mlr3 1.1.0

• feat: Add new measure MeasureRegrRQR for quantile regression.
• feat: Add $predict_newdata_fast() method to Learner to speed up prediction.
• fix: configure_learner is passed on run_experiment() for autotest learners.

mlr3 1.0.1

• fix: The printer of Learner failed when the validate field was set.
• fix: Avoid printing empty line for feature less tasks.
• perf: Use data.table::setattr() for less copying.

mlr3 1.0.0

• BREAKING CHANGE: The mlr3 ecosystem has a base logger now which is named mlr3.
  The mlr3/core logger is a child of the mlr3 logger and is used for logging messages from the mlr3 package.
  Some extension packages have their own loggers which are children of the mlr3 logger e.g. mlr3/mlr3pipelines and mlr3/bbotk for tuning.

• BREAKING CHANGE: weights property and functionality is split into weights_learner and weights_measure:

  • weights_learner: Weights used during training by the Learner.
  • weights_measure: Weights used during scoring predictions via measures.

  Each of these can be disabled via the new field use_weights in Learner and Measure objects.

• feat: Add $confusion_weighted field to PredictionClassif.

• feat: Add $weights field to Prediction. It contains the weights_measure weights from the Task that was used for prediction.

• feat: Add "macro_weighted" option to Measure$average field.

• feat: MeasureRegrRSQ and MeasureClassifCost gain "weights" property.

• feat: LearnerClassifFeatureless, LearnerRegrFeatureless, LearnerClassifDebug, LearnerRegrDebug gain "weights" property.

• feat: Learner printer now prints information about encapsulation and weights use.

• feat: Add score_roc_measures() to score a prediction on various roc measures.

• feat: A better error message is thrown, which often happens when incorrectly configuring the validate field
  of a GraphLearner

• feat: Added method $set_threshold() to BenchmarkResult and ResamplingResult, which allows to set the threshold for the response prediction of classification learners, given they have output a probability prediction (#1270).

• feat: Added field $uhash_table to BenchmarkResult and functions uhash() and uhashes()
  to easily compute uhashes for given learner, task, or resampling ids (#1270).

• feat: You can now change the default predict type of classification learners to "prob" by setting
  the option mlr3.prob_as_default to TRUE (#1273).

• feat: benchmark_grid() will now throw a warning if you mix different predict types in the
  design (#1273).

• feat: Converting a BenchmarkResult to a data.table now includes the task_id, learner_id, and resampling_id columns (#1275).

• fix: Add missing parameters for "regr.pinball" and "sim.phi" measures.

mlr3 0.23.0

• feat: Add new col_role offset in Task and offset Learner property.
  A warning is produced if a learner that doesn't support offsets is trained with a task that has an offset column.
• fix: The $predict_newdata() method of Learner now automatically conducts type conversions (#685).
• BREAKING_CHANGE: Predicting on a Task with the wrong column information is now an error and not a warning.
• Column names with UTF-8 characters are now allowed by default.
  The option mlr3.allow_utf8_names is removed.
• BREAKING CHANGE: Learner$predict_types is read-only now.
• docs: Clear up behavior of Learner$predict_type after training.
• feat: Add callbacks to resample() and benchmark().
• fix: Internal tuning and validation now works when the model requires marshaling (#1256).

mlr3 0.22.1

• fix: Extend assert_measure() with checks for trained models in assert_scorable().

mlr3 0.22.0

• fix: Quantiles must not ascend with probabilities.
• refactor: Replace tsk("boston_housing") with tsk("california_housing").
• feat: Require unique learner ids in benchmark_grid().
• BREAKING CHANGE: Remove $loglik() method from all learners.
• fix: Ignore future.globals.maxSize when future::plan("sequential") is used.
• feat: Add $characteristics field to Task to store additional information.

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").