• We have published an article about mlr3 in the Journal of Open Source
  Software: https://joss.theoj.org/papers/10.21105/joss.01903.
  See citation("mlr3") for the citation info.
• New method Learner$reset().
• New method BenchmarkResult$filter().
• Learners returned by BenchmarkResult$learners are reset to encourage the
  safer alternative BenchmarkResult$score() to access trained models.
• Fix ordering of levels in PredictionClassif$set_threshold() (triggered an
  assertion).

• Switched from package Metrics to package mlr3measures.
• Measures can now calculate all scores using micro or macro averaging (#400).
• Measures can now be configured to return a customizable performance score
  (instead of NA) in case the score cannot be calculated.
• Character columns are now treated differently from factor columns.
  In the long term, character() columns are supposed to store text.
• Fixed a bug triggered by integer grouping variables in Task (#396).
• benchmark_grid() now accepts instantiated resamplings under certain
  conditions.

• Task$set_col_roles() and Task$set_row_roles() are now deprecated.
  Instead it is recommended for now to work with the lists Task$col_roles and
  Task$row_roles directly.
• Learner$predict_newdata() now works without argument task if the learner
  has been fitted with Learner$train() (#375).
• Names of column roles have been unified ("weights", "label",
  "stratify" and "groups" have been renamed).
• Replaced MeasureClassifF1 with MeasureClassifFScore and fixed a bug in the
  F1 performance calculation (#353). Thanks to @001ben for reporting.
• Stratification is now controlled via a task column role (was a parameter of
  class Resampling before).
• Added a S3 predict() method for class Learner to increase
  interoperability with other packages.
• Many objects now come with a $help() which opens the respective manual page.

• It is now possible to predict and score results on the training set or on both
  training and test set.
  Learners can be instructed to predict on multiple sets by setting
  predict_sets (default: "test"). Measures operate on all sets specified in
  their field predict_sets (default: "test".

• ResampleResult$prediction and ResampleResult$predictions() are now methods
  instead of fields, and allow to extract predictions for different predict
  sets.

• ResampleResult$performance() has been renamed to ResampleResult$score()
  for consistency.

• BenchmarkResult$performance() has been renamed to BenchmarkResult$score()
  for consistency.

• Changed API for (internal) constructors accepting paradox::ParamSet().
  Instead of passing the initial values separately, the initial values must now
  be set directly in the ParamSet.

• Deprecated support of automatically creating objects from strings.
  Instead, mlr3 provides the following helper functions intended to ease the
  creation of objects stored in dictionaries:
  tsk(), tgen(), lrn(), rsmp(), msr().

• BenchmarkResult now ensures that the stored ResampleResults are in a
  persistent order. Thus, ResampleResults can now be addressed by their
  position instead of their hash.

• New field BenchmarkResult$n_resample_results.

• New field BenchmarkResult$hashes.

• New method Task$rename().

• New S3 generic as_benchmark_result().

• Renamed Generator to TaskGenerator.

• Removed the control object mlr_control().

• Removed ResampleResult$combine().

• Removed BenchmarkResult$best().

Initial upload to CRAN.