Splits data into a single training set and a test set. Parameter ratio determines the ratio of observation in the train set (default: 2/3).

ResamplingHoldout

## 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
rho = mlr_resamplings$get("holdout") rho$instantiate(task)

# Individual sets:
rho$train_set(1)#> [1] 9 3 6 7 1 8 10rho$test_set(1)#> [1] 2 4 5intersect(rho$train_set(1), rho$test_set(1))#> integer(0)
# Internal storage:
rho$instance # simple list#>$train
#> [1]  9  3  6  7  1  8 10
#>
#> \$test
#> [1] 2 4 5
#>