Classification and Regression Trees

Classification and Regression Trees

Johannes Gehrke (Cornell University, USA)
Copyright: © 2005 |Pages: 3
DOI: 10.4018/978-1-59140-557-3.ch027
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Abstract

It is the goal of classification and regression to build a data-mining model that can be used for prediction. To construct such a model, we are given a set of training records, each having several attributes. These attributes either can be numerical (e.g., age or salary) or categorical (e.g., profession or gender). There is one distinguished attribute—the dependent attribute; the other attributes are called predictor attributes. If the dependent attribute is categorical, the problem is a classification problem. If the dependent attribute is numerical, the problem is a regression problem. It is the goal of classification and regression to construct a data-mining model that predicts the (unknown) value for a record, where the value of the dependent attribute is unknown. (We call such a record an unlabeled record.) Classification and regression have a wide range of applications, including scientific experiments, medical diagnosis, fraud detection, credit approval, and target marketing (Hand, 1997).

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