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Interpreting a nonparametric regression model with many predictors is knownto be a challenging problem. There has been renewed interest in this topic dueto the extensive use of machine learning algorithms and the difficulty inunderstanding and explaining their input-output relationships. This paperdevelops a unified framework using a derivative-based approach for existingtools in the literature, including the partial-dependence plots, marginal plotsand accumulated effects plots. It proposes a new interpretation techniquecalled the accumulated total derivative effects plot and demonstrates how itscomponents can be used to develop extensive insights in complex regressionmodels with correlated predictors. The techniques are illustrated throughsimulation results.

Model Interpretation: A Unified Derivative-based Framework for Nonparametric Regression and Supervised Machine Learning