mlr3 (version 0.5.0)

mlr_resamplings_cv: Cross Validation Resampling

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():

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

Parameters

• folds (integer(1)) Number of folds.

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

• ResamplingCV$new()

• ResamplingCV$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

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

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_holdout, mlr_resamplings_insample, mlr_resamplings_loo, mlr_resamplings_repeated_cv, mlr_resamplings_subsampling, mlr_resamplings

Examples

# NOT RUN {
# 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$train_set(1)
rcv$test_set(1)
intersect(rcv$train_set(1), rcv$test_set(1))

# Internal storage:
rcv$instance # table
# }