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

12 February 2021

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

8 / 8 papers shown

Title
A Comprehensive Study of Shapley Value in Data Analytics Hong Lin Shixin Wan Zhongle Xie Ke Chen Meihui Zhang Lidan Shou Gang Chen 97 0 0 02 Dec 2024
Improving the Weighting Strategy in KernelSHAP Lars Henry Berge Olsen Martin Jullum TDI FAtt 77 2 0 07 Oct 2024
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 23 3 0 17 Mar 2023
Shapley Curves: A Smoothing Perspective Ratmir Miftachov Georg Keilbar Wolfgang Karl Härdle FAtt 40 1 0 23 Nov 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 32 17 0 26 Nov 2021
Explaining by Removing: A Unified Framework for Model Explanation Ian Covert Scott M. Lundberg Su-In Lee FAtt 48 243 0 21 Nov 2020
Feature Removal Is a Unifying Principle for Model Explanation Methods Ian Covert Scott M. Lundberg Su-In Lee FAtt 33 33 0 06 Nov 2020
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R Marvin N. Wright A. Ziegler 113 2,735 0 18 Aug 2015