Splits data into a training set and a test set.
Parameter ratio
determines the ratio of observation going into the training set (default: 2/3).
This Resampling can be instantiated via the dictionary mlr_resamplings or with the associated sugar function rsmp()
:
mlr_resamplings$get("holdout") rsmp("holdout")
ratio
(numeric(1)
)
Ratio of observations to put into the training set.
Bischl B, Mersmann O, Trautmann H, Weihs C (2012). “Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation.” Evolutionary Computation, 20(2), 249--275. doi: 10.1162/evco_a_00069 .
Dictionary of Resamplings: mlr_resamplings
as.data.table(mlr_resamplings)
for a complete table of all (also dynamically created) Resampling implementations.
Other Resampling:
Resampling
,
mlr_resamplings_bootstrap
,
mlr_resamplings_custom
,
mlr_resamplings_cv
,
mlr_resamplings_insample
,
mlr_resamplings_loo
,
mlr_resamplings_repeated_cv
,
mlr_resamplings_subsampling
,
mlr_resamplings
mlr3::Resampling
-> ResamplingHoldout
iters
(integer(1)
)
Returns the number of resampling iterations, depending on the values stored in the param_set
.
new()
Creates a new instance of this R6 class.
ResamplingHoldout$new()
clone()
The objects of this class are cloneable with this method.
ResamplingHoldout$clone(deep = FALSE)
deep
Whether to make a deep clone.
# Create a task with 10 observations task = tsk("penguins") task$filter(1:10) # Instantiate Resampling rho = rsmp("holdout", ratio = 0.5) rho$instantiate(task) # Individual sets: rho$train_set(1)#> [1] 2 5 6 8 9rho$test_set(1)#> [1] 1 3 4 7 10#> integer(0)# Internal storage: rho$instance # simple list#> $train #> [1] 2 5 6 8 9 #> #> $test #> [1] 1 3 4 7 10 #>