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".

mlr3 0.4.0

• Added new task generators ("cassini", "circle", "simplex", "spirals",
  and "moons").
• Columns in tasks generated by task generators have been renamed to be more
  consistent.
• Added a plot() method for most task generators.
• Corrected data in task german_credit (#514).

mlr3 0.3.0

• Package future.apply is now imported (instead of suggested).
  This is necessary to ensure reproducibility: This way exactly the same result
  is calculated, independent of the parallel backend.
• Fixed a bug where prediction on new data for a task with blocking information
  raised an exception (#496).
• New binding: Task$order.

mlr3 0.1.8

• Added S3 methods to combine ResampleResults and BenchmarkResults with
  c().
• Fixed a bug where automatic generation of row ids could lead to duplicated ids
  via Task$predict_newdata()/Task$rbind() (#423).