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A $k$-additive Choquet integral-based approach to approximate the SHAP
  values for local interpretability in machine learning

A kkk-additive Choquet integral-based approach to approximate the SHAP values for local interpretability in machine learning

3 November 2022
G. D. Pelegrina
L. Duarte
M. Grabisch
    FAtt
    TDI
ArXivPDFHTML

Papers citing "A $k$-additive Choquet integral-based approach to approximate the SHAP values for local interpretability in machine learning"

4 / 4 papers shown
Title
shapiq: Shapley Interactions for Machine Learning
shapiq: Shapley Interactions for Machine Learning
Maximilian Muschalik
Hubert Baniecki
Fabian Fumagalli
Patrick Kolpaczki
Barbara Hammer
Eyke Hüllermeier
TDI
14
9
0
02 Oct 2024
KernelSHAP-IQ: Weighted Least-Square Optimization for Shapley
  Interactions
KernelSHAP-IQ: Weighted Least-Square Optimization for Shapley Interactions
Fabian Fumagalli
Maximilian Muschalik
Patrick Kolpaczki
Eyke Hüllermeier
Barbara Hammer
22
6
0
17 May 2024
Looking Deeper into Tabular LIME
Looking Deeper into Tabular LIME
Damien Garreau
U. V. Luxburg
FAtt
LMTD
104
30
0
25 Aug 2020
A Random Forest Guided Tour
A Random Forest Guided Tour
Gérard Biau
Erwan Scornet
AI4TS
131
2,701
0
18 Nov 2015
1