| mlr3spatiotempcv-package {mlr3spatiotempcv} | R Documentation |
mlr3spatiotempcv: Spatiotemporal Resampling Methods for 'mlr3'
Description
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
Book on mlr3: https://mlr3book.mlr-org.com
mlr3book section about spatiotemporal data: https://mlr3book.mlr-org.com/chapters/chapter13/beyond_regression_and_classification.html#spatiotemp-cv
package vignettes: https://mlr3spatiotempcv.mlr-org.com/dev/articles/
Miscellaneous mlr3 content
Use cases and examples: https://mlr3gallery.mlr-org.com
More classification and regression tasks: mlr3data
More classification and regression learners: mlr3learners
Even more learners: https://github.com/mlr-org/mlr3extralearners
Preprocessing and machine learning pipelines: mlr3pipelines
Tuning of hyperparameters: mlr3tuning
Visualizations for many mlr3 objects: mlr3viz
Survival analysis and probabilistic regression: mlr3proba
Cluster analysis: mlr3cluster
Feature selection filters: mlr3filters
Feature selection wrappers: mlr3fselect
Interface to real (out-of-memory) data bases: mlr3db
Performance measures as plain functions: mlr3measures
Parallelization framework: future
Progress bars: progressr
Author(s)
Maintainer: Patrick Schratz patrick.schratz@gmail.com (ORCID)
Authors:
Marc Becker marcbecker@posteo.de (ORCID)
Other contributors:
Jannes Muenchow jannes.muenchow@uni-jena.de (ORCID) [contributor]
Michel Lang michellang@gmail.com (ORCID) [contributor]
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. doi:10.1145/584792.584877.
See Also
Useful links:
Report bugs at https://github.com/mlr-org/mlr3spatiotempcv/issues