Jalil Kazemitabar

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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(More)
We present a complete presentation of the theoretical results presented in the main text. We provide detailed analysis of the DStump algorithm in the context of a general additive regression model with uncorrelated design. We derive the results for the linear case as special case of the general theory. Our analysis is high-dimensional and non-asymptotic,(More)
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