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This provides code for a general Naive Bayes learner.
Naive Bayes is a supervised classification algorithm that uses Bayes rule to compute the fit between a new observation and some previously observed data. The observations are discrete feature vectors, with the Bayes assumption that the features are independent. Although this is hardly ever true, the classifier works well enough in practice.


NaiveBayes Holds information for a NaiveBayes classifier. 











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Calculate log P(classobservation) for each class.

Classify an observation into a class. classify(nb, observation) > class 
Train a naive bayes classifier on a training set. train(training_set, results[, priors]) > NaiveBayes

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