mlr3spatiotempcv: Spatiotemporal Resampling Methods for Machine Learning in R
Spatial and spatiotemporal machine-learning models require a suitable framework for their model assessment, model selection, and hyperparameter tuning, in order to avoid error estimation bias and over-fitting. This contribution provides an overview of the state-of-the-art in spatial and spatiotempo...
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| Format: | Article |
| Language: | English |
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Foundation for Open Access Statistics
2024-11-01
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| Series: | Journal of Statistical Software |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/4778 |
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| author | Patrick Schratz Marc Becker Michel Lang Alexander Brenning |
| author_facet | Patrick Schratz Marc Becker Michel Lang Alexander Brenning |
| author_sort | Patrick Schratz |
| collection | DOAJ |
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Spatial and spatiotemporal machine-learning models require a suitable framework for their model assessment, model selection, and hyperparameter tuning, in order to avoid error estimation bias and over-fitting. This contribution provides an overview of the state-of-the-art in spatial and spatiotemporal cross-validation techniques and their implementations in R while introducing the R package mlr3spatiotempcv as an extension package of the machine-learning framework mlr3. Currently various R packages implementing different spatiotemporal partitioning strategies exist: blockCV, CAST, skmeans and sperrorest. The goal of mlr3spatiotempcv is to gather the available spatiotemporal resampling methods in R and make them available to users through a simple and common interface. This is made possible by integrating the package directly into the mlr3 machine-learning framework, which already has support for generic non-spatiotemporal resampling methods such as random partitioning. One advantage is the use of a consistent nomenclature in an overarching machine-learning toolkit instead of a varying package-specific syntax, making it easier for users to choose from a variety of spatiotemporal resampling methods. This package avoids giving recommendations which method to use in practice as this decision depends on the predictive task at hand, the autocorrelation within the data, and the spatial structure of the sampling design or geographic objects being studied.
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| format | Article |
| id | doaj-art-8ff553d4a8824ab09a09e603eef41fad |
| institution | DOAJ |
| issn | 1548-7660 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj-art-8ff553d4a8824ab09a09e603eef41fad2025-08-20T02:57:30ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602024-11-01111110.18637/jss.v111.i07mlr3spatiotempcv: Spatiotemporal Resampling Methods for Machine Learning in RPatrick Schratz0Marc Becker1Michel Lang2Alexander Brenning3Friedrich-Schiller-University JenaLudwig-Maximilians-Universität MünchenTU Dortmund UniversityFriedrich Schiller University Jena Spatial and spatiotemporal machine-learning models require a suitable framework for their model assessment, model selection, and hyperparameter tuning, in order to avoid error estimation bias and over-fitting. This contribution provides an overview of the state-of-the-art in spatial and spatiotemporal cross-validation techniques and their implementations in R while introducing the R package mlr3spatiotempcv as an extension package of the machine-learning framework mlr3. Currently various R packages implementing different spatiotemporal partitioning strategies exist: blockCV, CAST, skmeans and sperrorest. The goal of mlr3spatiotempcv is to gather the available spatiotemporal resampling methods in R and make them available to users through a simple and common interface. This is made possible by integrating the package directly into the mlr3 machine-learning framework, which already has support for generic non-spatiotemporal resampling methods such as random partitioning. One advantage is the use of a consistent nomenclature in an overarching machine-learning toolkit instead of a varying package-specific syntax, making it easier for users to choose from a variety of spatiotemporal resampling methods. This package avoids giving recommendations which method to use in practice as this decision depends on the predictive task at hand, the autocorrelation within the data, and the spatial structure of the sampling design or geographic objects being studied. https://www.jstatsoft.org/index.php/jss/article/view/4778 |
| spellingShingle | Patrick Schratz Marc Becker Michel Lang Alexander Brenning mlr3spatiotempcv: Spatiotemporal Resampling Methods for Machine Learning in R Journal of Statistical Software |
| title | mlr3spatiotempcv: Spatiotemporal Resampling Methods for Machine Learning in R |
| title_full | mlr3spatiotempcv: Spatiotemporal Resampling Methods for Machine Learning in R |
| title_fullStr | mlr3spatiotempcv: Spatiotemporal Resampling Methods for Machine Learning in R |
| title_full_unstemmed | mlr3spatiotempcv: Spatiotemporal Resampling Methods for Machine Learning in R |
| title_short | mlr3spatiotempcv: Spatiotemporal Resampling Methods for Machine Learning in R |
| title_sort | mlr3spatiotempcv spatiotemporal resampling methods for machine learning in r |
| url | https://www.jstatsoft.org/index.php/jss/article/view/4778 |
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