Combining effects of dummy variables in a regression model
You may google SAS STAT manual /User guide. Check out any major regression procedures there that support Class statement. Underneath the Class it details Reference... option. They all detail how a design matrix is fashioned out. The way you fed your 100 dummies must have been obvious enough to trigger JMP to roll back to a temp class variable that re-engineer back to one single variable. If you want to know how exactly JMP is triggered to do roll back, go to JMP website and open a technical support track. But mechanically I am confident this is what happens.
I am building a regression model with about 300 features using Python Sklearn. One of the features has over 100 categories and I end up having ~100 dummy columns for this feature.Now each of the dummy column has its own coefficient, or a feature ranking score (if using Random Forest or xgb) - which is something I dont like.  However, when I create the same model in SAS JMP, it gives me one single feature score for the feature with 100 categories -it apparently handles categories automatically. Can someone tell me how SAS JMP combines the coefficients/feature importances of 100 dummy variables into one metric. And how can i can achieve the same in Python.