Spatial Block Cross validation implemented by the blockCV package.

Details

By default blockCV::spatialBlock() does not allow the creation of multiple repetitions. mlr3spatiotempcv adds support for this when using the range argument for fold creation. When supplying a vector of length(repeats) for argument range, these different settings will be used to create folds which differ among the repetitions.

Multiple repetitions are not possible when using the "row & cols" approach because the created folds will always be the same.

References

Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G (2018). “blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models.” bioRxiv. doi: 10.1101/357798 .

Super class

mlr3::Resampling -> ResamplingSpCVBlock

Active bindings

iters

integer(1)
Returns the number of resampling iterations, depending on the values stored in the param_set.

Methods

Public methods

Inherited methods

Method new()

Create an "Environmental Block" resampling instance.

Usage

ResamplingSpCVBlock$new(id = "spcv_block")

Arguments

id

character(1)
Identifier for the resampling strategy.


Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSpCVBlock$instantiate(task)

Arguments

task

Task
A task to instantiate.


Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSpCVBlock$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) { library(mlr3) task = tsk("ecuador") # Instantiate Resampling rcv = rsmp("spcv_block", range = 1000) 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 }
#> row_id fold #> 1: 3 1 #> 2: 5 1 #> 3: 19 1 #> 4: 46 1 #> 5: 54 1 #> --- #> 747: 541 10 #> 748: 557 10 #> 749: 693 10 #> 750: 712 10 #> 751: 739 10