Shapley Based Residual Decomposition for Instance Analysis
Machine Learning
2023-05-31 v1 Artificial Intelligence
Abstract
In this paper, we introduce the idea of decomposing the residuals of regression with respect to the data instances instead of features. This allows us to determine the effects of each individual instance on the model and each other, and in doing so makes for a model-agnostic method of identifying instances of interest. In doing so, we can also determine the appropriateness of the model and data in the wider context of a given study. The paper focuses on the possible applications that such a framework brings to the relatively unexplored field of instance analysis in the context of Explainable AI tasks.
Keywords
Cite
@article{arxiv.2305.18818,
title = {Shapley Based Residual Decomposition for Instance Analysis},
author = {Tommy Liu and Amanda Barnard},
journal= {arXiv preprint arXiv:2305.18818},
year = {2023}
}
Comments
Accepted, 40th International Conference on Machine Learning