mlr_resamplings_cv: Cross-Validation Resampling

Description Dictionary Parameters Super class Active bindings Methods References See Also Examples

Description

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():

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

Parameters

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.

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

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

Examples

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# 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

mlr3 documentation built on Aug. 6, 2021, 1:07 a.m.