Environment which stores various information to allow objects to examine and introspect their structure and properties (c.f. Reflections).

This environment be modified by third-party packages, e.g. by adding information about new task types or by extending the set of allowed feature types.

The following objects are set by mlr3:

  • data_formats :: character()
    Vectors of supported data formats, e.g. "data.table" or "Matrix".

  • task_types :: data.table()
    Table with task type ("type"), the implementing package ("pkg"), and the names of the generators of the corresponding Task ("task"), Learner ("learner"), Prediction ("prediction") and Measure ("measure").

  • task_feature_types :: named character()
    Vector of base R types supported as Task features, named with a 3 letter abbreviation.

  • task_row_roles :: character()
    Vector of supported row roles for a Task.

  • task_col_roles :: list of character()
    List of vectors of supported column roles for a Task, named by their task type.

  • task_properties :: list of character()
    List of vectors of supported Task properties, named by their task type.

  • learner_properties :: list of character()
    List of vectors of supported Learner properties, named by their task type.

  • learner_predict_types :: list of list of character()
    List of lists of supported Learner predict_types, named by their task type. The inner list translates the "predict_type" to all predict types returned, e.g. predict type "prob" for a LearnerClassif provides the probabilities as well as the predicted labels, therefore "prob" maps to c("response", "prob").

  • learner_predict_types :: list of list of character()
    List of lists of supported Learner predict_types, named by their task type.

  • measure_properties :: list of character()
    List of vectors of supported Measure properties, named by their task type.

  • default_measures :: list of character()
    List of keys for the default Measures, named by their task type.

  • rr_names :: character()
    Names of the elements of a ResampleResult.

mlr_reflections

Format

environment.

Examples

ls.str(mlr_reflections)
#> data_formats : chr [1:2] "data.table" "Matrix" #> default_measures : List of 2 #> $ classif: chr "classif.ce" #> $ regr : chr "regr.mse" #> learner_predict_types : List of 2 #> $ classif:List of 2 #> $ regr :List of 2 #> learner_properties : List of 2 #> $ classif: chr [1:8] "missings" "weights" "parallel" "importance" ... #> $ regr : chr [1:6] "missings" "weights" "parallel" "importance" ... #> measure_properties : List of 2 #> $ classif: chr [1:3] "requires_task" "requires_learner" "requires_train_set" #> $ regr : chr [1:3] "requires_task" "requires_learner" "requires_train_set" #> rr_names : chr [1:5] "task" "learner" "resampling" "iteration" "prediction" #> task_col_roles : List of 2 #> $ regr : chr [1:6] "feature" "target" "label" "order" ... #> $ classif: chr [1:6] "feature" "target" "label" "order" ... #> task_feature_types : Named chr [1:7] "logical" "integer" "numeric" "character" "factor" ... #> task_properties : List of 2 #> $ classif: chr [1:4] "weights" "groups" "twoclass" "multiclass" #> $ regr : chr [1:2] "weights" "groups" #> task_row_roles : chr [1:2] "use" "validation" #> task_types : Classes ‘data.table’ and 'data.frame': 2 obs. of 6 variables: #> $ type : chr "classif" "regr" #> $ package : chr "mlr3" "mlr3" #> $ task : chr "TaskClassif" "TaskRegr" #> $ learner : chr "LearnerClassif" "LearnerRegr" #> $ prediction: chr "PredictionClassif" "PredictionRegr" #> $ measure : chr "MeasureClassif" "MeasureRegr"