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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("subsampling")
rsmp("subsampling")

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

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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("penguins")
task$filter(1:10)

# Instantiate Resampling
subsampling = rsmp("subsampling", repeats = 2, ratio = 0.5)
subsampling$instantiate(task)

# Individual sets:
subsampling$train_set(1)
#> [1] 5 9 8 3 2
subsampling$test_set(1)
#> [1]  1  4  6  7 10

# Disjunct sets:
intersect(subsampling$train_set(1), subsampling$test_set(1))
#> integer(0)

# Internal storage:
subsampling$instance$train # list of index vectors
#> [[1]]
#> [1] 5 9 8 3 2
#> 
#> [[2]]
#> [1]  3  8  9  6 10
#>