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Scalable and Efficient Hypothesis Testing with Random Forests
v1v2v3 (latest)

Scalable and Efficient Hypothesis Testing with Random Forests

16 April 2019
T. Coleman
Wei Peng
L. Mentch
ArXiv (abs)PDFHTML

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
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
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
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
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
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
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
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