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A Debiased MDI Feature Importance Measure for Random Forests

A Debiased MDI Feature Importance Measure for Random Forests

26 June 2019
Xiao Li
Yu Wang
Sumanta Basu
Karl Kumbier
Bin Yu
ArXivPDFHTML

Papers citing "A Debiased MDI Feature Importance Measure for Random Forests"

14 / 14 papers shown
Title
Feature Importance Depends on Properties of the Data: Towards Choosing the Correct Explanations for Your Data and Decision Trees based Models
Feature Importance Depends on Properties of the Data: Towards Choosing the Correct Explanations for Your Data and Decision Trees based Models
Célia Wafa Ayad
Thomas Bonnier
Benjamin Bosch
Sonali Parbhoo
Jesse Read
FAtt
XAI
103
0
0
11 Feb 2025
Regression Trees Know Calculus
Regression Trees Know Calculus
Nathan Wycoff
31
0
0
22 May 2024
Individualized and Global Feature Attributions for Gradient Boosted
  Trees in the Presence of $\ell_2$ Regularization
Individualized and Global Feature Attributions for Gradient Boosted Trees in the Presence of ℓ2\ell_2ℓ2​ Regularization
Qingyao Sun
31
2
0
08 Nov 2022
How important are socioeconomic factors for hurricane performance of
  power systems? An analysis of disparities through machine learning
How important are socioeconomic factors for hurricane performance of power systems? An analysis of disparities through machine learning
Alexys Herleym Rodríguez Avellaneda
A. Shafieezadeh
Alper Yılmaz
14
6
0
18 Aug 2022
FACT: High-Dimensional Random Forests Inference
FACT: High-Dimensional Random Forests Inference
Chien-Ming Chi
Yingying Fan
Jinchi Lv
32
2
0
04 Jul 2022
From global to local MDI variable importances for random forests and
  when they are Shapley values
From global to local MDI variable importances for random forests and when they are Shapley values
Antonio Sutera
Gilles Louppe
V. A. Huynh-Thu
L. Wehenkel
Pierre Geurts
FAtt
29
7
0
03 Nov 2021
Bias, Fairness, and Accountability with AI and ML Algorithms
Bias, Fairness, and Accountability with AI and ML Algorithms
Neng-Zhi Zhou
Zach Zhang
V. Nair
Harsh Singhal
Jie Chen
Agus Sudjianto
FaML
21
9
0
13 May 2021
MDA for random forests: inconsistency, and a practical solution via the
  Sobol-MDA
MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA
Clément Bénard
Sébastien Da Veiga
Erwan Scornet
47
49
0
26 Feb 2021
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest
  Feature Importance
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance
Mattia Carletti
M. Terzi
Gian Antonio Susto
36
42
0
21 Jul 2020
Trees, forests, and impurity-based variable importance
Trees, forests, and impurity-based variable importance
Erwan Scornet
FAtt
37
75
0
13 Jan 2020
Analyzing CART
Analyzing CART
Jason M. Klusowski
27
6
0
24 Jun 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
37
153
0
01 May 2019
Unbiased Measurement of Feature Importance in Tree-Based Methods
Unbiased Measurement of Feature Importance in Tree-Based Methods
Zhengze Zhou
Giles Hooker
14
63
0
12 Mar 2019
ranger: A Fast Implementation of Random Forests for High Dimensional
  Data in C++ and R
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
Marvin N. Wright
A. Ziegler
93
2,732
0
18 Aug 2015
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