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  "Title": "Resampling Algorithms for 'mlr3' Framework",
  "Version": "2026.5.19",
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  "Authors@R": "c(\nperson(\"Toby\", \"Hocking\",\nemail=\"toby.hocking@r-project.org\",\nrole=c(\"aut\", \"cre\"),\ncomment = c(ORCID=\"0000-0002-3146-0865\")),\nperson(\"Daniel\", \"Agyapong\", email = \"agyapongdaniel7777@gmail.com\", role = \"ctb\",\ncomment = c(ORCID = \"0009-0004-0857-3150\")),\nperson(\"Michel\", \"Lang\", , \"michellang@gmail.com\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0001-9754-0393\", \"Author of mlr3 when Resampling/ResamplingCV was copied/modified\")),\nperson(\"Bernd\", \"Bischl\", , \"bernd_bischl@gmx.net\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0001-6002-6980\", \"Author of mlr3 when Resampling/ResamplingCV was copied/modified\")),\nperson(\"Jakob\", \"Richter\", , \"jakob1richter@gmail.com\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0003-4481-5554\", \"Author of mlr3 when Resampling/ResamplingCV was copied/modified\")),\nperson(\"Patrick\", \"Schratz\", , \"patrick.schratz@gmail.com\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0003-0748-6624\", \"Author of mlr3 when Resampling/ResamplingCV was copied/modified\")),\nperson(\"Giuseppe\", \"Casalicchio\", , \"giuseppe.casalicchio@stat.uni-muenchen.de\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0001-5324-5966\", \"Author of mlr3 when Resampling/ResamplingCV was copied/modified\")),\nperson(\"Stefan\", \"Coors\", , \"mail@stefancoors.de\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0002-7465-2146\", \"Author of mlr3 when Resampling/ResamplingCV was copied/modified\")),\nperson(\"Quay\", \"Au\", , \"quayau@gmail.com\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0002-5252-8902\", \"Author of mlr3 when Resampling/ResamplingCV was copied/modified\")),\nperson(\"Martin\", \"Binder\", , \"mlr.developer@mb706.com\", role = \"ctb\"),\nperson(\"Florian\", \"Pfisterer\", , \"pfistererf@googlemail.com\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0001-8867-762X\", \"Author of mlr3 when Resampling/ResamplingCV was copied/modified\")),\nperson(\"Raphael\", \"Sonabend\", , \"raphaelsonabend@gmail.com\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0001-9225-4654\", \"Author of mlr3 when Resampling/ResamplingCV was copied/modified\")),\nperson(\"Lennart\", \"Schneider\", , \"lennart.sch@web.de\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0003-4152-5308\", \"Author of mlr3 when Resampling/ResamplingCV was copied/modified\")),\nperson(\"Marc\", \"Becker\", , \"marcbecker@posteo.de\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0002-8115-0400\", \"Author of mlr3 when Resampling/ResamplingCV was copied/modified\")),\nperson(\"Sebastian\", \"Fischer\", , \"sebf.fischer@gmail.com\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0002-9609-3197\", \"Author of mlr3 when Resampling/ResamplingCV was copied/modified\"))\n)",
  "Description": "A supervised learning algorithm inputs a train set, and\noutputs a prediction function, which can be used on a test set.\nIf each data point belongs to a subset (such as geographic\nregion, year, etc), then how do we know if subsets are similar\nenough so that we can get accurate predictions on one subset,\nafter training on Other subsets? And how do we know if training\non All subsets would improve prediction accuracy, relative to\ntraining on the Same subset? SOAK, Same/Other/All K-fold\ncross-validation, <doi:10.1002/sam.70055> can be used to answer\nthese questions, by fixing a test subset, training models on\nSame/Other/All subsets, and then comparing test error rates\n(Same versus Other and Same versus All). Also provides code for\nestimating how many train samples are required to get accurate\npredictions on a test set.",
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  "URL": "https://github.com/tdhock/mlr3resampling",
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  "Date/Publication": "2026-05-22 20:08:49 UTC",
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