mlr_resamplings_custom | R Documentation |
Splits data into training and test sets using manually provided indices.
This Resampling can be instantiated via the dictionary mlr_resamplings or with the associated sugar function rsmp()
:
mlr_resamplings$get("custom") rsmp("custom")
mlr3::Resampling
-> ResamplingCustom
iters
(integer(1)
)
Returns the number of resampling iterations, depending on the values stored in the param_set
.
new()
Creates a new instance of this R6 class.
ResamplingCustom$new()
instantiate()
Instantiate this Resampling with custom splits into training and test set.
ResamplingCustom$instantiate(task, train_sets, test_sets)
task
Task
Mainly used to check if train_sets
and test_sets
are feasible.
train_sets
(list of integer()
)
List with row ids for training, one list element per iteration.
Must have the same length as test_sets
.
test_sets
(list of integer()
)
List with row ids for testing, one list element per iteration.
Must have the same length as train_sets
.
clone()
The objects of this class are cloneable with this method.
ResamplingCustom$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_cv
,
mlr_resamplings_cv
,
mlr_resamplings_holdout
,
mlr_resamplings_insample
,
mlr_resamplings_loo
,
mlr_resamplings_repeated_cv
,
mlr_resamplings_subsampling
# Create a task with 10 observations
task = tsk("penguins")
task$filter(1:10)
# Instantiate Resampling
custom = rsmp("custom")
train_sets = list(1:5, 5:10)
test_sets = list(5:10, 1:5)
custom$instantiate(task, train_sets, test_sets)
custom$train_set(1)
custom$test_set(1)
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