mlr3 (version 0.18.0)

mlr_resamplings_bootstrap: Bootstrap Resampling

Description

Splits data into bootstrap samples (sampling with replacement). Hyperparameters are the number of bootstrap iterations (repeats, default: 30) and the ratio of observations to draw per iteration (ratio, default: 1) for the training set.

Dictionary

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

mlr_resamplings$get("bootstrap")
rsmp("bootstrap")

Parameters

• repeats (integer(1))
  Number of repetitions.

• ratio (numeric(1))
  Ratio of observations to put into the training set.

Super class

mlr3::Resampling -> ResamplingBootstrap

Active bindings

iters

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

Methods

Public methods

• ResamplingBootstrap$new()

• ResamplingBootstrap$clone()

Inherited methods

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

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Method new()

Creates a new instance of this R6 class.

Usage

ResamplingBootstrap$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingBootstrap$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Bischl B, Mersmann O, Trautmann H, Weihs C (2012). “Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation.” Evolutionary Computation, 20(2), 249--275. tools:::Rd_expr_doi("10.1162/evco_a_00069").

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_custom, mlr_resamplings_custom_cv, mlr_resamplings_cv, mlr_resamplings_holdout, mlr_resamplings_insample, mlr_resamplings_loo, mlr_resamplings_repeated_cv, mlr_resamplings_subsampling

Examples

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

# Instantiate Resampling
bootstrap = rsmp("bootstrap", repeats = 2, ratio = 1)
bootstrap$instantiate(task)

# Individual sets:
bootstrap$train_set(1)
bootstrap$test_set(1)

# Disjunct sets:
intersect(bootstrap$train_set(1), bootstrap$test_set(1))

# Internal storage:
bootstrap$instance$M # Matrix of counts