Mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in R

Spatial and spatiotemporal prediction tasks are common in applications ranging from environmental sciences to archaeology and epidemiology. While sophisticated mathematical frameworks have long been developed in spatial statistics to characterize predictive uncertainties under well-defined mathematical assumptions such as intrinsic stationarity (e.g., Cressie 1993), computational estimation procedures have only been proposed more recently to assess predictive performances of spatial and spatiotemporal prediction models (Brenning 2005, 2012; Pohjankukka, Pahikkala, Nevalainen, and Heikkonen 2017; Roberts, Bahn, Ciuti, Boyce, Elith, Guillera-Arroita, Hauenstein, Lahoz-Monfort, Schröder, Thuiller, Warton, Wintle, Hartig, and Dormann 2017). Although alternatives such as the bootstrap exist since some decades (Efron and Gong 1983; Hand 1997), cross-validation (CV) is a particularly well-established, easy-to-implement algorithm for model assessment of supervised machine-learning models (Efron and Gong 1983, and next section) and model selection (Arlot and Celisse 2010). In its basic form, CV is based on resampling the data without paying attention to any possible dependence structure, which may arise from, e.g., grouped or structured data, or underlying environmental processes inducing some sort of spatial coherence at the landscape scale. In treating dependent observations as independent, or ignoring autocorrelation, CV test samples may in fact be heavily correlated with, or even pseudo-replicates of, the data used for training the model, which introduces a potentially severe bias in assessing the transferability of flexible machine-learning (ML) models.