Exploring the Decision Forest: An Empirical Investigation of Occam's
  Razor in Decision Tree Induction
  We report on a series of experiments in which all decision trees consistent
with the training data are constructed. These experiments were run to gain an
understanding of the properties of the set of consistent decision trees and the
factors that affect the accuracy of individual trees. In particular, we
investigated the relationship between the size of a decision tree consistent
with some training data and the accuracy of the tree on test data. The
experiments were performed on a massively parallel Maspar computer. The results
of the experiments on several artificial and two real world problems indicate
that, for many of the problems investigated, smaller consistent decision trees
are on average less accurate than the average accuracy of slightly larger
trees.
