My organisation is running v13.2 of EM so there is no HPBNET node in EM and it has to be run through a code node. I have set the tool type to model and run the following code as per 'What's New in SAS Enterprise Miner 13.1':
proc HPBNET data=&em_import_data;
target %em_target;
input %em_nominal_input / level=nominal;
input %em_interval_input / level=interval;
code file="&em_file_empublishscorecode";
run;
The input is from a data partition node then metadata node. It runs without error but I get a warning that one of my variables is constant (which I don't understand because it has different values).
It seems to not produce a scored version of my training and validation datasets because I get errors when I try to run a model comparison node - ERROR: Variable P_FLAG_RCVR1 not found. (where P_FLAG_RCVR1 is the predicted value of my target variable.)
Any suggestions greatly appreciated. Or if there is more information that would help please let me know.
I have received an answer to this from tech support. Turns out the following code also needs to be added after the proc HPBNET statements: (This creates scored versions of datasets for comparison node.)
data &EM_EXPORT_TRAIN;
set &EM_IMPORT_DATA;
%inc "&EM_FILE_EMPUBLISHSCORECODE";
run;
data &EM_EXPORT_VALIDATE;
set &EM_IMPORT_VALIDATE;
%inc "&EM_FILE_EMPUBLISHSCORECODE";
run;
Would be much easier if we were running version 14.1 of EM 😉
I have received an answer to this from tech support. Turns out the following code also needs to be added after the proc HPBNET statements: (This creates scored versions of datasets for comparison node.)
data &EM_EXPORT_TRAIN;
set &EM_IMPORT_DATA;
%inc "&EM_FILE_EMPUBLISHSCORECODE";
run;
data &EM_EXPORT_VALIDATE;
set &EM_IMPORT_VALIDATE;
%inc "&EM_FILE_EMPUBLISHSCORECODE";
run;
Would be much easier if we were running version 14.1 of EM 😉
Are you ready for the spotlight? We're accepting content ideas for SAS Innovate 2025 to be held May 6-9 in Orlando, FL. The call is open until September 16. Read more here about why you should contribute and what is in it for you!
Use this tutorial as a handy guide to weigh the pros and cons of these commonly used machine learning algorithms.
Find more tutorials on the SAS Users YouTube channel.