Defines how the hyperparameter tuning should be conducted

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

Arguments

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

Value

[HyperControl]

See also

Examples

hyper.control = makeHyperControl( mlr.control = makeTuneControlRandom(maxit = 5), resampling = cv2, measures = acc ) hyperopt(task = iris.task, learner = "classif.svm", hyper.control = hyper.control)
#> [Tune] Started tuning learner classif.svm for parameter set:
#> Type len Def Constr Req Tunable Trafo #> cost numeric - 0 -15 to 15 - TRUE Y #> gamma numeric - -2 -15 to 15 - TRUE Y
#> With control class: TuneControlRandom
#> Imputation value: -0
#> [Tune-x] 1: cost=7.5e+03; gamma=0.0178
#> [Tune-y] 1: acc.test.mean=0.9333333; time: 0.0 min
#> [Tune-x] 2: cost=1.28e+03; gamma=123
#> [Tune-y] 2: acc.test.mean=0.3000000; time: 0.0 min
#> [Tune-x] 3: cost=0.0014; gamma=0.00285
#> [Tune-y] 3: acc.test.mean=0.2800000; time: 0.0 min
#> [Tune-x] 4: cost=0.0661; gamma=0.00402
#> [Tune-y] 4: acc.test.mean=0.2800000; time: 0.0 min
#> [Tune-x] 5: cost=0.0271; gamma=0.000712
#> [Tune-y] 5: acc.test.mean=0.2800000; time: 0.0 min
#> [Tune] Result: cost=7.5e+03; gamma=0.0178 : acc.test.mean=0.9333333
#> Tune result: #> Op. pars: cost=7.5e+03; gamma=0.0178 #> acc.test.mean=0.9333333