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