module documentation
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(features) explicitly, the algorithm just calculates the numerator 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) )
| Class | |
A Naive Bayes classifier. Naive Bayes classifiers are paramaterized by two probability distributions: |
| Function | demo |
Undocumented |