mlr3spatiotempcv-package

mlr3spatiotempcv: Spatiotemporal Resampling Methods for 'mlr3'

Extends the mlr3 machine learning 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. A JSS article is available at tools:::Rd_expr_doi("10.18637/jss.v111.i07") . package

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: list("mlr3data")
• Connector to OpenML: list("mlr3oml")
• More classification and regression learners: list("mlr3learners")
• Even more learners: https://github.com/mlr-org/mlr3extralearners
• Preprocessing and machine learning pipelines: list("mlr3pipelines")
• Tuning of hyperparameters: list("mlr3tuning")
• Visualizations for many mlr3 objects: list("mlr3viz")
• Survival analysis and probabilistic regression: list("mlr3proba")
• Cluster analysis: list("mlr3cluster")
• Feature selection filters: list("mlr3filters")
• Feature selection wrappers: list("mlr3fselect")
• Interface to real (out-of-memory) data bases: list("mlr3db")
• Performance measures as plain functions: list("mlr3measures")
• Parallelization framework: list("future")
• Progress bars: list("progressr")

References

Schratz P, Muenchow J, Iturritxa E, Richter J, Brenning A (2019). Hyperparameter tuning and performance assessment of statistical andmachine-learning algorithms using spatial data.

Ecological Modelling, 406 , 109--120. tools:::Rd_expr_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 environmentallyseparated folds for k-fold cross-validation of species distributionmodels.

bioRxiv. tools:::Rd_expr_doi("10.1101/357798") .

Meyer H, Reudenbach C, Hengl T, Katurji M, Nauss T (2018). Improving performance of spatio-temporal machine learning models usingforward feature selection and target-oriented validation.

Environmental Modelling & Software, 101 , 1--9. tools:::Rd_expr_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. tools:::Rd_expr_doi("https://doi.org/10.1145/584792.584877") .

See Also

Useful links:

• https://mlr3spatiotempcv.mlr-org.com/
• https://github.com/mlr-org/mlr3spatiotempcv
• https://mlr3book.mlr-org.com
• Report bugs at https://github.com/mlr-org/mlr3spatiotempcv/issues

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]