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 -> ResamplingRepeatedSpCVEnv

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 "coordinate-based" repeated resampling instance.

Usage

ResamplingRepeatedSpCVEnv$new(id = "repeated_spcv_env")

Arguments

id

character(1)
Identifier for the resampling strategy.


Method folds()

Translates iteration numbers to fold number.

Usage

ResamplingRepeatedSpCVEnv$folds(iters)

Arguments

iters

integer()
Iteration number.


Method repeats()

Translates iteration numbers to repetition number.

Usage

ResamplingRepeatedSpCVEnv$repeats(iters)

Arguments

iters

integer()
Iteration number.


Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingRepeatedSpCVEnv$instantiate(task)

Arguments

task

Task
A task to instantiate.


Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingRepeatedSpCVEnv$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 rrcv = rsmp("repeated_spcv_env", folds = 4, repeats = 2) rrcv$instantiate(task) # Individual sets: rrcv$train_set(1) rrcv$test_set(1) intersect(rrcv$train_set(1), rrcv$test_set(1)) # Internal storage: rrcv$instance }
#> row_id rep fold #> 1: 1 1 2 #> 2: 2 1 2 #> 3: 3 1 4 #> 4: 4 1 2 #> 5: 5 1 2 #> --- #> 1498: 747 2 4 #> 1499: 748 2 4 #> 1500: 749 2 4 #> 1501: 750 2 4 #> 1502: 751 2 4