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.

翻译：尽管回归模型能够捕捉预测变量与响应变量之间的关系，但往往缺乏直观的辅助方法来理解预测变量对结果的影响。为此，我们提出一种名为影响范围评估(IRA)的可解释性方法，该方法通过测量响应变量在预测变量整个取值范围内的总潜在变化，来量化每个预测变量的最大影响。使用合成的线性和非线性数据集进行验证表明，相关预测变量产生的IRA值高于无关预测变量。此外，重复评估产生的结果与单次执行分析的结果高度一致，证实了该方法的稳健性。通过一个预测颗粒质量的模型案例研究证明，IRA为解释和排序预测变量影响提供了一种简单直观的方法，从而提高了模型的透明度和可靠性。