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)
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.

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.