  Complex models are commonly used in predictive modeling. In this paper we
present R packages that can be used to explain predictions from complex black
box models and attribute parts of these predictions to input features. We
introduce two new approaches and corresponding packages for such attribution,
namely live and breakDown. We also compare their results with existing
implementations of state of the art solutions, namely lime that implements
Locally Interpretable Model-agnostic Explanations and ShapleyR that implements
Shapley values.
