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Function to tune a mlr3::Learner. The function internally creates a TuningInstanceBatchSingleCrit or TuningInstanceBatchMultiCrit which describes the tuning problem. It executes the tuning with the Tuner (tuner) and returns the result with the tuning instance ($result). The ArchiveBatchTuning and ArchiveAsyncTuning ($archive) stores all evaluated hyperparameter configurations and performance scores.

You can find an overview of all tuners on our website.


  measures = NULL,
  term_evals = NULL,
  term_time = NULL,
  terminator = NULL,
  search_space = NULL,
  store_benchmark_result = TRUE,
  store_models = FALSE,
  check_values = FALSE,
  callbacks = NULL,
  rush = NULL



Optimization algorithm.


Task to operate on.


Learner to tune.


Resampling that is used to evaluate 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.


(mlr3::Measure or list of mlr3::Measure)
A single measure creates a TuningInstanceBatchSingleCrit and multiple measures a TuningInstanceBatchMultiCrit. If NULL, default measure is used.


Number of allowed evaluations. Ignored if terminator is passed.


Maximum allowed time in seconds. Ignored if terminator is passed.


Stop criterion of the tuning process.


Hyperparameter search space. If NULL (default), the search space is constructed from the TuneToken of the learner's parameter set (learner$param_set).


If TRUE (default), store resample result of evaluated hyperparameter configurations in archive as mlr3::BenchmarkResult.


If TRUE, fitted models are stored in the benchmark result (archive$benchmark_result). If 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.


If TRUE, hyperparameter values are checked before evaluation and performance scores after. If FALSE (default), values are unchecked but computational overhead is reduced.


(list of Callback)
List of callbacks.


If a rush instance is supplied, the tuning runs without batches.


The mlr3::Task, mlr3::Learner, mlr3::Resampling, mlr3::Measure and Terminator are used to construct a TuningInstanceBatchSingleCrit. If multiple performance Measures are supplied, a TuningInstanceBatchMultiCrit is created. The parameter term_evals and term_time are shortcuts to create a Terminator. If both parameters are passed, a TerminatorCombo is constructed. For other Terminators, pass one with terminator. If no termination criterion is needed, set term_evals, term_time and terminator to NULL. The search space is created from paradox::TuneToken or is supplied by search_space.


There are several sections about hyperparameter optimization in the mlr3book.

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

Default Measures

If no measure is passed, the default measure is used. The default measure depends on the task type.

TaskDefault MeasurePackage


For analyzing the tuning results, it is recommended to pass the ArchiveBatchTuning to The returned data table is joined with the benchmark result which adds the mlr3::ResampleResult for each hyperparameter evaluation.

The archive provides various getters (e.g. $learners()) to ease the access. All getters extract by position (i) or unique hash (uhash). For a complete list of all getters see the methods section.

The benchmark result ($benchmark_result) allows to score the hyperparameter configurations again on a different measure. Alternatively, measures can be supplied to

The mlr3viz package provides visualizations for tuning results.


# Hyperparameter optimization on the Palmer Penguins data set
task = tsk("pima")

# Load learner and set search space
learner = lrn("classif.rpart",
  cp = to_tune(1e-04, 1e-1, logscale = TRUE)

# Run tuning
instance = tune(
  tuner = tnr("random_search", batch_size = 2),
  task = tsk("pima"),
  learner = learner,
  resampling = rsmp ("holdout"),
  measures = msr("classif.ce"),
  terminator = trm("evals", n_evals = 4)

# Set optimal hyperparameter configuration to learner
learner$param_set$values = instance$result_learner_param_vals

# Train the learner on the full data set

# Inspect all evaluated configurations$archive)
#>           cp classif.ce  x_domain_cp runtime_learners           timestamp
#>        <num>      <num>        <num>            <num>              <POSc>
#> 1: -8.184490  0.2851562 0.0002789467            0.018 2024-05-14 10:21:25
#> 2: -5.825756  0.2734375 0.0029505731            0.015 2024-05-14 10:21:25
#> 3: -6.855103  0.2851562 0.0010540635            0.013 2024-05-14 10:21:25
#> 4: -4.647497  0.2265625 0.0095855692            0.013 2024-05-14 10:21:25
#>    batch_nr warnings errors  resample_result
#>       <int>    <int>  <int>           <list>
#> 1:        1        0      0 <ResampleResult>
#> 2:        1        0      0 <ResampleResult>
#> 3:        2        0      0 <ResampleResult>
#> 4:        2        0      0 <ResampleResult>