  Machine learning models can automatically learn complex relationships, such
as non-linear and interaction effects. Interpretable machine learning methods
such as partial dependence plots visualize marginal feature effects but may
lead to misleading interpretations when feature interactions are present.
Hence, employing additional methods that can detect and measure the strength of
interactions is paramount to better understand the inner workings of machine
learning models. We demonstrate several drawbacks of existing global
interaction detection approaches, characterize them theoretically, and evaluate
them empirically. Furthermore, we introduce regional effect plots with implicit
interaction detection, a novel framework to detect interactions between a
feature of interest and other features. The framework also quantifies the
strength of interactions and provides interpretable and distinct regions in
which feature effects can be interpreted more reliably, as they are less
confounded by interactions. We prove the theoretical eligibility of our method
and show its applicability on various simulation and real-world examples.
