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Runs a resampling (possibly in parallel): Repeatedly apply Learner learner on a training set of Task task to train a model, then use the trained model to predict observations of a test set. Training and test sets are defined by the Resampling resampling.


  store_models = FALSE,
  store_backends = TRUE,
  encapsulate = NA_character_,
  allow_hotstart = FALSE









Store the fitted model in the resulting object= Set to TRUE if you want to further analyse the models or want to extract information like variable importance.


Keep the DataBackend of the Task in the ResampleResult? Set to TRUE if your performance measures require a Task, or to analyse results more conveniently. Set to FALSE to reduce the file size and memory footprint after serialization. The current default is TRUE, but this eventually will be changed in a future release.


If not NA, enables encapsulation by setting the field Learner$encapsulate to one of the supported values: "none" (disable encapsulation), "evaluate" (execute via evaluate) and "callr" (start in external session via callr). If NA, encapsulation is not changed, i.e. the settings of the individual learner are active. Additionally, if encapsulation is set to "evaluate" or "callr", the fallback learner is set to the featureless learner if the learner does not already have a fallback configured.


Determines if learner(s) are hot started with trained models in $hotstart_stack. See also HotstartStack.


The fitted models are discarded after the predictions have been computed in order to reduce memory consumption. If you need access to the models for later analysis, set store_models to TRUE.

Predict Sets

If you want to compare the performance of a learner on the training with the performance on the test set, you have to configure the Learner to predict on multiple sets by setting the field predict_sets to c("train", "test") (default is "test"). Each set yields a separate Prediction object during resampling. In the next step, you have to configure the measures to operate on the respective Prediction object:

m1 = msr("classif.ce", id = "ce.train", predict_sets = "train")
m2 = msr("classif.ce", id = "ce.test", predict_sets = "test")

The (list of) created measures can finally be passed to $aggregate() or $score().


This function can be parallelized with the future package. One job is one resampling iteration, and all jobs are send to an apply function from future.apply in a single batch. To select a parallel backend, use future::plan().

Progress Bars

This function supports progress bars via the package progressr. Simply wrap the function call in progressr::with_progress() to enable them. Alternatively, call progressr::handlers() with global = TRUE to enable progress bars globally. We recommend the progress package as backend which can be enabled with progressr::handlers("progress").


The mlr3 uses the lgr package for logging. lgr supports multiple log levels which can be queried with getOption("lgr.log_levels").

To suppress output and reduce verbosity, you can lower the log from the default level "info" to "warn":


To get additional log output for debugging, increase the log level to "debug" or "trace":


To log to a file or a data base, see the documentation of lgr::lgr-package.

See also

Other resample: ResampleResult


task = tsk("penguins")
learner = lrn("classif.rpart")
resampling = rsmp("cv")

# Explicitly instantiate the resampling for this task for reproduciblity

rr = resample(task, learner, resampling)
#> <ResampleResult> of 10 iterations
#> * Task: penguins
#> * Learner: classif.rpart
#> * Warnings: 0 in 0 iterations
#> * Errors: 0 in 0 iterations

# Retrieve performance
#>                  task  task_id                   learner    learner_id
#>  1: <TaskClassif[49]> penguins <LearnerClassifRpart[37]> classif.rpart
#>  2: <TaskClassif[49]> penguins <LearnerClassifRpart[37]> classif.rpart
#>  3: <TaskClassif[49]> penguins <LearnerClassifRpart[37]> classif.rpart
#>  4: <TaskClassif[49]> penguins <LearnerClassifRpart[37]> classif.rpart
#>  5: <TaskClassif[49]> penguins <LearnerClassifRpart[37]> classif.rpart
#>  6: <TaskClassif[49]> penguins <LearnerClassifRpart[37]> classif.rpart
#>  7: <TaskClassif[49]> penguins <LearnerClassifRpart[37]> classif.rpart
#>  8: <TaskClassif[49]> penguins <LearnerClassifRpart[37]> classif.rpart
#>  9: <TaskClassif[49]> penguins <LearnerClassifRpart[37]> classif.rpart
#> 10: <TaskClassif[49]> penguins <LearnerClassifRpart[37]> classif.rpart
#>             resampling resampling_id iteration              prediction
#>  1: <ResamplingCV[19]>            cv         1 <PredictionClassif[20]>
#>  2: <ResamplingCV[19]>            cv         2 <PredictionClassif[20]>
#>  3: <ResamplingCV[19]>            cv         3 <PredictionClassif[20]>
#>  4: <ResamplingCV[19]>            cv         4 <PredictionClassif[20]>
#>  5: <ResamplingCV[19]>            cv         5 <PredictionClassif[20]>
#>  6: <ResamplingCV[19]>            cv         6 <PredictionClassif[20]>
#>  7: <ResamplingCV[19]>            cv         7 <PredictionClassif[20]>
#>  8: <ResamplingCV[19]>            cv         8 <PredictionClassif[20]>
#>  9: <ResamplingCV[19]>            cv         9 <PredictionClassif[20]>
#> 10: <ResamplingCV[19]>            cv        10 <PredictionClassif[20]>
#>     classif.ce
#>  1: 0.00000000
#>  2: 0.00000000
#>  3: 0.02857143
#>  4: 0.00000000
#>  5: 0.17647059
#>  6: 0.05882353
#>  7: 0.05882353
#>  8: 0.02941176
#>  9: 0.11764706
#> 10: 0.05882353
#> classif.ce 
#> 0.05285714 

# merged prediction objects of all resampling iterations
pred = rr$prediction()
#>            truth
#> response    Adelie Chinstrap Gentoo
#>   Adelie       145         6      0
#>   Chinstrap      7        59      2
#>   Gentoo         0         3    122

# Repeat resampling with featureless learner
rr_featureless = resample(task, lrn("classif.featureless"), resampling)

# Convert results to BenchmarkResult, then combine them
bmr1 = as_benchmark_result(rr)
bmr2 = as_benchmark_result(rr_featureless)
#> <BenchmarkResult> of 20 rows with 2 resampling runs
#>  nr  task_id          learner_id resampling_id iters warnings errors
#>   1 penguins       classif.rpart            cv    10        0      0
#>   2 penguins classif.featureless            cv    10        0      0