Nope you can just put it in the Score Code and it will apply to all your partitions, including Train. ... View more

Here is information about those properties.  If you choose Assessment for the subtree method, then you should be able to see in the Iteration plot that the subtree selected has the best value for whatever assessment measure you chose, but you can view the other measures as well (in case you want to re-run using one of the other measures to get a different subtree).   Method — specifies the method that you want to use to select a subtree from the fully grown tree for each possible number of leaves. The following subtree methods are available: Assessment (default) — the smallest subtree with the best assessment value. The assessment value depends on the setting that you choose for the Assessment Measure property. Validation data set is used if available. Largest — the largest (full) tree is selected. N — the largest subtree with at most N leaves is selected. Use the Number of Leaves property to specify the value of N, the number of leaves. Number of Leaves — when the Method property of the Decision Tree node is set to N, specifies the largest number of leaves that you want in a subtree of n leaves. Permitted values are integers greater than or equal to 1. The default value for the Number of Leaves property is 1. Assessment Measure — specifies the method that you want to use to select the best tree, based on the validation data when the Method property is set to Assessment. If no validation data is available, training data is used. The available assessment measurements are as follows: Decision (default setting) — selects the tree that has the largest average profit and smallest average loss if a profit or loss matrix is defined. If no profit or loss matrix is defined, the value of the model assessment measure is reset in the training process, depending on the measurement level of the target. If the target is interval, the measure is set to Average Square Error. If the target is categorical, the measure is set to Misclassification. Average Square Error — selects the tree that has the smallest average square error. Misclassification — selects the tree that has the smallest misclassification rate. Lift — evaluates the tree based on the prediction of the top n% of the ranked observations. Observations are ranked based on their posterior probabilities or predicted target values. For an interval target, it is the average predicted target value of the top n% observations. For a categorical target, it is the proportion of events in the top n% of the data. When you set the Measure property to Lift, you must use the Assessment Fraction property to specify the proportion for the top n% of cases. ... View more

A few changes to make: 1) You need to add the runlabel parameter to the %macro statement:   %macro pmbr (run=1, runLabel=, method =scan, k=12); 2)  Need to remove " / level=level " from the VAR and TARGET statements in PROC DMDB. 3) I'm not sure what/where clusters_test data is, but using that causes an error for me after that.   4) I don't think PROC PMBR creates an ODS table FitStatistics   So I'm not sure this approach will work, but two things you can do to help figure out where your program is failing:    1) add  options mprint; to the Project Start Code so your Log will be more helpful.  And then...   2) View the Log in the Results of the SAS Code node after running it to see what errors are occurring. ... View more

Try:   proc contents data=&em_import_data; run;     ... View more

You should use role of Target for your observed (actual) target, whether it is numeric or character.  The Prediction role is typically automatically assigned by EM to the variable representing the predicted target from the model (P_target, e.g.).  One time when you would assign this role to a variable is if you were using the Model Import node to import predictions from a model. ... View more

The Interactive Binning node only does quantile or bucket binning for an interval input, and grouping of rare levels for a nominal input - no optimal binning.  The target is only used for the variable selection based on the Gini statistic.   In the Transform node, the Optimal binning does in fact use the target to do tree-based binning.  I'm guessing the ones not binned do not have a binning that optimizes the relationship with the target.  Do you see messages like this in the SAS log in the Results?   WARNING: Optimal Bin Transformations for Trans did not find any transformations for variable ZZZ ... View more