Methods and systems for automatic selection of preferred size classification and regression trees

Abstract

Methods and systems for automatically identifying and selecting preferred size classification and regression trees are disclosed. The invention is used to identify a specific decision tree or group of preferred size trees that are consistent across train and test samples in node-specific details that are often important to decision makers. Specifically, for a tree to be identified as preferred by this system, the train and test samples must both agree on key measures for every terminal node of the tree. In addition to this node-by-node criterion, an additional tree selection method may be imposed. Accordingly, the train and test samples rank order the nodes on a relevant measure in the same way. Both consistency criteria may be applied in a fuzzy manner in which agreement must be close but need not be exact.