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.

mlr3 0.11.0

• Added a as.data.table.Resampling method.
• Renamed column "row_id" to "row_ids" in the as.data.table() methods
  for PredictionClassif and PredictionRegr (#547).
• Added converters as_prediction_classif() and as_prediction_regr() to
  reverse the operation of as.data.table.PredictionClassif() and
  as.data.table.PredictionRegr().
• Specifying a weight column during learner$predict_newdata() is not mandatory
  anymore (#563).
• Task$data() defaults to return only active rows and columns, instead of
  asserting to only return rows and columns. As a result, the $data() method
  can now also be used to query inactive rows and cols from the DataBackend.
• New (experimental) column role uri which is intended to point to external
  resources, e.g. images on the file system.
• New helper set_threads() to control the number of threads during calls to
  external packages. All objects will be migrated to have threading disabled in
  their defaults to avoid conflicting parallelization techniques (#605).
• New option mlr3.debug: avoid calls to future in resample() and
  benchmark() to improve the readability of tracebacks.
• New experimental option mlr3.allow_utf8_names: allow non-ascii characters in
  column names in tasks.

mlr3 0.10.0

• Result containers ResampleResult and BenchmarkResult now optionally remove
  the DataBackend of the Tasks in order to reduce file size and memory
  footprint after serialization. To remove the backends from the containers,
  set store_backends to FALSE in resample() or benchmark(),
  respectively. Note that this behaviour will eventually will be the default for
  future releases.
• Prediction objects generated by Learner$predict_newdata() now have row ids
  starting from 1 instead auto incrementing row ids of the training task.
• as.data.table.DictionaryTasks now returns an additional column properties.
• Added flag conditions to ResampleResult$score() and
  BenchmarkResult$score() to allow to work with failing learners more
  conveniently.