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A decision tree (Quinlan, 1986, 1993) is a set of tests that are performed in sequence to classify patterns. After performing a test, one either reaches a terminal branch of the tree (at which point the pattern being tested can be classified) or a node of the decision tree (in other words another test that must be performed). The decision tree is complete for a pattern set if every pattern eventually leads the user to a terminal branch. Decision trees are created by processing instances in a training set, much like an artificial neural network is trained, though decision trees are induced (and not learned). Decision trees are popular classical models of categorization, because the rules in a decision tree can easily be understood by a human facing the same categorization task.
References:
- Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81-106.
- Quinlan, J. R. (1993). C4.5: Programs For Machine Learning. San Mateo, CA: Morgan Kaufmann.
(Added April 2011)
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