mlr3 0.12.0

• New method to assign labels to columns in tasks: Task$label().
  These will be used in visualizations in the future.
• New method to add stratification variables: Task$add_strata().
• New helper function partition() to split a task into a training and test
  set.
• New standardized getter loglik() for class Learner.
• New measures "aic" and "bic" to compute the Akaike Information Criterion
  or the Bayesian Information Criterion, respectively.
• New Resampling method: ResamplingCustomCV. Creates a custom resampling split
  based on the levels of a user-provided factor variable.
• New argument encapsulate for resample() and benchmark() to conveniently
  enable encapsulation and also set the fallback learner to the
  featureless learner. This is simply for convenience, configuring each learner
  individually is still possible and allows a more fine-grained control (#634,
  #642).
• New field parallel_predict for Learner to enable parallel predictions via
  the future backend. This currently is only enabled while calling the
  $predict() or $predict_newdata methods and is disabled during resample()
  and benchmark() where you have other means to parallelize.
• Deprecated public (and already documented as internal) field $data in
  ResampleResult and BenchmarkResult to simplify the API and avoid
  confusion. The converter as.data.table() can be used instead to access the
  internal data.
• Measures now have formal hyperparameters. A popular example where this is
  required is the F1 score, now implemented with customizable beta.
• Changed default of argument ordered in Task$data() from TRUE to FALSE.
• Fixed getter ResamplingRepeatedCV$folds() (#643).
• Fixed hashing of some measures.
• Removed experimental column role uri. This role be split up into multiple
  roles by the mlr3keras package.