mlr_resamplings_repeated_cv {mlr3} | R Documentation |
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$get("repeated_cv") rsmp("repeated_cv")
Parameters
-
repeats
(integer(1)
)
Number of repetitions. -
folds
(integer(1)
)
Number of folds.
Super class
mlr3::Resampling
-> ResamplingRepeatedCV
Active bindings
iters
(
integer(1)
)
Returns the number of resampling iterations, depending on the values stored in theparam_set
.
Methods
Public methods
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
ResamplingRepeatedCV$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$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
References
Bischl B, Mersmann O, Trautmann H, Weihs C (2012). “Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation.” Evolutionary Computation, 20(2), 249–275. doi:10.1162/evco_a_00069.
See Also
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter3/evaluation_and_benchmarking.html#sec-resampling
Package mlr3spatiotempcv for spatio-temporal resamplings.
-
as.data.table(mlr_resamplings)
for a table of available Resamplings in the running session (depending on the loaded packages). -
mlr3spatiotempcv for additional Resamplings for spatio-temporal tasks.
Other Resampling:
Resampling
,
mlr_resamplings
,
mlr_resamplings_bootstrap
,
mlr_resamplings_custom
,
mlr_resamplings_custom_cv
,
mlr_resamplings_cv
,
mlr_resamplings_holdout
,
mlr_resamplings_insample
,
mlr_resamplings_loo
,
mlr_resamplings_subsampling
Examples
# Create a task with 10 observations
task = tsk("penguins")
task$filter(1:10)
# Instantiate Resampling
repeated_cv = rsmp("repeated_cv", repeats = 2, folds = 3)
repeated_cv$instantiate(task)
repeated_cv$iters
repeated_cv$folds(1:6)
repeated_cv$repeats(1:6)
# Individual sets:
repeated_cv$train_set(1)
repeated_cv$test_set(1)
# Disjunct sets:
intersect(repeated_cv$train_set(1), repeated_cv$test_set(1))
# Internal storage:
repeated_cv$instance # table