Techniques for mitigating overfitting in machine learning: a comprehensive review, taxonomy, and practical guide

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.