the probability that A occurs given that B is true. It can be computed as follows:
Here, P(B|A) is the probability of observing B given that A occurs, while P(A) and P(B) are the probability that A and B occur, respectively. Too abstract? Let's look at some of the following concrete examples:
- Example 1: Given two coins, one is unfair with 90% of flips getting ...
