Leave-One-Out Cross-Validation
Splits data using leave-one-observation-out. This is identical to cross-validation with the number of folds set to the number of observations.
This Resampling can be instantiated via the dictionary mlr_resamplings or with the associated sugar function rsmp():
mlr_resamplings$get("loo")
rsmp("loo")mlr3::Resampling -> ResamplingLOO
iters(integer(1))
Returns the number of resampling iterations which is the number of rows of the task
provided to instantiate. Is NA if the resampling has not been instantiated.
new()
Creates a new instance of this R6 class.
ResamplingLOO$new()
clone()
The objects of this class are cloneable with this method.
ResamplingLOO$clone(deep = FALSE)
deepWhether to make a deep clone.
Bischl B, Mersmann O, Trautmann H, Weihs C (2012). “Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation.” Evolutionary Computation, 20(2), 249–275. doi: 10.1162/evco_a_00069.
as.data.table(mlr_resamplings) for a complete table of all (also dynamically created) Resampling implementations.
# Create a task with 10 observations
task = tsk("penguins")
task$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 # vectorPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.