  Understanding why a model makes a certain prediction can be as crucial as the
prediction's accuracy in many applications. However, the highest accuracy for
large modern datasets is often achieved by complex models that even experts
struggle to interpret, such as ensemble or deep learning models, creating a
tension between accuracy and interpretability. In response, various methods
have recently been proposed to help users interpret the predictions of complex
models, but it is often unclear how these methods are related and when one
method is preferable over another. To address this problem, we present a
unified framework for interpreting predictions, SHAP (SHapley Additive
exPlanations). SHAP assigns each feature an importance value for a particular
prediction. Its novel components include: (1) the identification of a new class
of additive feature importance measures, and (2) theoretical results showing
there is a unique solution in this class with a set of desirable properties.
The new class unifies six existing methods, notable because several recent
methods in the class lack the proposed desirable properties. Based on insights
from this unification, we present new methods that show improved computational
performance and/or better consistency with human intuition than previous
approaches.
