mlr3 (version 0.5.0)

mlr_resamplings_repeated_cv: Repeated Cross Validation Resampling

Description

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("holdout")
rsmp("holdout")

Parameters

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

• folds (integer(1)) Number of folds.

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()

• 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.

References

mlr3bischl_2012

See Also

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_loo, mlr_resamplings_subsampling, mlr_resamplings

Examples

# 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
# }