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.
Iteration numbers can be translated into folds or repeats with provided methods.
This Resampling can be instantiated via the dictionary mlr_resamplings or with the associated sugar function rsmp():
mlr_resamplings$get("holdout")
rsmp("holdout")
repeats (integer(1))
Number of repetitions.
folds (integer(1))
Number of folds.
mlr3::Resampling -> ResamplingRepeatedCV
iters(integer(1))
Returns the number of resampling iterations, depending on the values stored in the param_set.
new()Creates a new instance of this R6 class.
ResamplingRepeatedCV$new()
folds()Translates iteration numbers to fold numbers.
ResamplingRepeatedCV$folds(iters)
iters(integer())
Iteration number.
integer() of fold numbers.
repeats()Translates iteration numbers to repetition numbers.
ResamplingRepeatedCV$repeats(iters)
iters(integer())
Iteration number.
integer() of repetition numbers.
clone()The objects of this class are cloneable with this method.
ResamplingRepeatedCV$clone(deep = FALSE)
deepWhether to make a deep clone.
mlr3bischl_2012
Dictionary of Resamplings: mlr_resamplings
as.data.table(mlr_resamplings) for a complete table of all (also dynamically created) Resampling implementations.
Other Resampling:
Resampling,
mlr_resamplings_bootstrap,
mlr_resamplings_custom,
mlr_resamplings_cv,
mlr_resamplings_holdout,
mlr_resamplings_insample,
mlr_resamplings_subsampling,
mlr_resamplings
# NOT RUN {
# Create a task with 10 observations
task = tsk("iris")
task$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
# }
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