mlr3 (version 0.1.0-9000)

mlr_resamplings_repeated_cv: Repeated Cross Validation Resampling

Description

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.

Usage

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

# NOT RUN {
# 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$iters
rrcv$folds(1:6)
rrcv$repeats(1:6)

# Individual sets:
rrcv$train_set(1)
rrcv$test_set(1)
intersect(rrcv$train_set(1), rrcv$test_set(1))

# Internal storage:
rrcv$instance # table
# }