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The ability to interpret machine learning models has become increasinglyimportant as their usage in data science continues to rise. Most currentinterpretability methods are optimized to work on either (\textit{i}) a globalscale, where the goal is to rank features based on their contributions tooverall variation in an observed population, or (\textit{ii}) the local level,which aims to detail on how important a feature is to a particular individualin the data set. In this work, a new operator is proposed called the "GlObalAnd Local Score" (GOALS): a simple \textit{post hoc} approach to simultaneouslyassess local and global feature variable importance in nonlinear models.Motivated by problems in biomedicine, the approach is demonstrated usingGaussian process regression where the task of understanding how genetic markersare associated with disease progression both within individuals and acrosspopulations is of high interest. Detailed simulations and real data analysesillustrate the flexible and efficient utility of GOALS over state-of-the-artvariable importance strategies.

A Simple Approach for Local and Global Variable Importance in Nonlinear Regression Models