🤖 AI Summary
This study addresses the lack of intuitive and interpretable methods for quantifying variable influence in existing regression models. To this end, the authors propose Impact Range Assessment (IRA), a novel approach that robustly measures and ranks predictor importance by evaluating the maximum potential change a predictor can induce in the response variable across its entire range of values, relative to the total variation observed in the response. Experiments on both synthetic linear and nonlinear datasets, as well as a real-world particulate matter prediction case, demonstrate that IRA effectively distinguishes relevant from irrelevant variables with consistent and reliable results. By providing a clear, quantitative interpretation of each variable’s contribution, IRA significantly enhances model transparency and trustworthiness.
📝 Abstract
While regression models capture the relationship between predictors and the response variable, they often lack intuitive accompanying methods to understand the influence of predictors on the outcome. To address this, we introduce an interpretability method called Impact Range Assessment (IRA), which quantifies the maximal influence of each predictor by measuring the total potential change in the response variable, across the predictor range. Validation using synthetic linear and nonlinear datasets demonstrates that relevant predictors produced higher IRA values than irrelevant ones. Moreover, repeated evaluations produced results closely aligned with those from the single-execution analysis, confirming the robustness of the method. A case study using a model that predicts pellet quality demonstrated that the IRA provides a simple and intuitive approach to interpret and rank predictor influence, thereby improving model transparency and reliability.