Repeated Cross Validation Resampling

Source: R/mlr_resamplings_repeated_cv.R

repeats (default: 10) times repeated folds-fold (default: 10) cross-validation.

The iteration counter translates to repeats blocks of folds cross-validations, i.e., the first folds iterations belong to a single cross-validation.

ResamplingRepeatedCV

Format

R6::R6Class() inheriting from Resampling.

Fields

• id :: character(1)
  Stores the identifier of the learner.

• task_type :: character(1)
  Stores the type of class this learner can operate on, e.g. "classif" or "regr". A complete list of task types is stored in mlr_reflections$task_types.

• param_set :: paradox::ParamSet
  Description of available hyperparameters and hyperparameter settings.

• predict_types :: character()
  Stores the possible predict types the learner is capable of. A complete list of candidate predict types, grouped by task type, is stored in mlr_reflections$learner_predict_types.

• predict_type :: character(1)
  Stores the currently selected predict type. Must be an element of l$predict_types.

• feature_types :: character()
  Stores the feature types the learner can handle, e.g. "logical", "numeric", or "factor". A complete list of candidate feature types, grouped by task type, is stored in mlr_reflections$task_feature_types.

• properties :: character()
  Stores a set of properties/capabilities the learner has. A complete list of candidate properties, grouped by task type, is stored in mlr_reflections$learner_properties.

• packages :: character()
  Stores the names of required packages.

• fallback :: (Learner | NULL)
  Optionally stores a second Learner which is activated as fallback if this first Learner fails during train or predict. This mechanism is disabled unless you explicitly assign a learner to this slot. Additionally, you need to catch raised exceptions via encapsulation, see mlr_control().

• hash :: character(1)
  Hash (unique identifier) for this object.

Methods

• folds(iters)
integer() -> integer()
  Translates iteration numbers to fold number.

• repeats(iters)
integer() -> integer()
  Translates iteration numbers to repetition number.

• params(tag)
character(1) -> named list()
  Returns a list of hyperparameter settings from param_set where the corresponding parameters in param_set are tagged with tag. I.e., l$params("train") returns all settings of hyperparameters used in the training step.

• train(task)
  Task -> self
  Train the learner on the complete Task. The resulting model is stored in l$model.

• predict(task)
  Task -> Prediction
  Uses l$model (fitted during train()) to return a Prediction object.

Examples

# Create a task with 10 observations
task = mlr_tasks$get("iris")
task$filter(1:10)

# Instantiate Resampling
rrcv = mlr_resamplings$get("repeated_cv")
rrcv$param_set$values = list(repeats = 2, folds = 3)
rrcv$instantiate(task)
rrcv$iters
#> [1] 6
rrcv$folds(1:6)
#> [1] 1 2 1 2 1 2
rrcv$repeats(1:6)
#> [1] 1 1 1 2 2 2

# Individual sets:
rrcv$train_set(1)
#> [1]  1  2 10  3  5  8
rrcv$test_set(1)
#> [1] 4 6 7 9
intersect(rrcv$train_set(1), rrcv$test_set(1))
#> integer(0)

# Internal storage:
rrcv$instance # table
#>     row_id rep fold
#>  1:      1   1    2
#>  2:      2   1    2
#>  3:      3   1    3
#>  4:      4   1    1
#>  5:      5   1    3
#>  6:      6   1    1
#>  7:      7   1    1
#>  8:      8   1    3
#>  9:      9   1    1
#> 10:     10   1    2
#> 11:      1   2    1
#> 12:      2   2    2
#> 13:      3   2    3
#> 14:      4   2    3
#> 15:      5   2    2
#> 16:      6   2    1
#> 17:      7   2    3
#> 18:      8   2    2
#> 19:      9   2    1
#> 20:     10   2    1