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

Alexander Pearson Sheppert

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DOI: 10.3389/frai.2026.1794271
Published: 2026-03-24
Container: Frontiers in Artificial Intelligence
Publisher: Frontiers Media SA
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3389/frai.2026.1794271,
  title = {Techniques for mitigating overfitting in machine learning: a comprehensive review, taxonomy, and practical guide},
  author = {Alexander Pearson Sheppert},
  year = {2026},
  journal = {Frontiers in Artificial Intelligence},
  doi = {10.3389/frai.2026.1794271},
  url = {https://doi.org/10.3389/frai.2026.1794271}
}

RIS

TY  - JOUR
TI  - Techniques for mitigating overfitting in machine learning: a comprehensive review, taxonomy, and practical guide
AU  - Alexander Pearson Sheppert
PY  - 2026
JO  - Frontiers in Artificial Intelligence
DO  - 10.3389/frai.2026.1794271
UR  - https://doi.org/10.3389/frai.2026.1794271
ER  -

APA

Sheppert, A. P. (2026). Techniques for mitigating overfitting in machine learning: a comprehensive review, taxonomy, and practical guide. Frontiers in Artificial Intelligence. https://doi.org/10.3389/frai.2026.1794271

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crossref · retrieved 2026-09-25T18:01:02.773Z