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For supervised machine learning for prediction, if I had some feature variables that are real, and also some features that are categorical--which have been coded using dummy variables (010, 001 etc)--I have normalized the real variables so that each of them sums to one. I am wondering what kind of a preprocessing I should do for the rest of the categorical features, before I run cross-validation routines and regression methods.

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    $\begingroup$ Why have you normalised the real ones to add to 1? I don't think you need to normalise the dummy ones. $\endgroup$ – Dirk Nachbar Sep 18 '12 at 21:38
  • $\begingroup$ I need a more concrete answer than an "I think" $\endgroup$ – qlinck Sep 18 '12 at 22:19
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Normalizing categorical variables ought not have any real effect.

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