This is a subjective question, hence you have to write your answer in the Text-Field given below. Consider a 2-class classification problem in a 2 dimensional feature space x=[x1,x2] with target variable y=1.The training data comprises 7 samples as shown below. 4 black diamonds for the positive class (y=+1) and 3 gray diamonds for the negative class (y=1). 2+2+1+1+2=8 Marks Recall, given a dataset of n points, in leave-one-out cross-validation, the classifier is built using n-1 training points and validated using the remaining point. This process is repeated for all training data points and error rate is calculated. Question No: 02 Options a) Find the k-means cluster centers for the two (black diamond and gray diamonds) classes, where the cluster center is calculated as the mean of data points belonging to that class. b) What is the equation for linear decision boundary which is equidistant from the two cluster center? c) What is the training error rate? d) Are there any samples such that upon their removal, the decision boundary changes in a manner that the removed sample goes to the other side? e) What is the leave-one-out cross validation error rate