Are Linear Regression Models White Box and Interpretable?

Explainable artificial intelligence (XAI) is a set of tools and algorithms that applied or embedded to machine learning models to understand and interpret the models. They are recommended especially for complex or advanced models including deep neural network because they are not interpretable from human point of view. On the other hand, simple models including linear regression are easy to implement, has less computational complexity and easy to visualize the output. The common notion in the literature that simple models including linear regression are considered as "white box" because they are more interpretable and easier to understand. This is based on the idea that linear regression models have several favorable outcomes including the effect of the features in the model and whether they affect positively or negatively toward model output. Moreover, uncertainty of the model can be measured or estimated using the confidence interval. However, we argue that this perception is not accurate and linear regression models are not easy to interpret neither easy to understand considering common XAI metrics and possible challenges might face. This includes linearity, local explanation, multicollinearity, covariates, normalization, uncertainty, features contribution and fairness. Consequently, we recommend the so-called simple models should be treated equally to complex models when it comes to explainability and interpretability. Supplementary Material File (xai_for_linear_model.pdf) - Download - 833.50 KB Information & Authors Information Version history Copyright This work is licensed under a Creative Commons Attribution 4.0 International License

Authors Metrics & Citations Metrics Article Usage 316views 146downloads Citations Download citation Ahmed M Salih, Yuhe Wang. Are Linear Regression Models White Box and Interpretable?. Authorea. 17 July 2024. DOI: https://doi.org/10.22541/au.172122905.57636646/v1 DOI: https://doi.org/10.22541/au.172122905.57636646/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu. Cited by - Using Chemometrics for Unraveling Complex Property Activity Relationships in Zeolite Catalyzed Sugar Conversion, ChemCatChem, 18, 2, (2026).https://doi.org/10.1002/cctc.202501572 - Illuminating Industry Evolution: Reframing Artificial Intelligence Through Transparent Machine Reasoning, Information, 16, 12, (1044), (2025).https://doi.org/10.3390/info16121044 - Acoustic parameter combinations underlying mapping of pseudoword sounds to multiple domains of meaning: Representational similarity analyses and machine-learning models, The Journal of the Acoustical Society of America, 158, 6, (4243-4267), (2025).https://doi.org/10.1121/10.0041768 - Analysis of adhesive degradation mechanisms and strength prediction based on principal component analysis, The Journal of Adhesion, (1-25), (2025).https://doi.org/10.1080/00218464.2025.2591141 - A Hybrid Approach for Seasonal Rainfall Forecasting Across Vietnam Using Convolutional Neural Networks and Dynamical Downscaling, Pure and Applied Geophysics, 182, 11, (4825-4844), (2025).https://doi.org/10.1007/s00024-025-03838-4 - An Explainable Deep Learning Method With Squeeze and Excitation Block for 6‐Hour Forecast of Tropical Cyclone Remote Precipitation, Journal of Geophysical Research: Machine Learning and Computation, 2, 3, (2025).https://doi.org/10.1029/2025JH000675 - Opinion mining, context analysis, and global sustainable waste management, Ecological Indicators, 178, (113944), (2025).https://doi.org/10.1016/j.ecolind.2025.113944 - Explainable Artificial Intelligence: A Perspective on Drug Discovery, Pharmaceutics, 17, 9, (1119), (2025).https://doi.org/10.3390/pharmaceutics17091119 - A comprehensive evaluation of interpretable artificial intelligence for epileptic seizure diagnosis using an electroencephalogram: A systematic review, DIGITAL HEALTH, 11, (2025).https://doi.org/10.1177/20552076251325411 - Contrasting rule and machine learning based digital self triage systems in the USA, npj Digital Medicine, 7, 1, (2024).https://doi.org/10.1038/s41746-024-01367-3 Loading...