Hi Benjamin, The workaround that Yogen suggested in the paper you cited will only work if you have a binary target and you are predicting the event "1". How Yogen's code works I took a look at the score code produced by the Cutoff node. It creates the EM_Cutoff variable as a flag of whether the probability of event is higher than a certain cutoff. You can find that cutoff through specific methods, or specify your own in the Cutoff node properties. For example, I created a binary model to predict the event "good" on the binary target "good_bad". Then I used the Cutoff node with mehtod "Event Precision Equal Recall", which found that 0.75 was a better cutoff. The Cutoff node created the flag EM_Cutoff based on the new cutoff as below:      IF P_good_badgood > 0.75 THEN EM_CUTOFF = 1;      ELSE EM_CUTOFF = 0; Yogen's workaround is not going to work for my example because it does not make sense to set the "into" variable I_good_bad to be equal to EM_Cutoff which is a binary flag. How to fix this Instead of doing I_good_bad=EM_Cutoff, I should do as below in my SAS Code node.      if EM_cutoff = 1 then I_good_bad="good";      else I_good_bad="bad"; Now this works! This fixed this workaround for my example. I was not exactly sure what you were doing and what b_top_order variables were for your example. But I think this fixed workaround should help you. If not, please explain a bit more and someone from this community or myself can help you fix this code some more. good luck! -Miguel ... View more