mlr3 0.16.1

• Function data.table() is now re-exported.
• Fixed a test which randomly failed.
• Improved documentation.
• Add encapsulation mode "try", which works similar to "none" but captures errors

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 %.