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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. Glossary: observation A feature vector of discrete data. class A possible classification for an observation. Classes: NaiveBayes Holds information for a naive Bayes classifier. Functions: train Train a new naive Bayes classifier. calculate Calculate the probabilities of each class, given an observation. classify Classify an observation into a class.


NaiveBayes Holds information for a NaiveBayes classifier. 




probability dict 


class 


NaiveBayes 



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Calculate log P(classobservation) for each class. nb is a NaiveBayes classifier that has been trained. observation is a list representing the observed data. scale is whether the probability should be scaled by P(observation). By default, no scaling is done. The return value is a dictionary where the keys is the class and the value is the log probability of the class.

Train a naive bayes classifier on a training set. training_set is a list of observations. results is a list of the class assignments for each observation. Thus, training_set and results must be the same length. priors is an optional dictionary specifying the prior probabilities for each type of result. If not specified, the priors will be estimated from the training results.

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