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Consistent Individualized Feature Attribution for Tree Ensembles

Consistent Individualized Feature Attribution for Tree Ensembles

12 February 2018
Scott M. Lundberg
G. Erion
Su-In Lee
    FAtt
    TDI
ArXivPDFHTML

Papers citing "Consistent Individualized Feature Attribution for Tree Ensembles"

13 / 13 papers shown
Title
MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model
MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model
Alexander Koebler
Ingo Thon
Florian Buettner
55
0
0
26 Mar 2025
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
131
0
0
11 Feb 2025
Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory
Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory
Fabian Fumagalli
Maximilian Muschalik
Eyke Hüllermeier
Barbara Hammer
J. Herbinger
FAtt
185
4
0
22 Dec 2024
Provably Accurate Shapley Value Estimation via Leverage Score Sampling
Provably Accurate Shapley Value Estimation via Leverage Score Sampling
Christopher Musco
R. Teal Witter
FAtt
FedML
TDI
86
4
0
02 Oct 2024
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients
Evandro S. Ortigossa
Fábio F. Dias
Brian Barr
Claudio T. Silva
L. G. Nonato
FAtt
106
3
0
25 Apr 2024
Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring
Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring
Hué Sullivan
Hurlin Christophe
Pérignon Christophe
Saurin Sébastien
51
0
0
12 Dec 2022
On the Tractability of SHAP Explanations
On the Tractability of SHAP Explanations
Guy Van den Broeck
A. Lykov
Maximilian Schleich
Dan Suciu
FAtt
TDI
57
275
0
18 Sep 2020
Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A
  Top-Down Approach
Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach
A. Moawad
E. Islam
Namdoo Kim
R. Vijayagopal
A. Rousseau
Wei Biao Wu
58
5
0
15 Jun 2020
A Unified Approach to Interpreting Model Predictions
A Unified Approach to Interpreting Model Predictions
Scott M. Lundberg
Su-In Lee
FAtt
1.1K
21,906
0
22 May 2017
XGBoost: A Scalable Tree Boosting System
XGBoost: A Scalable Tree Boosting System
Tianqi Chen
Carlos Guestrin
796
38,961
0
09 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAtt
FaML
1.2K
16,976
0
16 Feb 2016
How to Explain Individual Classification Decisions
How to Explain Individual Classification Decisions
D. Baehrens
T. Schroeter
Stefan Harmeling
M. Kawanabe
K. Hansen
K. Müller
FAtt
130
1,103
0
06 Dec 2009
Variable importance in binary regression trees and forests
Variable importance in binary regression trees and forests
H. Ishwaran
193
386
0
15 Nov 2007
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