Environmental Block Cross Validation. This strategy uses k-means clustering to specify blocks of similar environmental conditions. Only numeric features can be used. The features used for building blocks can be specified in the param_set. By default, all numeric features are used.

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 -> ResamplingSpCVEnv

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

ResamplingSpCVEnv$new(id = "spcv_env")

Arguments

id

character(1)
Identifier for the resampling strategy.


Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSpCVEnv$instantiate(task)

Arguments

task

Task
A task to instantiate.


Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSpCVEnv$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_env", folds = 4) 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: 1 1 #> 2: 2 1 #> 3: 4 1 #> 4: 5 1 #> 5: 6 1 #> --- #> 747: 346 4 #> 748: 370 4 #> 749: 474 4 #> 750: 539 4 #> 751: 623 4