An implementation of Naive Bayes that works with categorical attributes. This learner works in time proportional to the number of training examples. It also requires memory that is proportional to the number of classes * number attributes * number of values.
The learner takes input and does output in c4.5 format. It expects to find the files <stem>.names and <stem>.data. Depending on command line argument, it will either output the model if you use the -v argument enough or test its error rate on <stem>.test.
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