mlr3 (version 0.5.0)

mlr_resamplings_custom: Custom Resampling

Description

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")

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

• ResamplingCustom$new()

• ResamplingCustom$instantiate()

• ResamplingCustom$clone()

• mlr3::Resampling$format()

• mlr3::Resampling$help()

• mlr3::Resampling$print()

• mlr3::Resampling$test_set()

• mlr3::Resampling$train_set()

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.

See Also

Dictionary of Resamplings: mlr_resamplings

as.data.table(mlr_resamplings) for a complete table of all (also dynamically created) Resampling implementations.

Other Resampling: Resampling, mlr_resamplings_bootstrap, mlr_resamplings_cv, mlr_resamplings_holdout, 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("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$train_set(1)
rc$test_set(1)
# }