Consider A Naive Bayes Classifier For Spam Filtering. We Are Given A Training Set Of 500 Randomly Chosen Emails. We Examine Them

Consider A Naive Bayes Classifier For Spam Filtering. We Are Given A Training Set Of 500 Randomly Chosen Emails. We Examine Them And Label 200 Of Them As Spam Emails And 300 As Non-Spam Emails. There Are 2000 Words In The 200 Spam Emails. 200 Spam Emails Contain The Word “A”; 60 Contain The Word “Good”; And 50 Contain The Word “Job”. In The 300

(19pts) Consider a Naive Bayes classifier for spam filtering. We are given a training set of
500 randomly chosen emails. We e


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