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mlr3resampling  

Resampling Algorithms for 'mlr3' Framework
View on CRAN: Click here


Download and install mlr3resampling package within the R console
Install from CRAN:
install.packages("mlr3resampling")

Install from Github:
library("remotes")
install_github("cran/mlr3resampling")

Install by package version:
library("remotes")
install_version("mlr3resampling", "2025.11.19")



Attach the package and use:
library("mlr3resampling")
Maintained by
Toby Hocking
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-12-21
Latest Update: 2025-06-23
Description:
A supervised learning algorithm inputs a train set, and outputs a prediction function, which can be used on a test set. If each data point belongs to a group (such as geographic region, year, etc), then how do we know if it is possible to train on one group, and predict accurately on another group? Cross-validation can be used to determine the extent to which this is possible, by first assigning fold IDs from 1 to K to all data (possibly using stratification, usually by group and label). Then we loop over test sets (group/fold combinations), train sets (same group, other groups, all groups), and compute test/prediction accuracy for each combination. Comparing test/prediction accuracy between same and other, we can determine the extent to which it is possible (perfect if same/other have similar test accuracy for each group; other is usually somewhat less accurate than same; other can be just as bad as featureless baseline when the groups have different patterns). For more information, describes the method in depth. How many train samples are required to get accurate predictions on a test set? Cross-validation can be used to answer this question, with variable size train sets.
How to cite:
Toby Hocking (2023). mlr3resampling: Resampling Algorithms for 'mlr3' Framework. R package version 2025.11.19, https://cran.r-project.org/web/packages/mlr3resampling. Accessed 12 Aug. 2026.
Previous versions and publish date:
(2026-07-09 06:31), 2023.12.20 (2023-12-21 17:40), 2024.1.8 (2024-01-09 05:30), 2024.1.23 (2024-02-01 06:20), 2024.4.14 (2024-04-16 17:50), 2024.7.3 (2024-07-06 00:10), 2024.7.7 (2024-07-12 17:50), 2024.9.6 (2024-09-11 22:40), 2025.3.30 (2025-04-09 09:30), 2025.6.23 (2025-06-23 12:20), 2025.11.19 (2025-11-20 20:10), 2026.2.24 (2026-02-26 17:50)
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Complete documentation for mlr3resampling
Functions, R codes and Examples using the mlr3resampling R package
Some associated functions: ResamplingSameOtherCV . ResamplingVariableSizeTrainCV . score . 
Some associated R codes: ResamplingBase.R . ResamplingSameOtherCV.R . ResamplingVariableSizeTrainCV.R . score.R .  Full mlr3resampling package functions and examples
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