Cross Validation Resampling

Source: R/mlr_resamplings_cv.R

Cross validation with folds folds (default: 10).

ResamplingCV

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

• 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
rcv = mlr_resamplings$get("cv")
rcv$param_set$values = list(folds = 3)
rcv$instantiate(task)

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

# Internal storage:
rcv$instance # table
#>     row_id fold
#>  1:      2    1
#>  2:      3    1
#>  3:      6    1
#>  4:      7    1
#>  5:      4    2
#>  6:      8    2
#>  7:      9    2
#>  8:      1    3
#>  9:      5    3
#> 10:     10    3