Splits data into a training set and a test set. Parameter ratio determines the ratio of observation going into the training set (default: 2/3).

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

  • 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 -> ResamplingHoldout

Public fields

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

ResamplingHoldout$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingHoldout$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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