makeHyperControl: Hyperparameter Tuning Control Object
In jakob-r/mlrHyperopt: Easy Hyperparameteroptimization with mlr and mlrMBO

Description Usage Arguments Value See Also Examples

Defines how the hyperparameter tuning should be conducted

makeHyperControl(
  mlr.control = NULL,
  resampling = NULL,
  measures = NULL,
  par.config = NULL
)

`mlr.control`	[`TuneControl`] Control object for search method. Also selects the optimization algorithm for tuning.
`resampling`	[`ResampleDesc`] The resampling determines how the performance is obtained during tuning.
`measures`	[`Measure` \| list of `Measure`] Performance measure(s) to evaluate. Default is the default measure for the task, see here `getDefaultMeasure`.
`par.config`	[`ParConfig`] The Parameter Configuration

[HyperControl]

Other HyperControl: getHyperControlMeasures(), getHyperControlMlrControl(), getHyperControlResampling(), setHyperControlMeasures(), setHyperControlMlrControl(), setHyperControlResampling()

hyper.control = makeHyperControl(
  mlr.control = makeTuneControlRandom(maxit = 5),
  resampling = cv2,
  measures = acc
)
hyperopt(task = iris.task, learner = "classif.svm", hyper.control = hyper.control)