You are hired by a new start-up called Readr that wants to offer automatic book recommendations based on a book's text. You are tasked with building a classifier that, given a book and its content classifies the book as good or bad. After consulting with a top literary critic, you decide that you only need to consider two binary features for this task: a) the length of the book is more than 500 pages (represented by random variable L with values short and long), and b) the word "wow" appears in the book text (represented by random variable W with values true and false) Potts, Christopher. 2011. On the negativity of negation. In Nan Li and David Lutz, eds., Proceedings of Semantics and uistic Theory 20, 636-659 a) You have a dataset of 10,000 books with good/bad labels provide by top literary critics, and you observe the following: i) 2,500 books are labeled as good and the rest as bad, ii) 2,000 of the good books are long, iii) 5,000 of the bad books are short, iv) 50 of the good books have the word "wow", and v) 1,500 of the bad books have the word "wow". Let G be the hypothesis that a book is good, and B the hypothesis that it is bad. What values would you pick for the priors P(G) and P(B), and the likelihoods P(L long G), P(L long | B), P(Wtrue G), P(Wtrue | B)? b) You decide to build your classifier using Naive Bayes, building on the probability mass functions derived in part a). Suppose your classifier receives a new book with length of 700 words and that does not contain the word "wow". What will your classifier predict about this book? C) Suppose you go back to your dataset and notice that all the good books that are long do not have the word "wow", and that 750 of the bad books that are long do not have the word "wow". Would this information lead you to a different answer than the one produced by your classifier from part b). If so, why