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

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.

hash

(character(1))
Hash (unique identifier) for this object.

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

ResamplingCustom$new()


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.


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("iris") task$filter(1:10) # Instantiate Resampling rc = rsmp("custom") train_sets = list(1:5, 5:10) test_sets = list(5:10, 1:5) rc$instantiate(task, train_sets, test_sets) rc$train_set(1)
#> [1] 1 2 3 4 5
rc$test_set(1)
#> [1] 5 6 7 8 9 10