On estimating the reliability of ecological forecasts

Perretti, Charles T.; Munch, Stephan B.

doi:10.1016/j.jtbi.2015.02.031

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…non-parametric models in the field of ecological modeling (Perretti & Munch, 2015), most studies prefer parametric ones due to the ability to interpret relationships between the predictors and the response (Aertsen et al, 2010;Jabot, 2015). However, when interpreting the coefficients of (semi-)parametric spatial models (e.g.…”

Section: Model Interpretabilitymentioning

confidence: 99%

Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data

Schratz

Muenchow

Iturritxa

et al. 2019

Ecological Modelling

332

176

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Machine-learning algorithms have gained popularity in recent years in the field of ecological modeling due to their promising results in predictive performance of classification problems. While the application of such algorithms has been highly predictors.Results show that GAM and Random Forest (RF) (mean AUROC estimates 0.708 and 0.699) outperform all other methods in predictive accuracy. The effect of hyperparameter tuning saturates at around 50 iterations for this data set. The AUROC differences between the bias-reduced (spatial cross-validation) and overoptimistic (non-spatial cross-validation) performance estimates of the GAM and RF are 0.167 (24%) and 0.213 (30%), respectively. It is recommended to also use spatial partitioning for cross-validation hyperparameter tuning of spatial data. The models developed in this study enhance the detection of Diplodia sapinea in the Basque Country compared to previous studies.

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Section: Model Interpretabilitymentioning

confidence: 99%