mlr3 0.21.1

• feat: Throw warning when prediction and measure type do not match.
• fix: The mlr_reflections were broken when an extension package was not loaded on the workers.
  Extension packages must now register themselves in the mlr_reflections$loaded_packages field.

mlr3 0.21.0

• BREAKING CHANGE: Deprecated data_format and data_formats for Learner, Task, and DataBackend classes.
• feat: The partition() function creates training, test and validation sets now.
• perf: Optimize the runtime of fixing factor levels.
• perf: Optimize the runtime of setting row roles.
• perf: Optimize the runtime of marshalling.
• perf: Optimize the runtime of Task$col_info.
• fix: column info is now checked for compatibility during Learner$predict (#943).
• BREAKING CHANGE: The predict time of the learner now stores the cumulative duration for all predict sets (#992).
• feat: $internal_valid_task can now be set to an integer vector.
• feat: Measures can now have an empty $predict_sets (#1094).
  This is relevant for measures that only extract information from the model of a learner (such as internal validation scores or AIC / BIC)
• BREAKING CHANGE: Deprecated the $divide() method
• fix: Task$cbind() now works with non-standard primary keys for data.frames (#961).
• fix: Triggering of fallback learner now has log-level "info" instead of "debug" (#972).
• feat: Added new measure regr.pinball here and in mlr3measures.
• feat: Added new measure mu_auc here and in mlr3measures.
• feat: Add option to calculate the mean of the true values on the train set in msr("regr.rsq").
• feat: Default fallback learner is set when encapsulation is activated.
• feat: Learners classif.debug and regr.debug have new methods $importance() and $selected_features() for testing, also in downstream packages.
• feat: Create default fallback learner with default_fallback().
• feat: Check column roles when using $set_col_roles() and $col_roles.
• fix: Add predict set to learner hash.
• BREAKING CHANGE: Encapsulation and the fallback learner are now set with the $encapsulate(method, fallback) method.
  The $fallback field is read-only now and the encapsulate status can be retrieved from the $encapsulation field.

mlr3 0.20.2

• refactor: move RhpcBLASctl to suggest.

mlr3 0.20.1

• feat: Add multiclass Matthews correlation coefficient msr("classif.mcc").

mlr3 0.20.0

• Added support for learner-internal validation and tuning.

mlr3 0.19.0

• Added support for "marshal" property, which allows learners to process models so they can be serialized.
  This happens automatically during resample() and benchmark().
• Encapsulation methods use the same RNG state now.
• Fix missing values in default_values.Learner() function.
• Encapsulated error messages are now printed with the lgr package.

mlr3 0.18.0

• Prepare compatibility with new paradox version.
• feat: dictionary conversion of mlr_learners respects prototype arguments
  recently added in mlr3misc
• perf: skip unnecessary clone of learner's state in resample()

mlr3 0.17.2

• Skip new data.table tests on mac.

mlr3 0.17.1

• Remove data_prototype when resampling from learner$state to reduce memory consumption.
• Reduce number of threads used by data.table and BLAS to 1 when running resample() or benchmark() in parallel.
• Optimize runtime of resample() and benchmark() by reducing the number of hashing operations.

mlr3 0.17.0

• Learners cannot be added to the HotstartStack anymore when the model is missing.
• Learners bellow the hotstart_threshold are not added to the HotstartStack anymore.
• The learner$state$train_time in hotstarted learners is now only the time of the last training.
• Added debug messages to the hotstart stack.
• Fixed bug where the HotstartStack did not work with column roles set in the task.
• The design of benchmark() can now include parameter settings.
• Speed up resampling by removing unnecessary calls to packageVersion().
• Fix boston housing data set.
• Export generic function col_info to allow adding new methods for backends.
• Task printer includes row roles now.
• Add "mlr3.exec_chunk_bins" option to split the resampling iterations into a number of bins.