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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.05239 |
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| _version_ | 1866908814253490176 |
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| author | You, Jihao Tulpan, Dan Diao, Jiaojiao Ellis, Jennifer L. |
| author_facet | You, Jihao Tulpan, Dan Diao, Jiaojiao Ellis, Jennifer L. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_05239 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Impact Range Assessment (IRA): An Interpretable Sensitivity Measure for Regression Modelling You, Jihao Tulpan, Dan Diao, Jiaojiao Ellis, Jennifer L. Methodology 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. |
| title | Impact Range Assessment (IRA): An Interpretable Sensitivity Measure for Regression Modelling |
| topic | Methodology |
| url | https://arxiv.org/abs/2602.05239 |