Repeated Cross-Validation Resampling
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$get("repeated_cv") rsmp("repeated_cv")
repeats
(integer(1)
)
Number of repetitions.
folds
(integer(1)
)
Number of folds.
mlr3::Resampling
-> ResamplingRepeatedCV
iters
(integer(1)
)
Returns the number of resampling iterations, depending on the values stored in the param_set
.
new()
Creates a new instance of this R6 class.
ResamplingRepeatedCV$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$clone(deep = FALSE)
deep
Whether to make a deep clone.
Bischl B, Mersmann O, Trautmann H, Weihs C (2012). “Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation.” Evolutionary Computation, 20(2), 249–275. doi: 10.1162/evco_a_00069.
as.data.table(mlr_resamplings)
for a complete table of all (also dynamically created) Resampling implementations.
# Create a task with 10 observations task = tsk("penguins") task$filter(1:10) # Instantiate Resampling rrcv = rsmp("repeated_cv", repeats = 2, folds = 3) rrcv$instantiate(task) rrcv$iters 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
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