In real world problems, it is quite often that we build prediction models with both continuous and categorical variables. My most naive approach to preprocessing is:
- Turn categorical variables into integers. (male,female)-> 0,1 etc
- then normalize all features, both categorical and continuous variables
- experiment with different predicting models and parameters...
I stopped asking questions long time about weather it makes sense turning categorical variables into integers and then normalize it. Nor do I even consider if it makes sense to put variables of both type into any model and test it.
My question is do we treat categorical variables just like continuous variables during prediction?