Subsampling Resampling
Splits data repeats (default: 30) times into training and test set
with a ratio of ratio (default: 2/3) observations going into the training set.
This Resampling can be instantiated via the dictionary mlr_resamplings or with the associated sugar function rsmp():
mlr_resamplings$get("holdout")
rsmp("holdout")repeats (integer(1))
Number of repetitions.
ratio (numeric(1))
Ratio of observations to put into the training set.
mlr3::Resampling -> ResamplingSubsampling
iters(integer(1))
Returns the number of resampling iterations, depending on the values stored in the param_set.
new()
Creates a new instance of this R6 class.
ResamplingSubsampling$new()
clone()
The objects of this class are cloneable with this method.
ResamplingSubsampling$clone(deep = FALSE)
deepWhether to make a deep clone.
Bischl B, Mersmann O, Trautmann H, Weihs C (2012). “Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation.” Evolutionary Computation, 20(2), 249–275. doi: 10.1162/evco_a_00069.
as.data.table(mlr_resamplings) for a complete table of all (also dynamically created) Resampling implementations.
# Create a task with 10 observations
task = tsk("penguins")
task$filter(1:10)
# Instantiate Resampling
rss = rsmp("subsampling", repeats = 2, ratio = 0.5)
rss$instantiate(task)
# Individual sets:
rss$train_set(1)
rss$test_set(1)
intersect(rss$train_set(1), rss$test_set(1))
# Internal storage:
rss$instance$train # list of index vectorsPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.