What if Cross Validation does not work?
A19. This is a variant of the NO TREE problem. When using a test sample or running a cross validation, all trees in the tree sequence have a relative error that exceeds one. Thus, no split exists that can improve the predictive performance of the tree. V-fold cross validation works by partitioning your data into equal-sized segments and holding out one segment at a time for test purposes. If certain classes of the target variable have very small sample sizes it may not be possible to subdivide each class into v subsets. Since CART requires at least one case in each class to run, the cross-validation trees cannot be constructed. SOLUTION 1: Fewer Cross Validations By reducing the number of cross validations you may be able to generate the test runs. However, Breiman, Friedman, Olshen and Stone warn that using fewer than 10 cross validations can seriously overestimate the error rate of any tree. SOLUTION 2: Dedicated Test Samples Define your own test subsamples with a random number gener
