mlr_resamplings_repeated_spcv_disc | R Documentation |
(sperrorest) Repeated spatial "disc" resampling
(sperrorest) Repeated spatial "disc" resampling
repeats
(integer(1)
)
Number of repeats.
mlr3::Resampling
-> ResamplingRepeatedSpCVDisc
iters
integer(1)
Returns the number of resampling iterations, depending on the
values stored in the param_set
.
new()
Create a "Spatial 'Disc' resampling" resampling instance.
For a list of available arguments, please see sperrorest::partition_disc.
ResamplingRepeatedSpCVDisc$new(id = "repeated_spcv_disc")
id
character(1)
Identifier for the resampling strategy.
folds()
Translates iteration numbers to fold number.
ResamplingRepeatedSpCVDisc$folds(iters)
iters
integer()
Iteration number.
repeats()
Translates iteration numbers to repetition number.
ResamplingRepeatedSpCVDisc$repeats(iters)
iters
integer()
Iteration number.
instantiate()
Materializes fixed training and test splits for a given task.
ResamplingRepeatedSpCVDisc$instantiate(task)
task
Task
A task to instantiate.
clone()
The objects of this class are cloneable with this method.
ResamplingRepeatedSpCVDisc$clone(deep = FALSE)
deep
Whether to make a deep clone.
Brenning A (2012). “Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest.” In 2012 IEEE International Geoscience and Remote Sensing Symposium. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/igarss.2012.6352393")}.
library(mlr3)
task = tsk("ecuador")
# Instantiate Resampling
rrcv = rsmp("repeated_spcv_disc",
folds = 3L, repeats = 2,
radius = 200L, buffer = 200L)
rrcv$instantiate(task)
# Individual sets:
rrcv$iters
rrcv$folds(1:6)
rrcv$repeats(1:6)
# Individual sets:
rrcv$train_set(1)
rrcv$test_set(1)
intersect(rrcv$train_set(1), rrcv$test_set(1))
# Internal storage:
rrcv$instance # table
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