影响范围评估（IRA）：回归建模中的可解释敏感性度量
Impact Range Assessment (IRA): An Interpretable Sensitivity Measure for Regression Modelling
AI总结：
本文提出IRA方法，用于回归建模中评估预测变量的影响范围，提高模型的可解释性和可靠性。
摘要：
尽管回归模型能够捕捉预测变量与响应变量之间的关系，但它们通常缺乏直观的辅助方法来理解预测变量对结果的影响。为此，我们引入了一种可解释性方法，称为影响范围评估（IRA），它通过测量响应变量在预测变量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值高于无关变量。此外，重复评估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。使用一个预测颗粒质量的模型进行的案例研究显示，IRA提供了一种简单直观的方法来解释和排序预测变量的影响，从而提高模型的透明度和可靠性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
