Induction methods have recently been found to be useful in a wide variety of business related problems, including in the construction of expert systems. Decision tree induction is an important type of inductive learning method. Empirical results have shown that pruning a decision tree sometimes improves its accuracy. In this paper we summarize theoretical results of pruning and illustrate these results with an example. We give a sample size sufficient for decision tree induction with pruning based on recently developed learning theory. For situations where it is difficult to obtain a large enough sample, we provide several methods for a posterior evaluation of the accuracy of a pruned decision tree. Finally we summarize conditions under which pruning is necessary for better prediction accuracy.
