auto_tuner(
method,
learner,
resampling,
measure,
term_evals = NULL,
term_time = NULL,
search_space = NULL,
...
)
Arguments
| method |
(character(1))
Key to retrieve tuner from mlr_tuners dictionary. |
| learner |
(mlr3::Learner). |
| resampling |
(mlr3::Resampling)
Resampling that is used to evaluated the performance of the hyperparameter
configurations. Uninstantiated resamplings are instantiated during
construction so that all configurations are evaluated on the same data
splits. Already instantiated resamplings are kept unchanged. Specialized
Tuner change the resampling e.g. to evaluate a hyperparameter configuration
on different data splits. This field, however, always returns the resampling
passed in construction. |
| measure |
(mlr3::Measure)
Measure to optimize. |
| term_evals |
(integer(1))
Number of allowed evaluations. |
| term_time |
(integer(1))
Maximum allowed time in seconds. |
| search_space |
(paradox::ParamSet)
Hyperparameter search space. If NULL, the search space is constructed from
the TuneToken in the ParamSet of the learner. |
| ... |
(named list())
Named arguments to be set as parameters of the tuner. |
Value
AutoTuner
Examples
at = auto_tuner(
method = "random_search",
learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE)),
resampling = rsmp ("holdout"),
measure = msr("classif.ce"),
term_evals = 4)
at$train(tsk("pima"))
