As crossvalidation essentially computes a prediction for each example in
the training set, it was soon realized that this information could be used in
more elaborate ways than simply counting the number of correct and incorrect
predictions. One general way to achieve this is Stacking (Wolpert, 1992), which
learns from predictions of base (=level-0) classifiers, via a single meta (=level-1)
classifier. The basic idea of Stacking is to use the predictions of the base classifiers
as attributes in a new training set that keeps the original class labels.