[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118343-en":3,"doc-seo-118343-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118343,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","mlr3spatiotempcv - Spatiotemporal Resampling Methods for Machine Learning in R","Spatial and spatiotemporal machine-learning models need reliable assessment, selection, and hyperparameter tuning frameworks to prevent error estimation bias and overfitting. The work surveys state-of-the-art spatial and spatiotemporal cross-validation approaches and provides R implementations while introducing the R package mlr3spatiotempcv as an extension of mlr3. By integrating these methods into a common interface, the package unifies nomenclature across resampling strategies (e.g., blockCV, CAST, skmeans, sperrorest), helping users compare options without shifting package-specific syntax. It avoids prescriptive recommendations, since method choice depends on prediction tasks, autocorrelation, and spatial sampling design structure.","Journal of Statistical Software  \n November 2024, Volume 111, Issue 7 . doi: 10.18637/jss.v111.i07  \nmlr3spatiotempcv: Spatiotemporal Resampling Methods for Machine Learning in R  \nPatrick Schratz   \nFriedrich Schiller University Jena  \nMarc Becker   \nLudwig-Maximilians-Universität München  \nMichel Lang   \nTU Dortmund University  \nAlexander Brenning   \nFriedrich Schiller University Jena  \nAbstract  \nSpatial 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 machinelearning 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.  \nKeywords: cross-validation, predictive performance, machine learning, autocorrelation, spatial, temporal, R.  \n1 . Introduction  \nSpatial and spatiotemporal prediction tasks are common in applications ranging from en-  \n2 mlr3spatiotempcv: Spatiotemporal Resampling Methods for Machine Learning in R  \nvironmental sciences to archaeology and epidemiology. While sophisticated mathematical frameworks have long been developed in spatial statistics to characterize predictive uncertainties under well-defined mathematical assumptions such as intrinsic stationarity (e.g. , Cressie 1993), computational estimation procedures have only been proposed more recently to assess predictive performances of spatial and spatiotemporal prediction models (Brenning 2005 , 2012 ; Pohjankukka, Pahikkala, Nevalainen, and Heikkonen 2017 ; Roberts et al. 2017) .  \nAlthough alternatives such as the bootstrap exist since some decades (Efron and Gong 1983 ; Hand 1997), cross-validation (CV) is a particularly well-established, easy-to-implement algorithm for model assessment of supervised machine-learning models (Efron and Gong 1983 , and next section) and model selection (Arlot and Celisse 2010) . In its basic form, CV is based on resampling the data without paying attention to any possible dependence structure, which may arise from, e.g., grouped or structured data, or underlying environmental processes inducing some sort of spatial coherence at the landscape scale. In treating dependent observations as independent, or ignoring autocorrelation, CV test samples may in fact be heavily correlated with, or even pseudo-replicates of, the data used for training the model, which introduces a potentially severe bias in assessing the transferability of flexible machine-learning (ML) models.  \nThis CV bias is well-known in spatial as well as non-spatial prediction (Brenning 2005 ; Brenning and Lausen 2008 ; Arlot and Celisse 2010 ; Roberts et al. 2017) and in forecasting (Bergmeir, Hyndman, and Koo 2018) . It is most easily understood from a predictive modeling perspective by focusing on the question of where (and when) the mo","cbCaiqA8Kfc0Hvai","https://ap.wps.com/l/cbCaiqA8Kfc0Hvai","pdf",3305895,1,36,"English","en",105,"# Introduction\n## Motivation: dependence, spatial structure, and CV bias\n## Prediction task alignment with resampling schemes","[{\"question\":\"Why can standard cross-validation produce biased estimates for spatial and spatiotemporal models?\",\"answer\":\"Because resampling without accounting for dependence can make test samples highly correlated with training data, creating pseudo-replicates. This yields over-optimistic performance estimates and can distort transferability of flexible models.\"},{\"question\":\"What problem does the mlr3spatiotempcv package address?\",\"answer\":\"It collects multiple spatial and spatiotemporal resampling methods available in R and exposes them via a simple, consistent interface integrated into the mlr3 framework.\"},{\"question\":\"What factors determine which spatiotemporal resampling method to use?\",\"answer\":\"The package does not prescribe a single method; selection depends on the predictive task, the autocorrelation structure of the data, and the spatial structure implied by the sampling design or studied geographic objects.\"}]","mlr3spatiotempcv - Spatiotemporal Resampling Methods for Machine Learning in R | PDF",1785683187,91,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"mlr3spatiotempcv-spatiotemporal-resampling-methods-for-machine-learning-in-r","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/mlr3spatiotempcv-spatiotemporal-resampling-methods-for-machine-learning-in-r/118343/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why can standard cross-validation produce biased estimates for spatial and spatiotemporal models?","Question",{"text":75,"@type":76},"Because resampling without accounting for dependence can make test samples highly correlated with training data, creating pseudo-replicates. This yields over-optimistic performance estimates and can distort transferability of flexible models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does the mlr3spatiotempcv package address?",{"text":80,"@type":76},"It collects multiple spatial and spatiotemporal resampling methods available in R and exposes them via a simple, consistent interface integrated into the mlr3 framework.",{"name":82,"@type":73,"acceptedAnswer":83},"What factors determine which spatiotemporal resampling method to use?",{"text":84,"@type":76},"The package does not prescribe a single method; selection depends on the predictive task, the autocorrelation structure of the data, and the spatial structure implied by the sampling design or studied geographic objects.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]