General Discriminant Analysis (GDA) is a general tool for classification and data mining techniques. It is called a "general" because it applies the methods of the general linear model (GLM) to the discriminant function analysis problem. Like the Discriminant Function Analysis module, GDA allows you to perform stepwise and best-subset discriminant analyses.

Another way to think about GDA is that the discriminant function analysis problem is "recast" as a general multivariate linear model, where the dependent variables of interest are (dummy-) coded vectors that reflect the group membership of each case.

One advantage of applying the general linear model to the discriminant analysis problem is that you can specify complex models for the set of predictor variables. For example, you can specify for a set of continuous predictor variables, a polynomial regression model, response surface model, factorial regression, or mixture surface regression without an intercept. Thus, you could analyze a constrained mixture experiment, where the predictor variable values must sum to a constant) where the dependent variable of interest is categorical in nature.

A powerful combination of analytics would be to generate an experimental design for quality improvement (Design of Experiments) with simulated data (e.g., distinct classifications of an outcome as "superior," "acceptable," or "failed"), and then model the posterior prediction probabilities of those outcomes with GDA.

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