Ah yes so originally my target race was a nominal variable which had 6 levels - Black, White, Asian, Native, Hispanic and Other and after research, I understood that SAS EM isn't suited for multinomial analysis so I converted this into a binary target variable in SAS Studio with it becoming 1 = Black, Native and Asian and 0 = White, Hispanic and Other. so its easier to do predictive modeling for race as target variable.
Now if i want to use the race as independent to do modeling for mental illness as a target, this would be an example output
So for example, if I was using race as a binary and I modelled a decision tree it would say for ex;
if race = 1 and if ... then
mental illness 0 = 0.6
mental illness 1 = 0.4
it would tell me that races white, Hispanic and other were more likely to not have a mental illness based on,.etcetc
Overall, I just wanted to see some opinions on what would be better suited, to go with race in its original format or binary format as an independent variable to do predictive modelling for mental illness as a target.
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