  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 simplified in the last years due to their
well-documented integration in commonly used statistical programming languages
such as R, there are several practical challenges in the field of ecological
modeling related to unbiased performance estimation, optimization of algorithms
using hyperparameter tuning and spatial autocorrelation. We address these
issues in the comparison of several widely used machine-learning algorithms
such as Boosted Regression Trees (BRT), k-Nearest Neighbor (WKNN), Random
Forest (RF) and Support Vector Machine (SVM) to traditional parametric
algorithms such as logistic regression (GLM) and semi-parametric ones like
generalized additive models (GAM). Different nested cross-validation methods
including hyperparameter tuning methods are used to evaluate model performances
with the aim to receive bias-reduced performance estimates. As a case study the
spatial distribution of forest disease Diplodia sapinea in the Basque Country
in Spain is investigated using common environmental variables such as
temperature, precipitation, soil or lithology as predictors. Results show that
GAM and 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.
