Module naivebayes

source code

A classifier based on the Naive Bayes algorithm. In order to find the probability for a label, this algorithm first uses the Bayes rule to express P(label|features) in terms of P(label) and P(features|label):

                     P(label) * P(features|label)
P(label|features) = ------------------------------
                            P(features)

The algorithm then makes the 'naive' assumption that all features are independent, given the label:

                     P(label) * P(f1|label) * ... * P(fn|label)
P(label|features) = --------------------------------------------
                                       P(features)

Rather than computing P(featues) explicitly, the algorithm just calculates the denominator for each label, and normalizes them so they sum to one:

                     P(label) * P(f1|label) * ... * P(fn|label)
P(label|features) = --------------------------------------------
                      SUM[l]( P(l) * P(f1|l) * ... * P(fn|l) )