Cross-Validation Resampling
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$get("cv")
rsmp("cv")folds (integer(1))
Number of folds.
mlr3::Resampling -> ResamplingCV
iters(integer(1))
Returns the number of resampling iterations, depending on the values stored in the param_set.
new()
Creates a new instance of this R6 class.
ResamplingCV$new()
clone()
The objects of this class are cloneable with this method.
ResamplingCV$clone(deep = FALSE)
deepWhether to make a deep clone.
Bischl B, Mersmann O, Trautmann H, Weihs C (2012). “Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation.” Evolutionary Computation, 20(2), 249–275. doi: 10.1162/evco_a_00069.
as.data.table(mlr_resamplings) for a complete table of all (also dynamically created) Resampling implementations.
# Create a task with 10 observations
task = tsk("penguins")
task$filter(1:10)
# Instantiate Resampling
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 # tablePlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.