Scalable and Efficient Hypothesis Testing with Random Forests

v1v2v3 (latest)

Scalable and Efficient Hypothesis Testing with Random Forests

16 April 2019

ArXiv (abs)PDF HTML

Papers citing "Scalable and Efficient Hypothesis Testing with Random Forests"

6 / 6 papers shown

Title
TrIM: Transformed Iterative Mondrian Forests for Gradient-based Dimension Reduction and High-Dimensional Regression Ricardo Baptista Eliza O'Reilly Yangxinyu Xie 94 2 0 13 Jul 2024
Sequential Permutation Testing of Random Forest Variable Importance Measures Alexander Hapfelmeier R. Hornung Bernhard Haller 59 15 0 02 Jun 2022
Getting Better from Worse: Augmented Bagging and a Cautionary Tale of Variable Importance L. Mentch Siyu Zhou 102 14 0 07 Mar 2020
Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success L. Mentch Siyu Zhou 87 72 0 01 Nov 2019
Asymptotic Distributions and Rates of Convergence for Random Forests via Generalized U-statistics Weiguang Peng T. Coleman L. Mentch 104 41 0 25 May 2019
Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance Giles Hooker L. Mentch Siyu Zhou 93 159 0 01 May 2019