Related papers: Explanation Shift: Detecting distribution shifts on tabular data via the explanation space

Explanation Shift: Detecting distribution shifts on tabular data via the explanation space

URL: http://arxiv.org/abs/2210.12369v1
Date: Sat, 22 Oct 2022 06:47:13 GMT
Title: Explanation Shift: Detecting distribution shifts on tabular data via the explanation space
Authors: Carlos Mougan, Klaus Broelemann, Gjergji Kasneci, Thanassis Tiropanis, Steffen Staab
Abstract summary: We investigate how model predictive performance and model explanation characteristics are affected under distribution shifts. We find that the modeling of explanation shifts can be a better indicator for the detection of predictive performance changes.
Score: 13.050516715665166
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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