Holdout Resampling

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

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter3/evaluation_and_benchmarking.html#sec-resampling

• Package mlr3spatiotempcv for spatio-temporal resamplings.

• Dictionary of Resamplings: mlr_resamplings

• as.data.table(mlr_resamplings) for a table of available Resamplings in the running session (depending on the loaded packages).

• mlr3spatiotempcv for additional Resamplings for spatio-temporal tasks.

Other Resampling: Resampling, mlr_resamplings, mlr_resamplings_bootstrap, mlr_resamplings_custom, mlr_resamplings_custom_cv, mlr_resamplings_cv, mlr_resamplings_insample, mlr_resamplings_loo, mlr_resamplings_repeated_cv, mlr_resamplings_subsampling

Super class

mlr3::Resampling -> ResamplingHoldout

Active bindings

iters

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

Methods

Public methods

• ResamplingHoldout$new()

• ResamplingHoldout$clone()

Inherited methods

• mlr3::Resampling$format()
• mlr3::Resampling$help()
• mlr3::Resampling$instantiate()
• mlr3::Resampling$print()
• mlr3::Resampling$test_set()
• mlr3::Resampling$train_set()

---

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

# Instantiate Resampling
holdout = rsmp("holdout", ratio = 0.5)
holdout$instantiate(task)

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

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

# Internal storage:
holdout$instance # simple list
#> $train
#> [1] 1 4 5 8 9
#> 
#> $test
#> [1]  2  3  6  7 10
#>