Understanding naive Bayes
The basic statistical ideas necessary to understand the naive Bayes algorithm have been around for centuries. The technique descended from the work of the 18th century mathematician Thomas Bayes, who developed foundational mathematical principles (now known as Bayesian methods) for describing the probability of events, and how probabilities should be revised in light of additional information.
We'll go more in depth later, but for now it suffices to say that the probability of an event is a number between 0 percent and 100 percent that captures the chance that the event will occur given the available evidence. The lower the probability, the less likely the event is to occur. A probability of 0 percent indicates that the event definitely will not occur, while a probability of 100 percent indicates that the event certainly will occur.
Classifiers based on Bayesian methods utilize training data to calculate an observed probability of each class based on feature values...
