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.

mlr3 0.9.0

• New methods for Task: $set_col_roles and $set_row_roles as a replacement
  for the deprecated and less flexible $set_col_role and $set_row_role.
• Learners can now have a timeout (#556).
• Removed S3 method friedman.test.BenchmarkResult() in favor of the new
  mlr3benchmark package.

mlr3 0.8.0

• MeasureOOBError now has set property minimize to TRUE.
• New learner property "featureless" to tag learners which can operate on
  featureless tasks.
• Fixed [ResampleResult] ignoring argument predict_sets for returned
  [Prediction] objects.
• Compability with new version of lgr.

mlr3 0.7.0

• Updated properties of featureless learners to apply it on all feature types
  (did not work on POSIXct columns).
• Fixed measures being calculated as NaN for BenchmarkResult for resamplings
  with a single iteration (#551).
• Fixed a bug where a broken heuristic disabled nested parallelization via
  package future (mlr3tuning#270).
• ResampleResult and BenchmarkResult now share a common interface to store
  the experiment results. Manual construction is still possible with helper
  function as_result_data()
• Fixed deep cloning of ResamplingCV and ResamplingRepeatedCV.
• New measure classif.prauc (area under precision-recall curve).
• Removed dependency on orphaned package bibtex.

mlr3 0.6.0

• Compact in-memory representation of R6 objects to save space when
  saving objects via saveRDS() or serialize().
• Objects in containers like ResampleResult or BenchmarkResult are now
  de-duplicated for an optimized serialization.
• Fixed data set breast_cancer: all factor features are now
  correctly stored as ordered factors.
• Added a new utility function convert_task().

mlr3 0.5.0

• Added classification task breast_cancer
• Added ResamplingLOO for leave-one-out resampling.
• Regression now supports predict type "distr" using the distr6 package.
• Fixed ResamplingBootstrap in combination with grouping (#514).
• Fixed plot method of TaskGeneratorMoons.
• Added hyperparameter keep_model to learners "classif.rpart" and
  "regr.rpart".