Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features

• URL: http://arxiv.org/abs/2111.13507v1
• Date: Fri, 26 Nov 2021 14:05:45 GMT
• Title: Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features
• Authors: Lars Henry Berge Olsen, Ingrid Kristine Glad, Martin Jullum and Kjersti Aas
• Abstract summary: We use a variational autoencoder with arbitrary conditioning (VAEAC) to model all feature dependencies simultaneously. We apply VAEAC to the Abalone data set from the UCI Machine Learning Repository.
• Score: 2.064612766965483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shapley values are today extensively used as a model-agnostic explanation framework to explain complex predictive machine learning models. Shapley values have desirable theoretical properties and a sound mathematical foundation. Precise Shapley value estimates for dependent data rely on accurate modeling of the dependencies between all feature combinations. In this paper, we use a variational autoencoder with arbitrary conditioning (VAEAC) to model all feature dependencies simultaneously. We demonstrate through comprehensive simulation studies that VAEAC outperforms the state-of-the-art methods for a wide range of settings for both continuous and mixed dependent features. Finally, we apply VAEAC to the Abalone data set from the UCI Machine Learning Repository.

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