In many machine learning algorithms, feature scaling (aka variable scaling, normalization) is a common prepocessing step Wikipedia - Feature Scaling -- this question was close Question#41704 - How and why do normalization and feature scaling work?
I have two questions specifically in regards to Decision Trees:
- Are there any decision tree implementations that would require feature scaling? I am under the impression that most algorithms' splitting criteria are indifferent to scale.
- Consider these variables: (1) Units, (2) Hours, (3) Units per Hour -- is it best to leave these three variables "as-is" when fed into a decision tree or do we run into some type of conflict since the "normalized" variable (3) is relatable to (1) and (2)? That is, would you attack this situation by throwing all three variables into the mix, or would you typically choose some combination of the three or simply use the "normalized/standardized" feature (3)?