Repeated Cross-Validation Resampling

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.

Dictionary

This Resampling can be instantiated via the dictionary mlr_resamplings or with the associated sugar function rsmp():

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

Parameters

• repeats (integer(1))
  Number of repetitions.

• folds (integer(1))
  Number of folds.

References

Bischl B, Mersmann O, Trautmann H, Weihs C (2012). “Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation.” Evolutionary Computation, 20(2), 249–275. doi:10.1162/evco_a_00069 .

See also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter3/evaluation_and_benchmarking.html#sec-resampling

• Package mlr3spatiotempcv for spatio-temporal resamplings.

• Dictionary of Resamplings: mlr_resamplings

• as.data.table(mlr_resamplings) for a table of available Resamplings in the running session (depending on the loaded packages).

• mlr3spatiotempcv for additional Resamplings for spatio-temporal tasks.

Other Resampling: Resampling, mlr_resamplings, mlr_resamplings_bootstrap, mlr_resamplings_custom, mlr_resamplings_custom_cv, mlr_resamplings_cv, mlr_resamplings_holdout, mlr_resamplings_insample, mlr_resamplings_loo, mlr_resamplings_subsampling

Super class

mlr3::Resampling -> ResamplingRepeatedCV

Active bindings

iters

(integer(1))
Returns the number of resampling iterations, depending on the values stored in the param_set.

Methods

Public methods

• ResamplingRepeatedCV$new()

• ResamplingRepeatedCV$folds()

• ResamplingRepeatedCV$repeats()

• ResamplingRepeatedCV$clone()

Inherited methods

• mlr3::Resampling$format()
• mlr3::Resampling$help()
• mlr3::Resampling$instantiate()
• mlr3::Resampling$print()
• mlr3::Resampling$test_set()
• mlr3::Resampling$train_set()

---

Method new()

Creates a new instance of this R6 class.

Usage

ResamplingRepeatedCV$new()

---

Method folds()

Translates iteration numbers to fold numbers.

Usage

ResamplingRepeatedCV$folds(iters)

Arguments

iters

(integer())
Iteration number.

Returns

integer() of fold numbers.

---

Method repeats()

Translates iteration numbers to repetition numbers.

Usage

ResamplingRepeatedCV$repeats(iters)

Arguments

iters

(integer())
Iteration number.

Returns

integer() of repetition numbers.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingRepeatedCV$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

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

# Individual sets:
repeated_cv$train_set(1)
#> [1]  2  6  9  7  8 10
repeated_cv$test_set(1)
#> [1] 1 3 4 5

# Disjunct sets:
intersect(repeated_cv$train_set(1), repeated_cv$test_set(1))
#> integer(0)

# Internal storage:
repeated_cv$instance # table
#>     row_id   rep  fold
#>      <int> <int> <int>
#>  1:      1     1     1
#>  2:      2     1     2
#>  3:      3     1     1
#>  4:      4     1     1
#>  5:      5     1     1
#>  6:      6     1     2
#>  7:      7     1     3
#>  8:      8     1     3
#>  9:      9     1     2
#> 10:     10     1     3
#> 11:      1     2     2
#> 12:      2     2     3
#> 13:      3     2     1
#> 14:      4     2     3
#> 15:      5     2     3
#> 16:      6     2     1
#> 17:      7     2     2
#> 18:      8     2     1
#> 19:      9     2     2
#> 20:     10     2     1
#>     row_id   rep  fold