If in a multivariate model we have several continuous variables and some categorical ones, we have to change the categoricals to dummy variables containing either 0 or 1.
Now to put all the variables together to calibrate a regression or classification model, we need to scale the variables.
Scaling a continuous variable is a meaningful process. But doing the same with columns containing 0 or 1 does not seem to be ideal. The dummies will not have their "fair share" of influencing the calibrated model.
Is there a solution to this?
I would appreciate to specifically know your opinion on this:
Rasmus Bro José Manuel Amigo Lars Nørgaard
I am not exactly sure what you mean by "scale the variables," but it is appropriate to include nominal variables represented by binary representations (1 less than the number of categories in each case) and continuous variables as well.
John
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