A simple approach for local and global variable importance in nonlinear regression models
提出一种名为GOALS的新算子，可同时评估非线性模型中特征的局部和全局重要性，并通过高斯过程回归在生物医学数据上展示其灵活高效性。
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 (i) a global scale, where the goal is to rank features based on their contributions to overall variation in an observed population, or (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 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.