Stores the objective function that estimates the performance of hyperparameter configurations. This class is usually constructed internally by the TuningInstanceSingleCrit / TuningInstanceMultiCrit.
bbotk::Objective
-> ObjectiveTuning
task
(mlr3::Task).
learner
resampling
measures
(list of mlr3::Measure).
store_models
(logical(1)
).
store_benchmark_result
(logical(1)
).
archive
new()
Creates a new instance of this R6 class.
ObjectiveTuning$new( task, learner, resampling, measures, check_values = TRUE, store_benchmark_result = TRUE, store_models = FALSE )
task
(mlr3::Task)
Task to operate on.
learner
resampling
(mlr3::Resampling)
Uninstantiated resamplings are instantiated during construction
so that all configurations are evaluated on the same data splits.
measures
(list of mlr3::Measure)
Measures to optimize.
If NULL
, mlr3's default measure is used.
check_values
(logical(1)
)
Should parameters before the evaluation and the results be checked for
validity?
store_benchmark_result
(logical(1)
)
If TRUE
(default), stores the mlr3::BenchmarkResult in archive.
store_models
(logical(1)
)
If FALSE
(default), the fitted models are not stored in the
mlr3::BenchmarkResult. If store_benchmark_result = FALSE
, the models are
only stored temporarily and not accessible after the tuning. This combination
might be useful for measures that require a model.
clone()
The objects of this class are cloneable with this method.
ObjectiveTuning$clone(deep = FALSE)
deep
Whether to make a deep clone.