Frank, EibeWitten, Ian H.2008-10-292008-10-291996-12Frank, E. & Witten, I. H. (1996). Selecting multiway splits in decision trees. (Working paper 96/31). Hamilton, New Zealand: University of Waikato, Department of Computer Science.1170-487Xhttps://hdl.handle.net/10289/1193Decision trees in which numeric attributes are split several ways are more comprehensible than the usual binary trees because attributes rarely appear more than once in any path from root to leaf. There are efficient algorithms for finding the optimal multiway split for a numeric attribute, given the number of intervals in which it is to be divided. The problem we tackle is how to choose this number in order to obtain small, accurate trees.application/pdfencomputer scienceinductive learningclassificationdecision-tree learningrecursive model selectioncross-validationMachine learningWorking Paper