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Naïve Bayes is a set of functions to train a classification model.  A training data set for which we know the outcome (Predictor column) based on input variable columns are used to generate the model. 

We then run the model against a set of input variables for which we do not know the Predictor to see what the model says.  It’s quite similar to a Decision Tree with one big exception; the input data are independent of one other.  This is a strong assumption but it makes the computation of the model extremely simple.