ObjectiveTuning

Stores the objective function that estimates the performance of hyperparameter configurations. This class is usually constructed internally by the TuningInstanceSingleCrit / TuningInstanceMultiCrit.

Super class

bbotk::Objective -> ObjectiveTuning

Public fields

task

(mlr3::Task).

learner

(mlr3::Learner).

resampling

(mlr3::Resampling).

measures

(list of mlr3::Measure).

store_models

(logical(1)).

store_benchmark_result

(logical(1)).

archive

(ArchiveTuning).

Methods

Public methods

• ObjectiveTuning$new()

• ObjectiveTuning$clone()

Inherited methods

• bbotk::Objective$eval()

• bbotk::Objective$eval_dt()

• bbotk::Objective$eval_many()

• bbotk::Objective$format()

• bbotk::Objective$print()

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Method new()

Creates a new instance of this R6 class.

Usage

ObjectiveTuning$new(
  task,
  learner,
  resampling,
  measures,
  check_values = TRUE,
  store_benchmark_result = TRUE,
  store_models = FALSE
)

Arguments

task

(mlr3::Task)
Task to operate on.

learner

(mlr3::Learner).

resampling

(mlr3::Resampling)
Uninstantiated resamplings are instantiated during construction so that all configurations are evaluated on the same data splits.

measures

(list of mlr3::Measure)
Measures to optimize. If NULL, mlr3's default measure is used.

check_values

(logical(1))
Should parameters before the evaluation and the results be checked for validity?

store_benchmark_result

(logical(1))
If TRUE (default), stores the mlr3::BenchmarkResult in archive.

store_models

(logical(1))
If FALSE (default), the fitted models are not stored in the mlr3::BenchmarkResult. If store_benchmark_result = FALSE, the models are only stored temporarily and not accessible after the tuning. This combination might be useful for measures that require a model.

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Method clone()

The objects of this class are cloneable with this method.

Usage

ObjectiveTuning$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.