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My data consists of several continuous measurements and some dummy variables representing the years the measurements have been made. Now, I want to learn a neural network with the data. Therefore, I am zScore-normalizing all variables, including the dummy variables. However, I wonder if this is a reasonable approach, because normalizing the dummy variables alters their ranges, which I guess makes them less comparable if their distributions differ. On the other hand, not normalizing the dummy variables might also be questionable, because without normalization their influence on the networks output might be suboptimal.

What is the best approach to deal with dummy variables, normalizing them (zScore) or just leaving them as they are?

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Normalization would be required if you are doing some form a similarity measurement.

Dummy variables by its nature acts as a binary switch. Coding it as (0,1) or (-.5,.5) should have no impact on the relationships it exhibits to a dependent variable, if what you are trying to do is some form or regression or classification.

It would matter if you are performing clustering because it would be scale dependent.

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Normalizing dummy variables makes no sense. Usually, normalization is used when the variables are measured on different scales such that a proper comparison is not possible. With dummy variables, however, one puts just a binary information in the model and if it is normalized the information of the impact of e. g. one year is lost.

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  • $\begingroup$ So, according to the question, how do we deal with loss function? $\endgroup$ Jul 19, 2018 at 2:45

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