Creates a new instance of this R6 class.
ObjectiveTuning$new( task, learner, resampling, measures, store_benchmark_result = TRUE, store_models = FALSE, check_values = TRUE, allow_hotstart = FALSE, archive = NULL )
Task to operate on.
Learner to tune.
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.
(list of mlr3::Measure)
Measures to optimize.
TRUE(default), store resample result of evaluated hyperparameter configurations in archive as mlr3::BenchmarkResult.
TRUE, fitted models are stored in the benchmark result (
store_benchmark_result = FALSE, models are only stored temporarily and not accessible after the tuning. This combination is needed for measures that require a model.
TRUE, hyperparameter values are checked before evaluation and performance scores after. If
FALSE(default), values are unchecked but computational overhead is reduced.
Allow to hotstart learners with previously fitted models. See also mlr3::HotstartStack. The learner must support hotstarting. Sets
store_models = TRUE.