  Interpreting a nonparametric regression model with many predictors is known
to be a challenging problem. There has been renewed interest in this topic due
to the extensive use of machine learning algorithms and the difficulty in
understanding and explaining their input-output relationships. This paper
develops a unified framework using a derivative-based approach for existing
tools in the literature, including the partial-dependence plots, marginal plots
and accumulated effects plots. It proposes a new interpretation technique
called the accumulated total derivative effects plot and demonstrates how its
components can be used to develop extensive insights in complex regression
models with correlated predictors. The techniques are illustrated through
simulation results.
