ResamplingRepeatedCV: Repeated Cross Validation Resampling
In mllg/mlr3: Machine Learning in R - Next Generation

Description Format Construction Fields Methods Parameters See Also Examples

Splits data repeats (default: 10) times using a folds-fold (default: 10) cross-validation.

The iteration counter translates to repeats blocks of folds cross-validations, i.e., the first folds iterations belong to a single cross-validation.

R6::R6Class() inheriting from Resampling.

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ResamplingRepeatedCV$new()
mlr_resamplings$get("repeated_cv")
rsmp("repeated_cv")

See Resampling.

See Resampling. Additionally, the class provides two helper function to translate iteration numbers to folds / repeats:

folds(iters)
integer() -> integer()
Translates iteration numbers to fold number.
repeats(iters)
integer() -> integer()
Translates iteration numbers to repetition number.

stratify :: logical(1) | character()
Enables stratification. See Resampling.
repeats :: integer(1)
Number of repetitions.
folds :: integer(1)
Number of folds.

Dictionary of Resamplings: mlr_resamplings

# Create a task with 10 observations
task = tsk("iris")
task$filter(1:10)

# Instantiate Resampling
rrcv = rsmp("repeated_cv", repeats = 2, folds = 3)
rrcv$instantiate(task)
rrcv$iters
rrcv$folds(1:6)
rrcv$repeats(1:6)

# Individual sets:
rrcv$train_set(1)
rrcv$test_set(1)
intersect(rrcv$train_set(1), rrcv$test_set(1))

# Internal storage:
rrcv$instance # table