Let's walk through an example of training and testing naive Bayes with add-one smoothing. We'll use a sentiment analysis domain with the two classes positive (+) and negative (-), and take the following miniature training and test documents simplified from actual movie reviews. : P(−) = 3 5 P(+) = 2 5 The word with doesn't occur in the training set, so we drop it completely (as mentioned above, we don't use unknown word models for naive Bayes). The likelihoods from the training set for the remaining three words "predictable", "no", and "fun", are as follows, from Eq. 4.14 (computing the probabilities for the remainder of the words in the training set is left as an exercise for the reader): P("predictable"|−) = 1 + 1 14 + 20 P("predictable"|+) = 0 + 1 9 + 20 P("no"|−) = 1 + 1 14 + 20 P("no"|+) = 0 + 1 9 + 20 P("fun"|−) = 0 + 1 14 + 20 P("fun"