Data leakage jeopardizes ecological applications of machine learning

被引：9
|
作者：
Stock, Andy [1 ]
Gregr, Edward J. [1 ,2 ]
Chan, Kai M. A. [1 ]
机构：
[1] Univ British Columbia, Inst Resources Environm & Sustainabil, Vancouver, BC, Canada
[2] SciTech Environm Consulting, Vancouver, BC, Canada
来源：
NATURE ECOLOGY & EVOLUTION | 2023年 / 7卷 / 11期
基金：
加拿大自然科学与工程研究理事会;
关键词：
VALIDATION;
D O I：
10.1038/s41559-023-02162-1
中图分类号：
Q14 [生态学（生物生态学）];
学科分类号：
071012 ; 0713 ;
摘要：
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.
引用
收藏
页码：1743 / 1745
页数：3
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