4. Classifier using Mahalanobis distances ( 30 points). Use the Mahalanobis distance to construct a classifier that discriminates between the two classes. Make plots similar to those in lectures that show the minimum Mahalanobis distance from every point in a grid of (x,y) points. One way of doing this is along the following lines (put the code in a .m file rather than trying to do it all at the console): MAT592: Machine Learning, Spring 2023 Homework 1 You are required to do the following: (a) Modify your code so that it not only draws the picture, and plots the decision boundary which classifies data into two classes. (b) Find the classification rates (that is, the proportion of examples that are assigned to the correct class) for the training data Xtr and ttr and the test data Xte and tte. Remember that when you are classifying the test data, you should use the covariance matrices from the training data to calculate the Mahalanobis distances. (c) Modify your classifier to use pooled covariance matrices (note this is NOT the averaged covariance matrix between the two classes). Find the classification rates for both the training and test data. (d) Compare and discuss the difference between the classification rates you obtained above