A Powerful Classification Technique in Data Mining - Discriminant Analysis(part – IV)

Two Goals for Discriminant Analysis

 Interpretation: “How are the groups different?” Find and interpret linear combinations of variables that optimally predict group differences

 Classification: “How accurately can observations be classified into groups?” Using functions of variables to predict group membership for a data set and evaluate expected error rates

Steps involved in Discriminant Analysis Process

 Specify the dependent & the predictor variables

 Test the model’s assumptions a priori

 Determine the method for selection and criteria for entering the predictor variables into the model

 Estimate the parameters of the model

 Determine the goodness-of-fit of the model and examine the residuals

 Determine the significance of the predictors

 Test the assumptions

 Validate the results