mlr_resamplings_holdout function

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.

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$train_set(1)
holdout$test_set(1)

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

# Internal storage:
holdout$instance # simple list

References

Bischl B, Mersmann O, Trautmann H, Weihs C (2012). Resampling Methods for Meta-Model Validation with Recommendations forEvolutionary Computation.

Evolutionary Computation, 20 (2), 249--275. tools:::Rd_expr_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 list("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).
• list("mlr3spatiotempcv") for additional Resampling s 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

• list("ResamplingHoldout$new()")

• list("ResamplingHoldout$clone()")

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.