An unexpected unity among methods for interpreting model predictions
  Understanding why a model made a certain prediction is crucial in many data
science fields. Interpretable predictions engender appropriate trust and
provide insight into how the model may be improved. However, with large modern
datasets the best accuracy is often achieved by complex models even experts
struggle to interpret, which creates a tension between accuracy and
interpretability. Recently, several methods have been proposed for interpreting
predictions from complex models by estimating the importance of input features.
Here, we present how a model-agnostic additive representation of the importance
of input features unifies current methods. This representation is optimal, in
the sense that it is the only set of additive values that satisfies important
properties. We show how we can leverage these properties to create novel visual
explanations of model predictions. The thread of unity that this representation
weaves through the literature indicates that there are common principles to be
learned about the interpretation of model predictions that apply in many
scenarios.
