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This mlr3misc::Callback scores the hyperparameter configurations on additional measures while tuning. Usually, the configurations can be scored on additional measures after tuning (see ArchiveBatchTuning). However, if the memory is not sufficient to store the mlr3::BenchmarkResult, it is necessary to score the additional measures while tuning. The measures are not taken into account by the tuner.


#> <CallbackBatchTuning:mlr3tuning.measures>: Additional Measures Callback
#> * Active Stages: on_eval_before_archive, on_optimization_begin

# additionally score the configurations on the accuracy measure
instance = tune(
  tuner = tnr("random_search", batch_size = 2),
  task = tsk("pima"),
  learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE)),
  resampling = rsmp("cv", folds = 3),
  measures = msr("classif.ce"),
  term_evals = 4,
  callbacks = clbk("mlr3tuning.measures", measures = msr("classif.acc"))