Articles | Volume 17, issue 15
https://doi.org/10.5194/gmd-17-5897-2024
https://doi.org/10.5194/gmd-17-5897-2024
Methods for assessment of models
 | 
07 Aug 2024
Methods for assessment of models |  | 07 Aug 2024

kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation

Jan Linnenbrink, Carles Milà, Marvin Ludwig, and Hanna Meyer

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Cited articles

Beygelzimer, A., Kakadet, S., Langford, J., Arya, S., Mount, D., and Li, S.: FNN: Fast Nearest Neighbor Search Algorithms and Applications, r package version 1.1.3.1, https://CRAN.R-project.org/package=FNN (last access: 29 July 2024), 2022. a
Brenning, A.: Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest, in: 2012 IEEE Int. Geosci. Remote, 5372–5375, https://doi.org/10.1109/IGARSS.2012.6352393, 2012. a, b
Brenning, A.: Spatial machine-learning model diagnostics: a model-agnostic distance-based approach, Int. J. Geograph. Inf. Sci., 37, 584–606, https://doi.org/10.1080/13658816.2022.2131789, 2022. a, b
Conover, W. J.: Practical nonparametric statistics, vol. 350, John wiley & sons, ISBN 978-0-471-16068-7, 1999. a
Corporation, M. and Weston, S.: doParallel: Foreach Parallel Adaptor for the “parallel” Package, r package version 1.0.17, https://CRAN.R-project.org/package=doParallel (last access: 29 July 2024), 2022. a
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Short summary
Estimation of map accuracy based on cross-validation (CV) in spatial modelling is pervasive but controversial. Here, we build upon our previous work and propose a novel, prediction-oriented k-fold CV strategy for map accuracy estimation in which the distribution of geographical distances between prediction and training points is taken into account when constructing the CV folds. Our method produces more reliable estimates than other CV methods and can be used for large datasets.
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