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IND: Creation and Manipulation of Decision Trees from Data

A common approach to supervised classification and prediction in artificial intelligence and statistical pattern recognition is the use of decision trees. A tree is "grown" from data using a recursive partitioning algorithm to create a tree which (hopefully) has good prediction of classes on new data. Standard algorithms are 1) that of Breiman, Friedman, Olshen, and Stone; and 2) Id3 and its successor C4 (by Quinlan). As well as reimplementing parts of these algorithms and offering experimental control suites, IND also introduces Bayesian and MML methods and more sophisticated search in growing trees. These produce more accurate class probability estimates that are important in applications like diagnosis.

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