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lib/Algorithm/Classifier/NaiveBayes.pm  view on Meta::CPAN

=head1 DESCRIPTION

This module implements a multinomial naive Bayes classifier. Strings
are broken into tokens and each class is scored using the log of its
prior probability, based on how often the class was trained, plus the
sum of the log probabilities of each token appearing in that class.
Token probabilities are smoothed so tokens never seen for a class do
not zero out the whole score. By default this is add-one, Laplace,
smoothing, but Lidstone, add-alpha, smoothing with a configurable
alpha may be selected instead. Smaller alphas, such as 0.1 to 0.5,
often perform better on small training sets.

By default token occurrences are weighted by their raw counts, but
binary weighting, counting each unique token once per document, may
be selected instead via token_weighting. Class priors default to how
often each class was trained, but may be set to uniform via priors.

Classes are not predefined. A class exists once something has been
trained for it and stops existing if everything for it is untrained.

The model may be saved to a JSON file or string and loaded back later,
allowing training and classification to happen in different processes.

=head1 METHODS