Splits data repeats (default: 30) times into training and test set with a ratio of ratio (default: 2/3) observations going into the training set.

Dictionary

This Resampling can be instantiated via the dictionary mlr_resamplings or with the associated sugar function rsmp():

mlr_resamplings$get("holdout")
rsmp("holdout")

Parameters

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

  • ratio (numeric(1))
    Ratio of observations to put into the training set.

References

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 .

See also

Super class

mlr3::Resampling -> ResamplingSubsampling

Active bindings

iters

(integer(1))
Returns the number of resampling iterations, depending on the values stored in the param_set.

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

ResamplingSubsampling$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSubsampling$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Create a task with 10 observations task = tsk("iris") task$filter(1:10) # Instantiate Resampling rss = rsmp("subsampling", repeats = 2, ratio = 0.5) rss$instantiate(task) # Individual sets: rss$train_set(1)
#> [1] 5 4 1 2 9
rss$test_set(1)
#> [1] 3 6 7 8 10
intersect(rss$train_set(1), rss$test_set(1))
#> integer(0)
# Internal storage: rss$instance$train # list of index vectors
#> [[1]] #> [1] 5 4 1 2 9 #> #> [[2]] #> [1] 2 8 9 7 3 #>