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Statistical Advisor, Searching for Relationships on Categorial Variables

Classification Trees

Classification Trees page discusses techniques for predicting the membership of cases or objects in the classes of a categorical dependent variable from their measurements on one or more predictor variables.

Classification tree analysis is a techniques used in Data Mining. The Classification Trees module is a full-featured implementation of techniques for computing binary classification trees based on univariate splits for categorical predictor variables, ordered predictor variables (measured on at least an ordinal scale), or a mix of both types of predictors. It also has options for computing cassification trees based on linear combination splits for interval scale predictor variables.

The goal of classification tree is to predict or explain responses on a categorical dependent variable. These techniques have much in common with the techniques used in the more traditional methods of Discriminant Analysis, Cluster Analysis, Nonparametric Statistics, and Nonlinear Estimation.

The flexibility of classification trees make them a very attractive analysis option. This is not to say that their use is recommended to the exclusion of more traditional methods. Indeed, when the typically more stringent theoretical and distributional assumptions of more traditional methods are met, the traditional methods may be preferable. But as an exploratory technique, or as a technique of last resort when traditional methods fail, classification trees are, in the opinion of many researchers, unsurpassed.