R/ResamplingRepeatedCV.R
mlr_resamplings_repeated_cv.Rd
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.
This Resampling can be instantiated via the dictionary mlr_resamplings or with the associated sugar function rsmp()
:
mlr_resamplings$get("repeated_cv") rsmp("repeated_cv")
repeats
(integer(1)
)
Number of repetitions.
folds
(integer(1)
)
Number of folds.
Bischl B, Mersmann O, Trautmann H, Weihs C (2012). “Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation.” Evolutionary Computation, 20(2), 249--275. doi: 10.1162/evco_a_00069 .
Dictionary of Resamplings: mlr_resamplings
as.data.table(mlr_resamplings)
for a complete table of all (also dynamically created) Resampling implementations.
Other Resampling:
Resampling
,
mlr_resamplings_bootstrap
,
mlr_resamplings_custom
,
mlr_resamplings_cv
,
mlr_resamplings_holdout
,
mlr_resamplings_insample
,
mlr_resamplings_loo
,
mlr_resamplings_subsampling
,
mlr_resamplings
mlr3::Resampling
-> ResamplingRepeatedCV
iters
(integer(1)
)
Returns the number of resampling iterations, depending on the values stored in the param_set
.
new()
Creates a new instance of this R6 class.
ResamplingRepeatedCV$new()
folds()
Translates iteration numbers to fold numbers.
ResamplingRepeatedCV$folds(iters)
iters
(integer()
)
Iteration number.
integer()
of fold numbers.
repeats()
Translates iteration numbers to repetition numbers.
ResamplingRepeatedCV$repeats(iters)
iters
(integer()
)
Iteration number.
integer()
of repetition numbers.
clone()
The objects of this class are cloneable with this method.
ResamplingRepeatedCV$clone(deep = FALSE)
deep
Whether to make a deep clone.
# Create a task with 10 observations task = tsk("penguins") task$filter(1:10) # Instantiate Resampling rrcv = rsmp("repeated_cv", repeats = 2, folds = 3) rrcv$instantiate(task) rrcv$iters#> [1] 6rrcv$folds(1:6)#> [1] 1 2 1 2 1 2rrcv$repeats(1:6)#> [1] 1 1 1 2 2 2# Individual sets: rrcv$train_set(1)#> [1] 1 3 4 2 9 10rrcv$test_set(1)#> [1] 5 6 7 8#> integer(0)# Internal storage: rrcv$instance # table#> row_id rep fold #> 1: 1 1 2 #> 2: 2 1 3 #> 3: 3 1 2 #> 4: 4 1 2 #> 5: 5 1 1 #> 6: 6 1 1 #> 7: 7 1 1 #> 8: 8 1 1 #> 9: 9 1 3 #> 10: 10 1 3 #> 11: 1 2 2 #> 12: 2 2 3 #> 13: 3 2 1 #> 14: 4 2 1 #> 15: 5 2 2 #> 16: 6 2 1 #> 17: 7 2 3 #> 18: 8 2 3 #> 19: 9 2 2 #> 20: 10 2 1