This function creates a named list of settings which control the execution of an Experiment.

• store_model: If FALSE, the model returned by the learner is discarded in order to save some memory after the experiment is completed. Note that you will be unable to further predict on new data.

• store_prediction: If FALSE, the predictions are discarded in order to save some memory after the experiment is completed.

• encapsulate_train: How to call external code in third party packages during train.

• If set to "none" (default), the code is executed in the running session without error handling. Output is not stored, just send to the console.

• If set to "evaluate", the exceptions are caught using evaluate::evaluate(). All output is stored in a Log of the corresponding Experiment. evaluate does not start a separate session, and thus cannot guard you against segfaults.

• If set to "callr", the code is executed in an independent R session using the callr package. All output is stored in a Log of the corresponding Experiment. This guards your session from segfaults, at the cost of some computational overhead. See Log for an example.

• encapsulate_predict: How to call external code in third party packages during predict. Same format as encapsulate_train. See Log for an example.

Defaults

$store_model FALSE$store_prediction
TRUE

$encapsulate_train "none"$encapsulate_predict
"none"

$log_threshold 400  mlr_control(...) ## Arguments ... :: any Named arguments to overwrite the defaults / options. Settings may be provided in a name = value fashion, or by providing a single named list(). ## Value (named list()) of all settings. ## Examples # get a list of the defaults mlr_control()#>$store_model
#> [1] FALSE
#>
#> $store_prediction #> [1] TRUE #> #>$encapsulate_train
#> [1] "none"
#>
#> $encapsulate_predict #> [1] "none" #> #>$log_threshold
#> [1] 300
#>
# get a control object, with the default of store_prediction changed to FALSE
mlr_control(store_prediction = FALSE)#> $store_model #> [1] FALSE #> #>$store_prediction
#> [1] FALSE
#>
#> $encapsulate_train #> [1] "none" #> #>$encapsulate_predict
#> [1] "none"
#>
#> \$log_threshold
#> [1] 300
#>