mlr3 (version 0.12.0)

mlr_resamplings_holdout: Holdout Resampling

Description

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.

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

• ResamplingHoldout$new()

• ResamplingHoldout$clone()

• 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.

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. 10.1162/evco_a_00069.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/resampling.html

• 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).

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

Examples

# NOT RUN {
# 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
# }