Supporting functionality to run ‘caret’ with spatial or spatial-temporal data. ‘caret’ is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial or spatial-temporal modelling tasks using ‘caret’. To decrease spatial overfitting and to improve model performances, the package implements a forward feature selection that selects suitable predictor variables in view to their contribution to spatial or spatio-temporal model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models.
The talk from the OpenGeoHub summer school 2019 on spatial validation and variable selection: https://www.youtube.com/watch?v=mkHlmYEzsVQ.
Tutorial (https://youtu.be/EyP04zLe9qo) and Lecture (https://youtu.be/OoNH6Nl-X2s) recording from OpenGeoHub summer school 2020 on the area of applicability. As well as talk at the OpenGeoHub summer school 2021: https://av.tib.eu/media/54879
Talk and tutorial from the OpenGeoHub 2022 summer school on Machine learning-based maps of the environment - challenges of extrapolation and overfitting, including discussions on the area of applicability and the nearest neighbor distance matching cross-validation (https://doi.org/10.5446/59412).
Milà, C., Mateu, J., Pebesma, E., Meyer, H. (2022): Nearest Neighbour Distance Matching Leave-One-Out Cross-Validation for map validation. Methods in Ecology and Evolution 00, 1– 13. https://doi.org/10.1111/2041-210X.13851
Linnenbrink, J., Milà, C., Ludwig, M., and Meyer, H.: kNNDM (2023): k-fold Nearest Neighbour Distance Matching Cross-Validation for map accuracy estimation. EGUsphere [preprint]. https://doi.org/10.5194/egusphere-2023-1308
Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., Nauss, T. (2018): Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling & Software, 101, 1-9. https://doi.org/10.1016/j.envsoft.2017.12.001
Meyer, H., Reudenbach, C., Wöllauer, S., Nauss, T. (2019): Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction. Ecological Modelling. 411. https://doi.org/10.1016/j.ecolmodel.2019.108815
- Meyer, H., Pebesma, E. (2021). Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution, 12, 1620– 1633. https://doi.org/10.1111/2041-210X.13650
Meyer, H., Pebesma, E. (2022): Machine learning-based global maps of ecological variables and the challenge of assessing them. Nature Communications, 13. https://www.nature.com/articles/s41467-022-29838-9
Ludwig, M., Moreno-Martinez, A., Hoelzel, N., Pebesma, E., Meyer, H. (2023): Assessing and improving the transferability of current global spatial prediction models. Global Ecology and Biogeography. https://doi.org/10.1111/geb.13635.