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 ...