Variable Importance Using Decision Trees

NeurIPS 2017 Jalil KazemitabarArash AminiAdam BloniarzAmeet S. Talwalkar

Decision trees and random forests are well established models that not only offer good predictive performance, but also provide rich feature importance information. While practitioners often employ variable importance methods that rely on this impurity-based information, these methods remain poorly characterized from a theoretical perspective... (read more)

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