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Journal of Animal Ecology - 2025 - Wilson - Practical guidelines for validation of supervised machine learning models in1.02 MB
Published Version (Advanced Access)CC BY V4.0, Open Access
Journal article
Practical guidelines for validation of supervised machine learning models in accelerometer-based animal behaviour classification
Journal of Animal Ecology, Vol.94(7), pp.1322-1334
2025
PMID: 40387610
Appears in UniSC Supported Open Access Outputs
Abstract
Supervised machine learning has been used to detect fine-scale animal behaviour from accelerometer data, but a standardised protocol for implementing this workflow is currently lacking. As the application of machine learning to ecological problems expands, it is essential to establish technical protocols and validation standards that align with those in other ‘big data’ fields.
Overfitting is a prevalent and often misunderstood challenge in machine learning. Overfit models overly adapt to the training data to memorise specific instances rather than to discern the underlying signal. Associated results can indicate high performance on the training set, yet these models are unlikely to generalise to new data. Overfitting can be detected through rigorous validation using independent test sets.
Our systematic review of 119 studies using accelerometer-based supervised machine learning to classify animal behaviour reveals that 79% (94 papers) did not validate their models sufficiently well to robustly identify potential overfitting. Although this does not inherently imply that these models are overfit, the absence of independent test sets limits the interpretability of their results.
To address these challenges, we provide a theoretical overview of overfitting in the context of animal accelerometry and propose guidelines for optimal validation techniques. Our aim is to equip ecologists with the tools necessary to adapt general machine learning validation theory to the specific requirements of biologging, facilitating reliable overfitting detection and advancing the field.
Details
- Title
- Practical guidelines for validation of supervised machine learning models in accelerometer-based animal behaviour classification
- Authors
- Oakleigh Wilson (Corresponding Author) - University of the Sunshine Coast, Queensland, Centre for BioinnovationDavid Schoeman - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringAndrew Bradley - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringChristofer Clemente - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
- Publication details
- Journal of Animal Ecology, Vol.94(7), pp.1322-1334
- Publisher
- Wiley-Blackwell Publishing Ltd.
- Date published
- 2025
- DOI
- 10.1111/1365-2656.70054
- ISSN
- 1365-2656; 0021-8790
- PMID
- 40387610
- Copyright note
- © 2025 The Author(s). Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
- Data Availability
- Data available from the Dryad Digital Repository: https://doi.org/ 10.5061/dryad.fxpnvx14d (Wilson et al., 2025).
- Organisation Unit
- School of Science, Technology and Engineering; Centre for Bioinnovation
- Language
- English
- Record Identifier
- 991129987702621
- Output Type
- Journal article
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- Ecology
- Zoology
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