A classifier solves the problem of identifying sub-populations of individuals with certain features in a larger set, with the possible use of a subset of individuals known as a priori (a training set). A Naive Bayes classifier is a supervised learning classifier that uses Bayes' theorem to build the model. In this recipe, we will build a Naive Bayes classifier.
Building a Naive Bayes classifier
Getting ready
The underlying principle of a Bayesian classifier is that some individuals belong to a class of interest with a given probability based on some observations. This probability is based on the assumption that the characteristics observed can be either dependent or independent from one another; in this second case...
