The holdout method, where two-thirds of the data are used for training and the remaining one-third are used for testing.

While the .632 bootstrap approach is useful for obtaining a reliable estimate
of model accuracy, it has a known limitation. Consider a two-class problem,
where there are equal number of positive and negative examples in the data.

Suppose the class labels for the examples are generated randomly. The clas-
sifier used is an unpruned decision tree (i.e., a perfect memorizer). Determine

the accuracy of the classifier using each of the following methods.

Assuming that the training and test samples are equally representative,
the test error rate will be close to 50%.