Bias-variance tradeoff in machine learning: Theoretical formulation and implications to structural engineering applications
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The technical evolution of ML models, especially large neural networks, has begun to challenge traditional validation error minimization as the ideal strategy to optimize the bias-variance tradeoff during model training.
“In recent years, the technical evolution of ML models, and especially the rise of large neural network architectures, has begun to challenge the traditional approach of validation (or cross-validation) error minimization as the ideal strategy to optimize the bias-variance tradeoff during model training.17, 18, 19, 20”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [17]
Noise, bias, and variance are three components that lead to model performance degradation and negatively affect generalization to new data.
“From a mathematical point of view, noise (the joint probability distribution between training and test/inference samples), bias, and variance are the three components that lead to model performance degradation and negatively affect generalization to new data.17”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [17]
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Cha, Y., Choi, W., Suh, G., Mahmoudkhani, S., & Büyüköztürk, O. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12334 · Cited by 1,558 · see citing works
Azimi, M., Eslamlou, A., & Pekcan, G. (2020) · Sensors · doi:10.3390/s20102778 · Cited by 769 · see citing works
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Koçak, B., Ponsiglione, A., Stanzione, A., Bluethgen, C., Santinha, J., Ugga, L., Huisman, M., Klontzas, M. E., Cannella, R., & Cuocolo, R. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242854 · Cited by 168 · see citing works
“17 Given the somewhat irreducible nature of noise, ML has focused mostly on addressing bias and variance when optimizing model performance during the hyperparam-eter tuning process.”
- Authors
- Guan, X., & Burton, H.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Guan, Xingquan
- Venue
- Structures
- Publisher
- Elsevier BV
- Published
- 2022-12
- Volume
- 46
- Pages
- 17-30
- ISSN
- 2352-0124 (ISSN-L)
- OpenAlex
- W4307056866
- Cited by
- 47
- References
- 2
- Citations by year
- 2026: 18 · 2025: 14 · 2024: 13 · 2023: 2
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Structures (acceptedVersion)
- Funders
- National Science Foundation (1554714)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- bias-variance trade-off, overfitting, model generalization, model complexity, double descent, model parameter selection, structural engineering applications, testing error
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