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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$get("holdout") rsmp("holdout")
ratio
(numeric(1)
)
Ratio of observations to put into the training set.
mlr3::Resampling
-> ResamplingHoldout
iters
(integer(1)
)
Returns the number of resampling iterations, depending on the values stored in the param_set
.
new()
Creates a new instance of this R6 class.
ResamplingHoldout$new()
clone()
The objects of this class are cloneable with this method.
ResamplingHoldout$clone(deep = FALSE)
deep
Whether to make a deep clone.
mlr3bischl_2012
Dictionary of Resamplings: mlr_resamplings
as.data.table(mlr_resamplings)
for a complete table of all (also dynamically created) Resampling implementations.
Other Resampling:
Resampling
,
mlr_resamplings_bootstrap
,
mlr_resamplings_custom
,
mlr_resamplings_cv
,
mlr_resamplings_insample
,
mlr_resamplings_repeated_cv
,
mlr_resamplings_subsampling
,
mlr_resamplings
# NOT RUN {
# Create a task with 10 observations
task = tsk("iris")
task$filter(1:10)
# Instantiate Resampling
rho = rsmp("holdout", ratio = 0.5)
rho$instantiate(task)
# Individual sets:
rho$train_set(1)
rho$test_set(1)
intersect(rho$train_set(1), rho$test_set(1))
# Internal storage:
rho$instance # simple list
# }
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