The basic statistical ideas necessary to understand the Naive Bayes algorithm have existed for centuries. The technique descended from the work of the 18^th century mathematician Thomas Bayes, who developed foundational principles to describe the probability of events, and how probabilities should be revised in the light of additional information. These principles formed the foundation for what are now known as Bayesian methods.

We will cover these methods in greater detail later on. But, for now, it suffices to say that a probability is a number between 0 and 1 (that is, between 0 percent and 100 percent), which captures the chance that an event will occur in the light of the available evidence. The lower the probability, the less likely the event is to occur. A probability of 0 indicates that the event will definitely not occur, while a probability of 1 indicates that the event will occur with 100 percent certainty.

Classifiers based on Bayesian methods utilize training...