Regression Trees

Name	Regression Trees
Description	Regression trees may be considered as a variant of decision trees, designed to approximate real-valued functions instead of being used for classification tasks. The inner nodes of regression trees are marked with tests as in decision trees. The difference is, that the leafs of regression trees may be marked with arbitrary real values, whereas in decision trees the leafs may only be marked with elements of a finite set of nominal values. This makes a real difference only, if you look at sets of trees (e.g. as potential hypotheses languages), instead of comparing single trees. A further extension is to allow linear functions as labels of leaf nodes. In this case the function at the leaf node reached for a specific example is evaluated for the instance's attribute values, to determine the value of the target attribute. This allows for global approximating by using multiple local approximations.
Methods	CART Top-Down Induction of Regression Trees