A naive Bayes classifier assigns an observation with features 
to a class 
by applying Bayes' theorem
together with the assumption that the features are conditionally independent given
the class. Thus it chooses the class maximizing
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The independence assumption is often false, but the resulting classifier can still perform well and is inexpensive to train. Discrete, Gaussian, and other models differ in how the conditional feature distributions are estimated.
