How does this interaction affect me? Interpretable attribution for feature interactions

Understand

Machine learning transparency calls for interpretable explanations of how inputs relate to predictions.

• Feature attribution is a way to analyze the impact of features on predictions.
• Feature interactions are the contextual dependence between features that jointly impact predictions.
• There are a number of methods that extract feature interactions in prediction models; however, the methods that assign attributions to interactions are either uninterpretable, model-specific, or non-axiomatic.