mlr3 0.16.0

• Added argument paired to benchmark_grid() function, which can be used to create a benchmark design, where
  resamplings have been instantiated on tasks.
• Added S3 method for ResultData for as_resample_result() converter.
• Added S3 method for list for as_resample_result() converter.
• The featureless classification learner now returns proper probabilities
  (#918).

mlr3 0.15.0

• Many returned tables are now assigned a class for a print method to make the output
  more readable.
• Fixed some typos

mlr3 0.14.1

• Removed depdency on package distr6.
• Fixed reassembling of GraphLearner.
• Fixed bug where the measured elapsed time was 0:
  https://stackoverflow.com/questions/73797845/mlr3-benchmarking-with-elapsed-time-measure
• Fixed as_prediction_classif() for data.frame() input (#872).
• Improved the error message when predict type of fallback learner does not
  match the predict type of the learner (mlr-org/mlr3extralearners#241).
• The test set is now available to the Learner during train for early
  stopping.

mlr3 0.14.0

• Added multiclass measures: mauc_aunu, mauc_aunp, mauc_au1u, mauc_au1p.
• Measure classif.costs does not require a Task anymore.
• New converter: as_task_unsupervised()
• Refactored the task types in mlr_reflections.

mlr3 0.13.4

• Added new options for parallelization ("mlr3.exec_random" and
  "mlr3.exec_chunk_size"). These options are passed down to the respective map
  functions in package future.apply.
• Fixed runtime measures depending on specific predict types (#832).
• Added head() and tail() methods for Task.
• Improved printing of multiple objects.

mlr3 0.13.3

• Most objects now have a new (optional) field label, i.e. Task,
  TaskGenerator, Learner, Resampling, and Measure.
• as.data.table() methods for objects of class Dictonary have been extended
  with additional columns.
• as_task_classif.formula() and as_task_regr.formula() now remove additional
  atrributes attached to the data which caused some some learners to break.
• Packages are now loaded prior to calling the $train() and $predict()
  methods of a Learner. This ensures that package loading errors are properly
  propagated and not affected by encapsulation (#771).

mlr3 0.13.2

• Setting a fallback learner for a learner with encapsulation in its default
  settings now automatically sets encapsulation to "evaluate" (#763).
• as_task_classif() and as_task_regr() now support the construction of tasks
  using the formula interface, e.g. as_task_regr(mpg ~ ., data = mtcars)
  (#761).
• The row role "validation" has been renamed to "holdout".
  In the next release, mlr3 will start switching to the now more common terms
  "train"/"validation" instead of "train"/"test" for the sets created
  during resampling.

mlr3 0.13.1

• Improved performance for many operations on ResampleResult and
  BenchmarkResult.
• resample() and benchmark() got a new argument clone to control which
  objects to clone before performing computations.
• Tasks are checked for infinite values during the conversion from data.frame
  to Task in as_task_classif() and as_task_regr(). A warning is signaled
  if any column contains infinite values.

mlr3 0.13.0

• Learners which are capable of resuming/continuing (e.g.,
  learner (classif|regr|surv).xgboost with hyperparameter nrounds updated)
  can now optionally store a stack of trained learners to be used to hotstart
  their training. Note that this feature is still somewhat experimental.
  See HotstartStack and #719.
• New measures to score similarity of selected feature sets:
  sim.jaccard (Jaccard Index) and sim.phi (Phi coefficient) (#690).
• predict_newdata() now also supports DataBackend as input.
• New function install_pkgs() to install required packages. This generic works
  for all objects with a packages field as well as ResampleResult and
  BenchmarkResult (#728).
• New learner regr.debug for debugging.
• New Task method $set_levels() to control how data with factor columns
  is returned, independent of the used DataBackend.
• Measures now return NA if prerequisite are not met (#699).
  This allows to conveniently score your experiments with multiple measures
  having different requirements.
• Feature names may no longer contain the special character %.

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.