mlr3spatiotempcv-package: mlr3spatiotempcv: Spatiotemporal Resampling Methods for...

In mlr-org/mlr3spatiotempcv: Spatiotemporal Resampling Methods for 'mlr3'

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

• Connector to OpenML: mlr3oml

• 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 [e-mail] (ORCID)

Authors:

• Marc Becker [e-mail] (ORCID)

Other contributors:

• Jannes Muenchow [e-mail] (ORCID) [contributor]

• Michel Lang [e-mail] (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. \Sexpr[results=rd]{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 environmentally separated folds for k-fold cross-validation of species distribution models.” bioRxiv. \Sexpr[results=rd]{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 using forward feature selection and target-oriented validation.” Environmental Modelling & Software, 101, 1–9. \Sexpr[results=rd]{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. \Sexpr[results=rd]{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

mlr-org/mlr3spatiotempcv documentation built on April 23, 2024, 6:50 a.m.