The basic statistical ideas necessary to understand the Naive Bayes algorithm have existed for centuries. The technique descended from the work of the 18th century mathematician Thomas Bayes, who developed foundational principles for describing the probability of events and how probabilities should be revised in 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. For now, it suffices to say that a probability is a number between zero and one (that is, from zero to 100 percent), which captures the chance that an event will occur in light of the available evidence. The lower the probability, the less likely the event is to occur. A probability of zero indicates that the event will definitely not occur, while a probability of one indicates that the event will occur with absolute certainty.
Classifiers based on Bayesian methods utilize training data...
