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Why scaling is not needed for Decision Tree based algorithms like Random forest and Xgboost?


Very good question.

Think about the micro decision in decision tree 10 < X < 20

If we scale the features down by say 100, then the above condition will become 0.01 < X < .02

Even after scaling down the decision will not change right.

Hence, decision tree models like Random forest and XGboost don’t require feature scaling.

I hope this answers your question.