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Generalized SHAP: Generating multiple types of explanations in machine
  learning

Generalized SHAP: Generating multiple types of explanations in machine learning

12 June 2020
Dillon Bowen
L. Ungar
    FAtt
ArXivPDFHTML

Papers citing "Generalized SHAP: Generating multiple types of explanations in machine learning"

5 / 5 papers shown
Title
Explaining Probabilistic Models with Distributional Values
Explaining Probabilistic Models with Distributional Values
Luca Franceschi
Michele Donini
Cédric Archambeau
Matthias Seeger
FAtt
37
2
0
15 Feb 2024
Glocal Explanations of Expected Goal Models in Soccer
Glocal Explanations of Expected Goal Models in Soccer
Mustafa Cavus
Adrian Stando
P. Biecek
35
4
0
29 Aug 2023
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
15
0
0
12 Dec 2022
An Imprecise SHAP as a Tool for Explaining the Class Probability
  Distributions under Limited Training Data
An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data
Lev V. Utkin
A. Konstantinov
Kirill Vishniakov
FAtt
29
5
0
16 Jun 2021
Ensembles of Random SHAPs
Ensembles of Random SHAPs
Lev V. Utkin
A. Konstantinov
FAtt
16
20
0
04 Mar 2021
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