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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


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

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

Examples

# Create a task with 10 observations
task = tsk("penguins")
task$filter(1:10)

# Instantiate Resampling
rins = rsmp("insample")
rins$instantiate(task)

rins$train_set(1)
rins$test_set(1)

# Internal storage:
rins$instance # just row ids

mlr3

Machine Learning in R - Next Generation

v0.11.0
LGPL-3
Authors
Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Jakob Richter [aut] (<https://orcid.org/0000-0003-4481-5554>), Patrick Schratz [aut] (<https://orcid.org/0000-0003-0748-6624>), Giuseppe Casalicchio [ctb] (<https://orcid.org/0000-0001-5324-5966>), Stefan Coors [ctb] (<https://orcid.org/0000-0002-7465-2146>), Quay Au [ctb] (<https://orcid.org/0000-0002-5252-8902>), Martin Binder [aut], Marc Becker [ctb] (<https://orcid.org/0000-0002-8115-0400>)
Initial release

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