mlr_resamplings_cv function

Cross-Validation Resampling

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

Dictionary

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

mlr_resamplings$get("cv")
rsmp("cv")

Parameters

• folds (integer(1))

  Number of folds.

Examples

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

# Instantiate Resampling
cv = rsmp("cv", folds = 3)
cv$instantiate(task)

# Individual sets:
cv$train_set(1)
cv$test_set(1)

# Disjunct sets:
intersect(cv$train_set(1), cv$test_set(1))

# Internal storage:
cv$instance # table

References

Bischl B, Mersmann O, Trautmann H, Weihs C (2012). Resampling Methods for Meta-Model Validation with Recommendations forEvolutionary Computation.

Evolutionary Computation, 20 (2), 249--275. tools:::Rd_expr_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 list("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).
• list("mlr3spatiotempcv") for additional Resampling s for spatio-temporal tasks.

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

Super class

mlr3::Resampling -> ResamplingCV

Active bindings

• iters: (integer(1))

   Returns the number of resampling iterations, depending on the values stored in the `param_set`.
  

Methods

Public methods

• list("ResamplingCV$new()")

• list("ResamplingCV$clone()")

Method new()

Creates a new instance of this R6 class.

Usage

ResamplingCV$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingCV$clone(deep = FALSE)

Arguments

• deep: Whether to make a deep clone.