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

TL;DR

This paper introduces GOALS, a simple post hoc method for assessing both local and global feature importance in nonlinear models, demonstrated on biomedical data with Gaussian process regression.

Contribution

The paper presents GOALS, a novel, easy-to-apply operator that assesses local and global variable importance simultaneously in nonlinear models.

Findings

GOALS outperforms existing methods in simulations.

Effective in biomedical genetic data analysis.

Flexible and computationally efficient.

Abstract

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.