mlr3tuning (version 0.9.0)

auto_tuner: Syntactic Sugar for Automatic Tuning

Description

Function to create an AutoTuner object.

Usage

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

# NOT RUN {
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"))
# }