Naive Bayes Multiclass Classifier

The Naive Bayes trainer is a probabilistic classifier that can be used for multiclass problems. Using Bayes' theorem, the conditional probability for a sample belonging to a class can be calculated based on the sample count for each feature combination groups. However, Naive Bayes Classifier is feasible only if the number of features and the values each feature can take is relatively small. It assumes independence among the presence of features in a class even though they may be dependent on each other. This multi-class trainer accepts binary feature values of type float: feature values that are greater than zero are treated as true and feature values that are less or equal to 0 are treated as false.

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Video Tutorials