Explanation Shift: Detecting distribution shifts on tabular data via the explanation space
Abstract
As input data distributions evolve, the predictive performance of machine learning models tends to deteriorate. In the past, predictive performance was considered the key indicator to monitor. However, explanation aspects have come to attention within the last years. In this work, we investigate how model predictive performance and model explanation characteristics are affected under distribution shifts and how these key indicators are related to each other for tabular data. We find that the modeling of explanation shifts can be a better indicator for the detection of predictive performance changes than state-of-the-art techniques based on representations of distribution shifts. We provide a mathematical analysis of different types of distribution shifts as well as synthetic experimental examples.
Cite
@article{arxiv.2210.12369,
title = {Explanation Shift: Detecting distribution shifts on tabular data via the explanation space},
author = {Carlos Mougan and Klaus Broelemann and Gjergji Kasneci and Thanassis Tiropanis and Steffen Staab},
journal= {arXiv preprint arXiv:2210.12369},
year = {2022}
}
Comments
Neural Information Processing Systems (NeurIPS 2022). Workshop on Distribution Shifts: Connecting Methods and Applications