  Interactions such as double negation in sentences and scene interactions in
images are common forms of complex dependencies captured by state-of-the-art
machine learning models. We propose Mah\'e, a novel approach to provide
Model-agnostic hierarchical \'explanations of how powerful machine learning
models, such as deep neural networks, capture these interactions as either
dependent on or free of the context of data instances. Specifically, Mah\'e
provides context-dependent explanations by a novel local interpretation
algorithm that effectively captures any-order interactions, and obtains
context-free explanations through generalizing context-dependent interactions
to explain global behaviors. Experimental results show that Mah\'e obtains
improved local interaction interpretations over state-of-the-art methods and
successfully explains interactions that are context-free.
