mlr_resamplings_repeated_spcv_coords | R Documentation |
Splits data by clustering in the coordinate space.
See the upstream implementation at sperrorest::partition_kmeans()
and
Brenning (2012) for further information.
Universal partitioning method that splits the data in the coordinate space.
Useful for spatially homogeneous datasets that cannot be split well with
rectangular approaches like ResamplingSpCVBlock
.
folds
(integer(1)
)
Number of folds.
repeats
(integer(1)
)
Number of repeats.
mlr3::Resampling
-> ResamplingRepeatedSpCVCoords
iters
integer(1)
Returns the number of resampling iterations, depending on the
values stored in the param_set
.
new()
Create an "coordinate-based" repeated resampling instance.
For a list of available arguments, please see sperrorest::partition_cv.
ResamplingRepeatedSpCVCoords$new(id = "repeated_spcv_coords")
id
character(1)
Identifier for the resampling strategy.
folds()
Translates iteration numbers to fold number.
ResamplingRepeatedSpCVCoords$folds(iters)
iters
integer()
Iteration number.
repeats()
Translates iteration numbers to repetition number.
ResamplingRepeatedSpCVCoords$repeats(iters)
iters
integer()
Iteration number.
instantiate()
Materializes fixed training and test splits for a given task.
ResamplingRepeatedSpCVCoords$instantiate(task)
task
Task
A task to instantiate.
clone()
The objects of this class are cloneable with this method.
ResamplingRepeatedSpCVCoords$clone(deep = FALSE)
deep
Whether to make a deep clone.
Brenning A (2012). “Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest.” In 2012 IEEE International Geoscience and Remote Sensing Symposium. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/igarss.2012.6352393")}.
library(mlr3)
task = tsk("diplodia")
# Instantiate Resampling
rrcv = rsmp("repeated_spcv_coords", folds = 3, repeats = 5)
rrcv$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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