Last chance! 50% off unlimited learning
Sale ends in
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$get("custom")
rsmp("custom")
mlr3::Resampling -> ResamplingCustom
iters(integer(1))
Returns the number of resampling iterations, depending on the values stored in the param_set.
hash(character(1))
Hash (unique identifier) for this object.
new()Creates a new instance of this R6 class.
ResamplingCustom$new()
instantiate()Instantiate this Resampling with custom splits into training and test set.
ResamplingCustom$instantiate(task, train_sets, test_sets)
taskTask
Mainly used to check if train_sets and test_sets are feasible.
train_sets(list of integer())
List with row ids for training, one list element per iteration.
Must have the same length as test_sets.
test_sets(list of integer())
List with row ids for testing, one list element per iteration.
Must have the same length as train_sets.
clone()The objects of this class are cloneable with this method.
ResamplingCustom$clone(deep = FALSE)
deepWhether to make a deep clone.
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_cv,
mlr_resamplings_holdout,
mlr_resamplings_insample,
mlr_resamplings_repeated_cv,
mlr_resamplings_subsampling,
mlr_resamplings
# NOT RUN {
# Create a task with 10 observations
task = tsk("iris")
task$filter(1:10)
# Instantiate Resampling
rc = rsmp("custom")
train_sets = list(1:5, 5:10)
test_sets = list(5:10, 1:5)
rc$instantiate(task, train_sets, test_sets)
rc$train_set(1)
rc$test_set(1)
# }
Run the code above in your browser using DataLab