基于灵长类动物进化结构的机器学习模型威胁状态评估验证

Machine learning models have been employed increasingly as tools to inform conservation assessment when ecological data is insufficient; however, conventional random validation can lead to an overestimate of predictive transferability when closely related species are included in training and testing datasets. In this paper, the effect of the validation framework on predicting the current threat status of primates using a machine-learning approach is investigated. The data set consists of 423 primate species whose status was determined by the IUCN Red List and were categorized as either being t