Extends the mlr3 ML framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored.

Main resources

Miscellaneous mlr3 content

References

Schratz P, Muenchow J, Iturritxa E, Richter J, Brenning A (2019). “Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data.” Ecological Modelling, 406, 109--120. doi: 10.1016/j.ecolmodel.2019.06.002 .

Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G (2018). “blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models.” bioRxiv. doi: 10.1101/357798 .

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. doi: 10.1016/j.envsoft.2017.12.001 .

Zhao Y, Karypis G (2002). “Evaluation of Hierarchical Clustering Algorithms for Document Datasets.” 11th Conference of Information and Knowledge Management (CIKM), 51-524. http://glaros.dtc.umn.edu/gkhome/node/167.

See also

Author

Maintainer: Patrick Schratz patrick.schratz@gmail.com (ORCID)

Authors:

Other contributors: