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

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

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

Inherited methods

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.

Examples

# Create a task with 10 observations task = tsk("iris") task$filter(1:10) # Instantiate Resampling rcv = rsmp("cv", folds = 3) rcv$instantiate(task) # Individual sets: rcv$train_set(1)
#> [1] 5 7 9 1 4 8
rcv$test_set(1)
#> [1] 2 3 6 10
intersect(rcv$train_set(1), rcv$test_set(1))
#> integer(0)
# Internal storage: rcv$instance # table
#> row_id fold #> 1: 2 1 #> 2: 3 1 #> 3: 6 1 #> 4: 10 1 #> 5: 5 2 #> 6: 7 2 #> 7: 9 2 #> 8: 1 3 #> 9: 4 3 #> 10: 8 3