Residuals are listed as included in the output data set from the documentation:
https://documentation.sas.com/?docsetId=emref&docsetTarget=n1jqzz8cssr9m2n1ktx2iyv87q56.htm&docsetVe...
The Regression node must run before you can view the Regression node output data sets.
After a successful run, ensure that the Regression node is selected in the Diagram Workspace, then select the button to the right of the Exported Data property. This opens the Exported Data — Regression window, which lists the data sets that the Regression node outputs.
Two types of data sets are output from the Regression node:
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Scored Data Sets — are the scored training, validation, and test data set that contains original inputs and scores (prediction, residuals, classification results, and so on).
Here is a list of the name prefixes of the scores in the scored data sets:
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BL_ or BP_ — best possible profit or loss for any of the decisions
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CL_ or CP_ — profit or loss that is computed from the target value
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D_ — level of the decision that is chosen by the model
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EL_ or EP_ — expected profit or loss that is chosen by the model
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F_ — normalized category that the case comes from
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I_ — normalized category that the case is classified into
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P_ — posterior probabilities for categorical targets or predicted values for interval targets
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ROI_ — return on investment
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U_ — un-normalized category that the case is classified into.
When the model selection methods of forward, backward, or stepwise are applied, the Regression node automatically changes the model roles from input to rejected for variables that are not included in the regression model.