mlr3 (version 0.12.0)

mlr_resamplings_insample: Insample Resampling

Description

Uses all observations as training and as test set.

Dictionary

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

mlr_resamplings$get("insample")
rsmp("insample")

Super class

mlr3::Resampling -> ResamplingInsample

Public fields

iters

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

Methods

Public methods

• ResamplingInsample$new()

• ResamplingInsample$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

ResamplingInsample$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingInsample$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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_holdout, 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
insample = rsmp("insample")
insample$instantiate(task)

# Train set equal to test set:
setequal(insample$train_set(1), insample$test_set(1))

# Internal storage:
insample$instance # just row ids
# }