Splits data using leave-one-observation-out. This is identical to cross-validation with the number of folds set to the number of observations.
If this resampling is combined with the grouping features of tasks, it is possible to create custom splits based on an arbitrary factor variable, see the examples.
This Resampling can be instantiated via the dictionary mlr_resamplings or with the associated sugar function
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.
Creates a new instance of this R6 class.
The objects of this class are cloneable with this method.
ResamplingLOO$clone(deep = FALSE)
Whether to make a deep clone.
Bischl B, Mersmann O, Trautmann H, Weihs C (2012). “Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation.” Evolutionary Computation, 20(2), 249–275. doi: 10.1162/evco_a_00069.
Package mlr3spatiotempcv for spatio-temporal resamplings.
Dictionary of Resamplings: mlr_resamplings
as.data.table(mlr_resamplings) for a table of available Resamplings in the running session (depending on the loaded packages).
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# Create a task with 10 observations task = tsk("penguins") task$filter(1:10) # Instantiate Resampling loo = rsmp("loo") loo$instantiate(task) # Individual sets: loo$train_set(1) loo$test_set(1) # Disjunct sets: intersect(loo$train_set(1), loo$test_set(1)) # Internal storage: loo$instance # vector # Combine with group feature of tasks: task = tsk("penguins") task$set_col_roles("island", add_to = "group") loo$instantiate(task) loo$iters # one fold for each level of "island"
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