OK, it took some googling, but I got this working. You have a couple options. 1) you pass the train, validation, and test sets using the macro variables so that a Model Comparison node can pick up the partition and calculate the stats. 2) take advantage of the specific proc syntax. I think this is what you were trying to do: 1. Add a data set. In my example I used German Credit from F1->Generate Sample Data Sources 2. Add a Partition node 3. Add a SAS code node with the code below. Change the bold for your own target (response) and inputs (effects). data mydata; set &EM_IMPORT_DATA(in=a) &EM_IMPORT_VALIDATE(in=b) &EM_IMPORT_TEST(in=c); if a then _partition="_Train"; else if b then _partition="_Valid"; else if c then _partition="_Test"; run; proc glmselect DATA=mydata; effect MyPoly = polynomial(duration checking savings/degree=4); model amount = MyPoly; partition rolevar=_partition(TEST='_Test' TRAIN='_Train' VALIDATE='_Valid'); run; 4. Run. The output results will give you the ASE of training/validation/testing. This model isn't fabulous for this data set but hopefully this approach will give you good results on yours! Good luck! -m Good reference: SAS/STAT(R) User's Guide, proc glm select - partition statement PS If you try the other approach I described, you can easily use a Model Comparison node to compare with HPGLM or any of the model nodes in Enterprise Miner. Very recommended to give this a try!!!!!!!!!!
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