Q: Given Email B with its feature vector. Compute the probability of email B being “spam” and “ham” using Naïve bayes algorithm and then finally assign class label (spam or ham).

                                         P(spam) = 0.65

   Email B =  < 0, 1, 1, 1 >, =  < count(meeting), count(enron), count(dating), count(hi) >

         P (meeting | spam) = 0.6,                           P (meeting | ham) = 0.02

         P (enron | spam) = 0.4,                               P (enron | ham) = 0.001

         P (dating | spam) = 0.7,                              P (dating | ham) = 0.005

         P (hi | spam) = 0.3,                                    P (hi | ham) = 0.09

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