Evaluating the predictive performance of presence-absence models: Why can the same model appear excellent or poor?

ABSTRACT: When comparing multiple models of species distribution, models yielding higher predictive performance are clearly to be favored. A more difficult question is how to decide whether even the best model is "good enough". Here, we clarify key choices and metrics related to evaluating the predictive performance of presence-absence models. We use a hierarchical case study to evaluate how four metrics of predictive performance (AUC, Tjur's R ², max-Kappa, and max-TSS) relate to each other, the random and fixed effects parts of the model, the spatial scale at which predictive performance is measured, and the cross-validation strategy chosen. We demonstrate that the very same metric can achieve different values for the very same model, even when similar cross-validation strategies

SUBMITTER: Abrego N 

PROVIDER: S-EPMC10726276 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature