I am confused with the answers to the questions below:
Assume that we have a dataset D with 100 examples, 50 of which belong to the class ’good’ and 50 belong to the class ‘poor’. Assume further that we have a very naïve learning algorithm (L) that produces a model that simply predicts the majority class of the training examples (if there is no majority, it always predicts ‘good’)
1) Performing stratified 10-fold cross validation on D will give the same result as when using half the examples of each class for training and the other halves for testing. Answer> True
2) Leave-one-out cross validation will give a better accuracy estimate for how L will perform on examples that are independently sampled from the same distribution as D than when using half the examples for training and half the examples for testing. Answer> False.
May I know why the first answer is true and the second is false?