Hey ujjawal, To make things easier let's call your "out-of-time validation sample" a test partition. The way to incorporate a test partition in Enterprise Miner, is to set the role of a data source as Test. You can specify the role of a data source during step 7 of the Create A Data Source wizard. Another alternative, once you drag-and-drop your data into a diagram, specify the train property "role" as Test. You can connect your Test partition to your flow diagram. I tend to connect my test data after a partition node, but you can connect it directly to a Model Comparison node. The Model Comparison will calculate several fit statistics for all your partitions, including the area under the ROC curve (note that it is called roc index in this node). The exported set for your test partition will also have the predicted probability as a new column with the prefix p_<target>. It is a good practice to include a Data Partition node so that you don't end up with an overtrained model. Since you already have a test partition, you can specify 70 for training and 30 for validation in your data partition node. Your flow would look like this: Good luck! -Miguel ... View more

create a new variable using the input function. some examples here: SAS(R) 9.3 Functions and CALL Routines: Reference Good luck, Miguel ... View more

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

Hi, I think it is a standard practice to use both types of features for a predictive model. Most models use constant features like demographics or type of account with transaction summaries like the monthly in-house behavior you described. How are you defining customer lifetime value? Expected profit in X number of months ahead? There are many times of approaches to predict this. Most model nodes handle these two types of inputs. How complex is your model? Is it a two-step model, for example, you predict whether a customer is going to return as a binary target, then you predict the expected value as an interval target? Or are you predicting the lifetime value directly? What models are you trying for each step? Thanks, Miguel ... View more

Hey Butch, Sorry, I am not familiar with this error. Please submit a Tech Support track using this link, and they will help you get running in no time. Technical Support Form Good luck! -Miguel ... View more

Please clarify, did you create a model using the Incremental Response node in SAS Enterprise Miner? What do the results look like and how are they different once you put that model in production? Thanks, Miguel ... View more

Hey JBerry, Not sure I get the second part, specially instant risk. But I am no expert in survival analysis. I use this node a lot, mostly to get hazard functions, but I am still very low on the learning curb. It sounds to me like you were trying to get the survival function? If I was to redo the survival function in EM, I would add something like the below in the SAS Code node. Notice that your survival node creates a dataset _ehcendata which summarizes events, event dates, and _y_. You can use that to get the curves you were looking for. Add the below in a SAS code node and connect it to your Survival node.    data ehcendata;    set &EM_LIB..&EM_METASOURCE_NODEID._ehcendata;    run;    proc lifetest data=ehcendata method=LT;    time _y_*event(0);    run; If you did it in base SAS you would also get the plots (change em_lib for your workspace and em_metasource_nodeid for your survival node ID). ... View more

This test would be a nice thing to add since the model statement of procs like logistic and hplogistic support the / lackfit option to do an HL test. Unfortunately I can't think of a workaround to make the Scorecard node use this option. But it is definitely on our list! In the meantime, if you have access to SAS Credit Scoring for Banking, you will be presented with the results of this test by default. Thanks, Miguel SAS Enterprise Miner™ R&D ... View more