mlr3 (version 0.5.0)

mlr_resamplings_loo: Leave-One-Out Cross Validation

Description

Splits data using leave-one-observation-out. This is identical to cross validation with the number of folds set to the number of observations.

Dictionary

This Resampling can be instantiated via the dictionary mlr_resamplings or with the associated sugar function rsmp():

mlr_resamplings$get("loo")
rsmp("loo")

Super class

mlr3::Resampling -> ResamplingLOO

Active bindings

iters

(integer(1)) Returns the number of resampling iterations which is the number of rows of the task provided to instantiate. Is NA if the resampling has not been instantiated.

Methods

Public methods

• ResamplingLOO$new()

• ResamplingLOO$clone()

• mlr3::Resampling$format()

• mlr3::Resampling$help()

• mlr3::Resampling$instantiate()

• mlr3::Resampling$print()

• mlr3::Resampling$test_set()

• mlr3::Resampling$train_set()

Method new()

Creates a new instance of this R6 class.

Usage

ResamplingLOO$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingLOO$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

mlr3bischl_2012

See Also

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_holdout, mlr_resamplings_insample, mlr_resamplings_repeated_cv, mlr_resamplings_subsampling, mlr_resamplings

Examples

# NOT RUN {
# Create a task with 10 observations
task = tsk("iris")
task$filter(1:10)

# Instantiate Resampling
rcv = rsmp("loo")
rcv$instantiate(task)

# Individual sets:
rcv$train_set(1)
rcv$test_set(1)
intersect(rcv$train_set(1), rcv$test_set(1))

# Internal storage:
rcv$instance # vector
# }