Explaining predictive models using Shapley values and non-parametric
vine copulas

Explaining predictive models using Shapley values and non-parametric vine copulas

12 February 2021

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Papers citing "Explaining predictive models using Shapley values and non-parametric vine copulas"

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Title
A Comprehensive Study of Shapley Value in Data Analytics Hong Lin Shixin Wan Zhongle Xie Ke Chen Meihui Zhang Lidan Shou Gang Chen 165 0 0 02 Dec 2024
A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them Lars Henry Berge Olsen I. Glad Martin Jullum K. Aas FAtt 85 12 0 16 May 2023
Approximation of group explainers with coalition structure using Monte Carlo sampling on the product space of coalitions and features Konstandinos Kotsiopoulos A. Miroshnikov Khashayar Filom Arjun Ravi Kannan FAtt 97 3 0 17 Mar 2023
Shapley Curves: A Smoothing Perspective Ratmir Miftachov Georg Keilbar Wolfgang Karl Härdle FAtt 95 1 0 23 Nov 2022
Algorithms to estimate Shapley value feature attributions Hugh Chen Ian Covert Scott M. Lundberg Su-In Lee TDI FAtt 93 235 0 15 Jul 2022
Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features Lars Henry Berge Olsen I. Glad Martin Jullum K. Aas TDI FAtt 100 17 0 26 Nov 2021
groupShapley: Efficient prediction explanation with Shapley values for feature groups Martin Jullum Annabelle Redelmeier K. Aas TDI FAtt 80 22 0 23 Jun 2021
Accurate Shapley Values for explaining tree-based models Salim I. Amoukou Nicolas Brunel Tangi Salaun TDI FAtt 55 15 0 07 Jun 2021
Explaining by Removing: A Unified Framework for Model Explanation Ian Covert Scott M. Lundberg Su-In Lee FAtt 138 252 0 21 Nov 2020
Feature Removal Is a Unifying Principle for Model Explanation Methods Ian Covert Scott M. Lundberg Su-In Lee FAtt 138 33 0 06 Nov 2020