REVIEW article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Techniques for Mitigating Overfitting in Machine Learning: A Comprehensive Review, Taxonomy, and Practical Guide
1. Legacy Health, Portland, United States
2. the vancouver clinic, Vancouver, United States
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Abstract
ABSTRACT Introduction: Overfitting remains a persistent barrier to reliable machine learning, especially in modern overparameterized deep models. Methods: We conducted a narrative review synthesizing approximately 95 core studies (1943–2026) identified through structured searches of IEEE Xplore, ACM Digital Library, arXiv, Google Scholar, and Semantic Scholar, extracting mechanisms, assumptions, and empirical evidence for prominent methods for mitigating overfitting. Results: We organize techniques into a unified five-family taxonomy (parameter-, training-, data-, ensemble-, and objective-based) and provide a practical decision framework that maps data regimes, model families, and real-world scenarios to actionable regularization strategies. Conclusion: Overfitting mitigation benefits from coordinated choices in data, model capacity, optimization, and evaluation. Our taxonomy and decision framework help practitioners select complementary interventions and avoid common pitfalls such as leakage and over-regularization.
Summary
Keywords
Data augmentation, deep learning, Ensembling, generalization, Model selection, Narrativereview, overfitting, regularization
Received
23 January 2026
Accepted
23 February 2026
Copyright
© 2026 Sheppert. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Alex Sheppert
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.