Timezone: »

Variable Importance Using Decision Trees
Jalil Kazemitabar · Arash Amini · Adam Bloniarz · Ameet S Talwalkar

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #207

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. We provide novel insights into the performance of these methods by deriving finite sample performance guarantees in a high-dimensional setting under various modeling assumptions. We further demonstrate the effectiveness of these impurity-based methods via an extensive set of simulations.

Author Information

Jalil Kazemitabar (University of California, Los Angeles)
Arash Amini (UCLA)
Adam Bloniarz (Google)
Ameet S Talwalkar (CMU)

More from the Same Authors