mlr3 (version 0.1.6)

mlr_resamplings_repeated_cv: 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.

Format

R6::R6Class() inheriting from Resampling.

Construction

ResamplingRepeatedCV$new()
mlr_resamplings$get("repeated_cv")
rsmp("repeated_cv")

Fields

See Resampling.

Methods

See Resampling. Additionally, the class provides two helper function to translate iteration numbers to folds / repeats:

• folds(iters) integer() -> integer() Translates iteration numbers to fold number.

• repeats(iters) integer() -> integer() Translates iteration numbers to repetition number.

Parameters

• repeats :: integer(1) Number of repetitions.

• folds :: integer(1) Number of folds.

See Also

Dictionary of Resamplings: mlr_resamplings

as.data.table(mlr_resamplings) for a complete table of all (also dynamically created) Resampling implementations.

Examples

# NOT RUN {
# Create a task with 10 observations
task = tsk("iris")
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
# }