Read section 4.4 of the text book, then complete question 4.3 and answer the following questions

For the multinomial naive bayes classifier (the "normal" one):

What is P("good" | pos) ?

What is P("poor" | neg) ?

For the binarized naive bayes classifier :

What is P("great" | pos)?

What is P("good" | neg)?

The section it wants you to read ->

Train two models, multinomial naive Bayes and binarized naive Bayes, both with add-1 smoothing, on the following document counts for key sentiment words, with positive or negative class assigned as noted. doc "good" "poor" "great" (class) dl. 3 0 3 pos d2.0 1 2 pos d3.1 3 0 neg d4.1 5 2 neg d5.0 2 0 neg Use both naive Bayes models to assign a class (pos or neg) to this sentence: A good, good plot and great characters, but poor acting. Train two models, multinomial naive Bayes and binarized naive Bayes, both with add-1 smoothing, on the following document counts for key sentiment words, with positive or negative class assigned as noted. doc "good" "poor" "great" (class) dl. 3 0 3 pos d2.0 1 2 pos d3.1 3 0 neg d4.1 5 2 neg d5.0 2 0 neg Use both naive Bayes models to assign a class (pos or neg) to this sentence: A good, good plot and great characters, but poor acting