makeHyperoptWrapper: Fuse learner with mlrHyperopt tuning.
In jakob-r/mlrHyperopt: Easy Hyperparameteroptimization with mlr and mlrMBO

Description Usage Arguments Value Examples

Fuses an mlr base learner with mlrHyperopt tuning. Creates a learner object, which can be used like any other learner object. If the train function is called on it, hyperopt is invoked to select an optimal set of hyperparameter values. Finally, a model is fitted on the complete training data with these optimal hyperparameters and returned.

makeHyperoptWrapper(
  learner,
  par.config = NULL,
  hyper.control = NULL,
  show.info = getMlrOptions()$show.info
)

`learner`	[`Learner`] Learner
`par.config`	[`ParConfig`] The Parameter Configuration
`hyper.control`	[`HyperControl`] The Hyperparameter Control Object
`show.info`	[`logical(1)`] Print verbose output on console? Default is set via `configureMlr`.

[Learner].

task = makeClassifTask(data = iris, target = "Species")
lrn = makeLearner("classif.svm")
lrn = makeHyperoptWrapper(lrn)
mod = train(lrn, task)
print(getTuneResult(mod))
# nested resampling for evaluation
# we also extract tuned hyper pars in each iteration
r = resample(lrn, task, cv3, extract = getTuneResult)
getNestedTuneResultsX(r)