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The TunerBatch implements the optimization algorithm.


TunerBatch is an abstract base class that implements the base functionality each tuner must provide. A subclass is implemented in the following way:

  • Inherit from Tuner.

  • Specify the private abstract method $.optimize() and use it to call into your optimizer.

  • You need to call instance$eval_batch() to evaluate design points.

  • The batch evaluation is requested at the TuningInstanceBatchSingleCrit/TuningInstanceBatchMultiCrit object instance, so each batch is possibly executed in parallel via mlr3::benchmark(), and all evaluations are stored inside of instance$archive.

  • Before the batch evaluation, the bbotk::Terminator is checked, and if it is positive, an exception of class "terminated_error" is generated. In the later case the current batch of evaluations is still stored in instance, but the numeric scores are not sent back to the handling optimizer as it has lost execution control.

  • After such an exception was caught we select the best configuration from instance$archive and return it.

  • Note that therefore more points than specified by the bbotk::Terminator may be evaluated, as the Terminator is only checked before a batch evaluation, and not in-between evaluation in a batch. How many more depends on the setting of the batch size.

  • Overwrite the private super-method .assign_result() if you want to decide yourself how to estimate the final configuration in the instance and its estimated performance. The default behavior is: We pick the best resample-experiment, regarding the given measure, then assign its configuration and aggregated performance to the instance.

Private Methods

  • .optimize(instance) -> NULL
    Abstract base method. Implement to specify tuning of your subclass. See details sections.

  • .assign_result(instance) -> NULL
    Abstract base method. Implement to specify how the final configuration is selected. See details sections.


There are several sections about hyperparameter optimization in the mlr3book.

  • An overview of all tuners can be found on our website.

  • Learn more about tuners.

The gallery features a collection of case studies and demos about optimization.

  • Use the Hyperband optimizer with different budget parameters.

Super class

mlr3tuning::Tuner -> TunerBatch


Inherited methods

Method new()

Creates a new instance of this R6 class.


  id = "tuner_batch",
  packages = character(),
  label = NA_character_,
  man = NA_character_



Identifier for the new instance.


Set of control parameters.


Supported parameter classes for learner hyperparameters that the tuner can optimize, as given in the paradox::ParamSet $class field.


Set of properties of the tuner. Must be a subset of mlr_reflections$tuner_properties.


Set of required packages. Note that these packages will be loaded via requireNamespace(), and are not attached.


Label for this object. Can be used in tables, plot and text output instead of the ID.


String in the format [pkg]::[topic] pointing to a manual page for this object. The referenced help package can be opened via method $help().

Method optimize()

Performs the tuning on a TuningInstanceBatchSingleCrit or TuningInstanceBatchMultiCrit until termination. The single evaluations will be written into the ArchiveBatchTuning that resides in the TuningInstanceBatchSingleCrit/TuningInstanceBatchMultiCrit. The result will be written into the instance object.



Method clone()

The objects of this class are cloneable with this method.


TunerBatch$clone(deep = FALSE)



Whether to make a deep clone.