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).
learnerresamplingmeasures(list of mlr3::Measure).
store_models(logical(1)).
store_benchmark_result(logical(1)).
archivenew()Creates a new instance of this R6 class.
ObjectiveTuning$new( task, learner, resampling, measures, check_values = TRUE, store_benchmark_result = TRUE, store_models = FALSE )
task(mlr3::Task) Task to operate on.
learnerresampling(mlr3::Resampling) Resampling that is used to evaluated the performance of the hyperparameter configurations. Uninstantiated resamplings are instantiated during construction so that all configurations are evaluated on the same data splits. Already instantiated resamplings are kept unchanged. Specialized Tuner change the resampling e.g. to evaluate a hyperparameter configuration on different data splits. This field, however, always returns the resampling passed in construction.
measures(list of mlr3::Measure)
Measures to optimize.
If NULL, mlr3's default measure is used.
check_values(logical(1))
Should parameters before the evaluation and the results be checked for
validity?
store_benchmark_result(logical(1))
If TRUE (default), stores the mlr3::BenchmarkResult in archive.
store_models(logical(1))
If FALSE (default), the fitted models are not stored in the
mlr3::BenchmarkResult. If store_benchmark_result = FALSE, the models are
only stored temporarily and not accessible after the tuning. This combination
might be useful for measures that require a model.
clone()The objects of this class are cloneable with this method.
ObjectiveTuning$clone(deep = FALSE)
deepWhether to make a deep clone.