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• W3210843273 abstract "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 reviews the state-of-the-art in spatial and spatiotemporal CV, and introduces the proglang{R} package mlr3spatiotempcv as an extension package of the machine-learning framework textbf{mlr3}. Currently various proglang{R} packages implementing different spatiotemporal partitioning strategies exist: pkg{blockCV}, pkg{CAST}, pkg{kmeans} and pkg{sperrorest}. The goal of pkg{mlr3spatiotempcv} is to gather the available spatiotemporal resampling methods in proglang{R} and make them available to users through a simple and common interface. This is made possible by integrating the package directly into the pkg{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." @default.
• W3210843273 created "2021-11-08" @default.
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• W3210843273 date "2021-10-25" @default.
• W3210843273 modified "2023-09-27" @default.
• W3210843273 title "Mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in R." @default.
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