Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction

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dc.creatorMurphy, P. M.
dc.creatorPazzani, M. J.
dc.date1994-03-01
dc.date.accessioned2026-07-07T09:12:18Z
dc.date.available2026-07-07T09:12:18Z
dc.descriptionWe report on a series of experiments in which all decision trees consistent with the training data are constructed. These experiments were run to gain an understanding of the properties of the set of consistent decision trees and the factors that affect the accuracy of individual trees. In particular, we investigated the relationship between the size of a decision tree consistent with some training data and the accuracy of the tree on test data. The experiments were performed on a massively parallel Maspar computer. The results of the experiments on several artificial and two real world problems indicate that, for many of the problems investigated, smaller consistent decision trees are on average less accurate than the average accuracy of slightly larger trees.
dc.descriptionSee http://www.jair.org/ for any accompanying files
dc.identifierhttps://arxiv.org/abs/cs/9403101
dc.identifierhttp://arxiv.org/abs/cs/9403101
dc.identifierJournal of Artificial Intelligence Research, Vol 1, (1994), 257-275
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/151978
dc.subjectArtificial Intelligence
dc.titleExploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction
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