It depends on the structure of your network. With a Parent-child or Markov blanket structure, the inputs can be parents of the target. This paper has all the details and equations of how the predictions for a target are obtained from a Bayesian network: https://support.sas.com/resources/papers/proceedings17/SAS0474-2017.pdf
Hello WendyCzika,
Thank you for your answer !
I built a Bayesian Network Model in order to explain a binary variable. Is it possible to compute a kind of "contribution" in order to know which input variable is the most contributive in my model to explain my target ?
Also, I picked up the Scoring code from my model but I don't understand the rules on it :
Can someone can explain to me what is compute in this rule (in red) because is not a probability..
else if _I28 = 1
then do;
_target_score_HPBNC1{1}+(-1.538266175076);
_target_score_HPBNC1{2}+(-0.241757117828);
end;
Best regards,
Bruno
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.