Custom Resampling

Splits data into training and test sets using manually provided indices.

Dictionary

This Resampling can be instantiated via the dictionary mlr_resamplings or with the associated sugar function rsmp():

mlr_resamplings$get("custom")
rsmp("custom")

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_cv, mlr_resamplings_cv, mlr_resamplings_holdout, mlr_resamplings_insample, mlr_resamplings_loo, mlr_resamplings_repeated_cv, mlr_resamplings_subsampling

Super class

mlr3::Resampling -> ResamplingCustom

Active bindings

iters

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

Methods

Public methods

• ResamplingCustom$new()

• ResamplingCustom$instantiate()

• ResamplingCustom$clone()

Inherited methods

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

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Method new()

Creates a new instance of this R6 class.

Usage

ResamplingCustom$new()

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Method instantiate()

Instantiate this Resampling with custom splits into training and test set.

Usage

ResamplingCustom$instantiate(task, train_sets, test_sets)

Arguments

task

Task
Mainly used to check if train_sets and test_sets are feasible.

train_sets

(list of integer())
List with row ids for training, one list element per iteration. Must have the same length as test_sets.

test_sets

(list of integer())
List with row ids for testing, one list element per iteration. Must have the same length as train_sets.

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Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingCustom$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
custom = rsmp("custom")
train_sets = list(1:5, 5:10)
test_sets = list(5:10, 1:5)
custom$instantiate(task, train_sets, test_sets)

custom$train_set(1)
#> [1] 1 2 3 4 5
custom$test_set(1)
#> [1]  5  6  7  8  9 10