Suppose you want to learn how to classify an email as SPAM from three characteristics$-$ X$1,$ X$2,$ and X$3($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 X$1,$ the fourth part the characteristic X$2$ and $225$ have the characteristic X$3.$ Moreover, of the "SPAM," the fourth part has characteristic X$1,$ half the characteristic X$2$ and $100$ have the characteristic X$3.$ It is requested:$1.$ Assume a Naive Bayes model for this problem and draw the correspondingBayesian network$\mathrm{.}2.$ 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 X$1$ feature but that does have the other two, how would it be classified according to this Naive Bayes model?