Tree-based algorithms do not require feature scaling before fitting, and I am working on gradient boosted tree models (and random forest) without scaling features.

I'm curious if feature scaling affects the feature importance in a meaningful way?

To be specific,

  • When both X (explanatory) and Y are NOT scaled
  • When X (explanatory) is scaled and Y are not scaled
  • When both X and Y are scaled

If it does affect, what is the reason for that?



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