$PredDiff$: Explanations and Interactions from Conditional Expectations

• URL: http://arxiv.org/abs/2102.13519v1
• Date: Fri, 26 Feb 2021 14:46:47 GMT
• Title: $PredDiff$: Explanations and Interactions from Conditional Expectations
• Authors: Stefan Bl\"ucher and Nils Strodthoff
• Abstract summary: $PredDiff$ is a model-agnostic, local attribution method rooted in probability theory. In this work, we clarify properties of $PredDiff$ and put forward several extensions of the original formalism.
• Score: 0.3655021726150368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: $PredDiff$ is a model-agnostic, local attribution method that is firmly rooted in probability theory. Its simple intuition is to measure prediction changes when marginalizing out feature variables. In this work, we clarify properties of $PredDiff$ and put forward several extensions of the original formalism. Most notably, we introduce a new measure for interaction effects. Interactions are an inevitable step towards a comprehensive understanding of black-box models. Importantly, our framework readily allows to investigate interactions between arbitrary feature subsets and scales linearly with their number. We demonstrate the soundness of $PredDiff$ relevances and interactions both in the classification and regression setting. To this end, we use different analytic, synthetic and real-world datasets.

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