Hello,    Support on an external server for the SQL driver ended.  We are using in our ODBC connection with the driver SQL Server.  We recreated the connection with ODBC Driver 17 for SQL Server. This reestablished our connection but the data returned for some character vars is returned at a length of 32767.  I can cast the vars in the pass thru to get smaller numbers cast(var as varchar(9)) as var_name1.  This seems like an extra step.  What am I missing? Should I use the SAS/Access driver?   ... View more

Hi all,   I have a large data set that I have sampled (oversampled) and have a few models(Decision Tree, regression, neural) in a workflow.  In addition, there is an ensemble model of the decision tree and regression.  I have these 4 models going to a model comparison node. My question is where to put the decision node(s) to adjust the priors.  In the Sas EM 14.1 Help for the Ensemble Node there it states "When you create a process flow diagram that contains an Ensemble node, do not specify prior probabilities in the diagram before the modeling nodes. In order to obtain the correct fit statistics for the combined unadjusted posterior probabilities, follow the Ensemble node in your diagram with a Decisions node, and use the Decisions node to specify probabilities."  Should I use multiple Decision Nodes, one for each model before the model comparison node or just one after the model comparison node?  Should the priors be adjusted for the model assessment or for the scoring?   Thanks for the help   Larry     ... View more

Enterprise miner 14.1 Hello, I am following this example https://communities.sas.com/t5/SAS-Communities-Library/Tip-How-to-model-a-rare-target-using-an-oversample-approach-in/ta-p/223599?nobounce to familiarize myself with Oversampling.   As an additional learning, I connected a score node to the model comparison node. My thought is to copy the original data set and the first sample and score this data set.  So, I added set a copy of the original German Credit with a role of score and copied the first sample node (same seed, same sample size, and same event percent .05/.95) and ran the workflow.   Class Variable Summary Statistics Data Role=SCORE Output Type=CLASSIFICATION Numeric Formatted Frequency Variable Value Value Count Percent I_good_bad . BAD     204 34 I_good_bad . GOOD 396 66 Data Role=SCORE Output Type=MODELDECISION Numeric Formatted Frequency Variable Value Value Count Percent D_good_bad . BAD     226 37.6667 D_good_bad . GOOD 374 62.3333   I had expected the results to be closer to the sample proportions (Bad .05 vs Good . 95), but the results appear close to the original data set.  When I look at the score code, I see the original data set's posterior probabilities with no adjustment. Label P_good_badgood='Predicted: good_bad=good'; P_good_badgood = 0.7; Label P_good_badbad='Predicted: good_bad=bad'; P_good_badbad = 0.3;    Am I just approaching this problem incorrectly? Have I made an error or just an error in understanding? I've attached a copy of my workflow, I renamed it .jpg.  If you drop this you should be able to import into EM.  Thanks!   ... View more