There are common ways to split a tree in decision trees and all their variants:

  • Gini Index
  • Entropy
  • Misclassification

Why there is not a method which uses directly AUC or accuracy (or whichever the modeler need) to split the nodes.

Is it because of common use, or there is a mathematical explanation for it?


1 Answer 1


On accuracy:
Why we use information gain over accuracy as splitting criterion in decision tree?

AUC has been explored; it seems to work well, but is slower:


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