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Chapter 9 DECISION TREES
Decision Trees 167 In case of numeric attributes, decision trees can be geometrically interpreted as a collection of hyperplanes, each orthogonal to one of the axes. Naturally, decision-makers prefer less complex decision trees, since they may be consid-ered more comprehensible. Furthermore, according to Breiman et al. (1984)
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