Avoiding Overfitting of Decision Trees

Abstract

This chapter begins by examining techniques for dealing with clashes (i.e. inconsistent instances) in a training set. This leads to a discussion of methods for avoiding or reducing overfitting of a decision tree to training data. Overfitting arises when a decision tree is excessively dependent on irrelevant features of the training data with the result that its predictive power for unseen instances is reduced.