Dummy variables and accuracy



Dummy variables at times provide greater accuracy than continuous or categorical variables. What is the mathematical explanation behind the same ?

Thanks and regards with best wishes


I would say comparing features created from dummy variable represent a completely different information than their original categorical/continuous variables. So comparing them would not be correct.

Comparing dummy values of features and their original features would be same as comparing two different features.


If you have age as predictor, and dummied at 60, for instance, and the target is a disease or condition that affects people only 60+ , the model could be more accurate with the dummy.


Thanks a lot Faizy.

But I am more interested about the mathematical explanations of the same.

Why in some business cases dummy variables produce more accuracy and what is the mathematical explanation ?

Or is it purely business logic ?


Thanks a lot for your answer leoldv.

But I am interested in the mathematical explanation of the dummy variables.

Why mathematically does dummy variables produce more accuracy in some cases ?