Can you please please please answer question 5.2 in details with explanation (Thank you!) (I only provided 5.1 for question requirements)

5 Bayes Classifier [2.5 pts] 5.1 Bayes Classifier With General Loss Function A simple (and popular) loss function is the 0-1 loss function in which L(a, b) = 1 for a # b and 0 otherwise, which means all wrong predictions cause equal loss. Yet, in many other cases including cancer detection, the asymmetric loss is often preferred (misdiagnosing cancer as no-cancer is much worse). In this problem, we assume to have such an asymmetric loss function where L(a, a) = L(b, b) = 0 and L(a, b) = p, L(b, a) = q, p # q. Write down the the Bayes classifier f : X - Y for binary classification YE {-1, +1}. Simplify the classification rule as much as you can. [1.3 pts]5.2 Gaussian Class Conditional distribution (a) Suppose the class conditional distribution is a Gaussian. Based on the general loss function in problem 5.1, write the Bayes classifier as f(X) = sign(h(X)) and simplify h as much as possible. What is the geometric shape of the decision boundary? [0.4 pts] (b) Repeat (a) but assume the two Gaussians have identical covariance matrices. What is the geometric shape of the decision boundary? [0.4 pts] (c) Repeat (a) but assume now that the two Gaussians have covariance matrix which is equal to the identity matrix. What is the geometric shape of the decision boundary? [0.4 pts] 7