Data leakage jeopardizes ecological applications of machine learning

Reference Type:

Journal Article

Stock, Andy, Edward J. Gregr, and Kai M. A. Chan. 2023. “Data Leakage Jeopardizes Ecological Applications of Machine Learning.” Nature Ecology & Evolution 7 (11): 1743–45. https://doi.org/10.1038/s41559-023-02162-1

Machine learning is a popular tool in ecology but many scientific applications suffer from data leakage, causing misleading results. We highlight common pitfalls in ecological machine-learning methods and argue that discipline-specific model info sheets must be developed to aid in model evaluations.