Bootstrap Resampling
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.
This Resampling can be instantiated via the dictionary mlr_resamplings or with the associated sugar function rsmp():
mlr_resamplings$get("bootstrap")
rsmp("bootstrap")repeats (integer(1))
Number of repetitions.
ratio (numeric(1))
Ratio of observations to put into the training set.
mlr3::Resampling -> ResamplingBootstrap
iters(integer(1))
Returns the number of resampling iterations, depending on the values stored in the param_set.
new()
Creates a new instance of this R6 class.
ResamplingBootstrap$new()
clone()
The objects of this class are cloneable with this method.
ResamplingBootstrap$clone(deep = FALSE)
deepWhether to make a deep clone.
Bischl B, Mersmann O, Trautmann H, Weihs C (2012). “Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation.” Evolutionary Computation, 20(2), 249–275. doi: 10.1162/evco_a_00069.
as.data.table(mlr_resamplings) for a complete table of all (also dynamically created) Resampling implementations.
# Create a task with 10 observations
task = tsk("penguins")
task$filter(1:10)
# Instantiate Resampling
rb = rsmp("bootstrap", repeats = 2, ratio = 1)
rb$instantiate(task)
# Individual sets:
rb$train_set(1)
rb$test_set(1)
intersect(rb$train_set(1), rb$test_set(1))
# Internal storage:
rb$instance$M # Matrix of countsPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.