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.

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. doi: 10.1162/evco_a_00069 .

See also

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

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

ResamplingBootstrap$new()


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.

Examples

# Create a task with 10 observations task = tsk("iris") task$filter(1:10) # Instantiate Resampling rb = rsmp("bootstrap", repeats = 2, ratio = 1) rb$instantiate(task) # Individual sets: rb$train_set(1)
#> [1] 1 2 2 3 6 7 8 9 9 10
rb$test_set(1)
#> [1] 4 5
intersect(rb$train_set(1), rb$test_set(1))
#> integer(0)
# Internal storage: rb$instance$M # Matrix of counts
#> #> [,1] [,2] #> [1,] 1 1 #> [2,] 2 0 #> [3,] 1 1 #> [4,] 0 0 #> [5,] 0 2 #> [6,] 1 3 #> [7,] 1 1 #> [8,] 1 2 #> [9,] 2 0 #> [10,] 1 0