Classification trees are a nonparametric classification method that creates a binary tree by recursively splitting the data on the predictor values. The splits are selected so that the two child nodes are purer in terms of the levels of the Response column than the parent node. Various options are used to control how deep the tree is grown. Class predictions for an observation are based on the majority class in the terminal node for the observation.
Classification trees can handle response variables with more than two classes. The Predictor columns can be either numeric or character (provided there are not more then 31 unique character values in any one character column). There is no need to make transformations of the Predictor columns; the same tree is grown for any monotone transformations of the data.
See also:
Details on Classification Modeling – General