mlr_resamplings_insample | R Documentation |
Uses all observations as training and as test set.
This Resampling can be instantiated via the dictionary mlr_resamplings or with the associated sugar function rsmp()
:
mlr_resamplings$get("insample") rsmp("insample")
mlr3::Resampling
-> ResamplingInsample
iters
(integer(1)
)
Returns the number of resampling iterations, depending on the values stored in the param_set
.
new()
Creates a new instance of this R6 class.
ResamplingInsample$new()
clone()
The objects of this class are cloneable with this method.
ResamplingInsample$clone(deep = FALSE)
deep
Whether to make a deep clone.
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter3/evaluation_and_benchmarking.html#sec-resampling
Package mlr3spatiotempcv for spatio-temporal resamplings.
Dictionary of Resamplings: mlr_resamplings
as.data.table(mlr_resamplings)
for a table of available Resamplings in the running session (depending on the loaded packages).
mlr3spatiotempcv for additional Resamplings for spatio-temporal tasks.
Other Resampling:
Resampling
,
mlr_resamplings
,
mlr_resamplings_bootstrap
,
mlr_resamplings_custom
,
mlr_resamplings_custom_cv
,
mlr_resamplings_cv
,
mlr_resamplings_holdout
,
mlr_resamplings_loo
,
mlr_resamplings_repeated_cv
,
mlr_resamplings_subsampling
# Create a task with 10 observations
task = tsk("penguins")
task$filter(1:10)
# Instantiate Resampling
insample = rsmp("insample")
insample$instantiate(task)
# Train set equal to test set:
setequal(insample$train_set(1), insample$test_set(1))
# Internal storage:
insample$instance # just row ids
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