Overview: In this assignment. you will be analyzing the Naive Bayes model, making a Bayesian network, and creating probability tables. Instructions: Suppose you want to learn how to classify an email as SPAM from three characteristics - X1, X2, and X3 (each of them can take a true or false value). For this, we have a set of 1000 emails of which 750 are classified as "no SPAM" and 250 as "SPAM." Of the "no SPAM," half have the characteristic X1, the fourth part the characteristic X2 and 225 have the characteristic X3. Moreover, of the \"SPAM,\" the fourth part has characteristic X1, half the characteristic X2 and 100 have the characteristic X3. It is requested: 1. Assume a Naive Bayes model for this problem and draw the corresponding Bayesian network. 2. Estimate, according to the data taken from the training set. the probability tables of the previous network. What is the fundamental property of that estimation? 3. Given a new email that does not have the X1 feature but that does have the other two. how would it be classied according to this Naive Bayes model