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
Other HyperControl: getHyperControlMeasures
,
getHyperControlMlrControl
,
getHyperControlResampling
,
setHyperControlMeasures
,
setHyperControlMlrControl
,
setHyperControlResampling
Examples
#> [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=1.74e+03; gamma=0.000271
#> Resampling: cross-validation
#> Measures: acc
#> [Tune-y] 1: acc.test.mean=0.9666667; time: 0.0 min
#> [Tune-x] 2: cost=85.7; gamma=0.000866
#> Resampling: cross-validation
#> Measures: acc
#> [Tune-y] 2: acc.test.mean=0.9666667; time: 0.0 min
#> [Tune-x] 3: cost=8.7e-05; gamma=0.0179
#> Resampling: cross-validation
#> Measures: acc
#> [Tune-y] 3: acc.test.mean=0.2866667; time: 0.0 min
#> [Tune-x] 4: cost=0.00639; gamma=0.00551
#> Resampling: cross-validation
#> Measures: acc
#> [Tune-y] 4: acc.test.mean=0.2866667; time: 0.0 min
#> [Tune-x] 5: cost=0.157; gamma=0.000162
#> Resampling: cross-validation
#> Measures: acc
#> [Tune-y] 5: acc.test.mean=0.2866667; time: 0.0 min
#> [Tune] Result: cost=85.7; gamma=0.000866 : acc.test.mean=0.9666667
#> Tune result:
#> Op. pars: cost=85.7; gamma=0.000866
#> acc.test.mean=0.9666667