(10 points) Suppose you are testing a new algorithm on a data set consisting of 100 positive and 100 negative examples. You plan to use leave- one-out cross-validation and compare your algorithm to a baseline function, a simple majority classifier. With leave-one-out cross-validation, you train the algorithm on 199 data points and test it on 1 data point. You repeat the process 400 times, letting each point having a chance to represent the test set, and report the average of the classification accuracies. Given a set of training data, the majority classifier always outputs the class that is in the majority in the training set, regardless of the input. You expect the majority classifier to achieve about 50% classification accuracy, but to your surprise, it scores zero every time. Why