A question that frequently comes up while presenting the findings of a predictive model to a business audience (with non-statistical background) is: which variable/predictor is most important in explaining the phenomenon being modeled? Or they ask for the set of predictors included in the final model to be listed as per their importance.
Now, what measure of "importance" should I be using?:
I could use p-values for the predictors as a rough measure of importance, though am not sure if that will be accurate
Coefficient values could be another option to compare relative importance, but given that different variables may be on different scales it won't be an apple-to-apple comparison
I tend to prefer the the list of important variables that the Random Forest algorithm generates (for e.g. using the
VarImpfunction in the
randomForestpackage in R)
Are there any better variable importance measures that I can look at?