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Additional resources for Data Mining Using SAS Enterprise Miner: A Case Study Approach
The Regression node ﬁts models for interval, ordinal, nominal, and binary targets. Since you selected a binary variable (BAD) as the target in the Input Data Source node, the Regression node will ﬁt (by default) a binary logistic regression model with main effects for each input variable. The node also codes your grouping variables with either GLM coding or Deviation coding. By default, the node uses Deviation coding for categorical input variables. Right-click the Regression node and select Run.
The order depends on the order that is displayed in the Interaction Builder. To change the order of effects, you can select Tools Model Ordering but no ordering is done for this example. Stepwise Stopping Criteria enables you to set the maximum number of steps before the Stepwise method stops. The default is set to twice the number of effects in the model. Stop enables you to set the maximum (for Forward method) or minimum (for Backward method) number of effects to be included in the model. The Stepwise method uses cutoffs for variables to enter the model and for variables to leave the model.
025. Close the Regression node and save the changes when you are prompted. Since you have changed the default settings for the node, you will be prompted to change the default model name. Type StepReg for the model name. Select OK . Evaluating the Model Right-click the Assessment node and select Run. This enables you to generate and compare lift charts for the two regression models. Observe that each node becomes green as it runs. Since you ran the ﬂow from the Assessment node, you are prompted to see the Assessment results.