While significant progress has been made towards explaining black-box
machine-learning (ML) models, there is still a distinct lack of diagnostic
tools that elucidate the spatial behaviour of ML models in terms of predictive
skill and variable importance. This contribution proposes spatial prediction
error profiles (SPEPs) and spatial variable importance profiles (SVIPs) as
novel model-agnostic assessment and interpretation tools for spatial prediction
models with a focus on prediction distance. Their suitability is demonstrated
in two case studies representing a regionalization task in an
environmental-science context, and a classification task from remotely-sensed
land cover classification. In these case studies, the SPEPs and SVIPs of
geostatistical methods, linear models, random forest, and hybrid algorithms
show striking differences but also relevant similarities. Limitations of
related cross-validation techniques are outlined, and the case is made that
modelers should focus their model assessment and interpretation on the intended
spatial prediction horizon. The range of autocorrelation, in contrast, is not a
suitable criterion for defining spatial cross-validation test sets. The novel
diagnostic tools enrich the toolkit of spatial data science, and may improve ML
model interpretation, selection, and design.

该研究提出了空间预测误差剖面（SPEPs）和空间变量重要性剖面（SVIPs）作为空间预测模型的新型模型无关的评估和解释工具，以预测距离为重点。这些诊断工具为空间数据科学提供了新的工具，可能会改善机器学习模型的解释、选择和设计。