# Normalization of data in decision tree

After reading through a few references, I have come to know that for machine learning in general, it is necessary to normalize features so that no features are arbitrarily large ($centering$) and all features are on the same scale ($scaling$).

However, I'm having a bit of difficulty in visualizing the impact of this for a decision tree. Does data normalization impact the decision tree structure? If yes, how?

• If a column you are using is a combination of two others, then it might be a good idea to normalise those beforehand (or rescale in some manner so that each column contributes in the way you want to the final value). Sep 16, 2019 at 8:23