generateHyperControl: Generates a hyperparameter tuning control object

In jakob-r/mlrHyperopt: Easy Hyperparameteroptimization with mlr and mlrMBO

Description

Tries to automatically create a suitable hyperparameter tuning control.

Usage

generateHyperControl(
  task,
  par.config = NULL,
  learner = NULL,
  budget.evals = 250
)

Arguments

task	[Task] The Task
par.config	[ParConfig] The Parameter Configuration
learner	[Learner] Learner
budget.evals	[integer] How many train-test iterations do you want to allow?

Value

[HyperControl]

Examples

par.config = getDefaultParConfig("regr.randomForest")
hyper.control = generateHyperControl(bh.task, par.config)
# get what is inside
getHyperControlMeasures(hyper.control)
getHyperControlMlrControl(hyper.control)
getHyperControlResampling(hyper.control)
# change what is inside
hyper.control = setHyperControlMeasures(hyper.control, measures = medse)
hyper.control = setHyperControlMlrControl(
  hyper.control,
  mlr.control = makeTuneControlRandom(maxit = 10))
hyper.control = setHyperControlResampling(hyper.control, resampling = cv3)
hyperopt(task = bh.task, par.config = par.config, hyper.control = hyper.control)

jakob-r/mlrHyperopt documentation built on Jan. 10, 2022, 4:32 p.m.