  The ability to interpret machine learning models has become increasingly
important as their usage in data science continues to rise. Most current
interpretability methods are optimized to work on either (\textit{i}) a global
scale, where the goal is to rank features based on their contributions to
overall variation in an observed population, or (\textit{ii}) the local level,
which aims to detail on how important a feature is to a particular individual
in the data set. In this work, a new operator is proposed called the "GlObal
And Local Score" (GOALS): a simple \textit{post hoc} approach to simultaneously
assess local and global feature variable importance in nonlinear models.
Motivated by problems in biomedicine, the approach is demonstrated using
Gaussian process regression where the task of understanding how genetic markers
are associated with disease progression both within individuals and across
populations is of high interest. Detailed simulations and real data analyses
illustrate the flexible and efficient utility of GOALS over state-of-the-art
variable importance strategies.
