Foundations Of Cognitive Science

Decision Tree

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.


  1. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81-106.
  2. Quinlan, J. R. (1993). C4.5: Programs For Machine Learning. San Mateo, CA: Morgan Kaufmann.

(Added April 2011)