This is the abstract base class for learner objects like LearnerClassif and LearnerRegr. Predefined learners are stored in mlr_learners.

Format

R6::R6Class object.

Construction

l = Learner$new(id, task_type, param_set = ParamSet$new(), param_vals = list(), predict_types = character(),
     feature_types = character(), properties = character(), packages = character())
  • id :: character(1)
    Identifier for the learner.

  • task_type :: character(1)
    Type of the task the learner can operator on. E.g., "classif" or "regr".

  • param_set :: paradox::ParamSet
    Set of hyperparameters.

  • param_vals :: named list()
    List of hyperparameter settings.

  • predict_types :: character()
    Supported predict types. Must be a subset of mlr_reflections$learner_predict_types.

  • feature_types :: character()
    Feature types the learner operates on. Must be a subset of mlr_reflections$task_feature_types.

  • properties :: character()
    Set of properties of the learner. Must be a subset of mlr_reflections$learner_properties.

  • data_formats :: character()
    Vector of supported data formats which can be processed during $train() and $predict(). Will be matched against the data formats supported by the Task, and the first data format specified in the learner which is also supported by the task will be picked. Defaults to "data.table".

  • packages :: character()
    Set of required packages. Note that these packages will be loaded via requireNamespace(), and are not attached.

Fields

  • fallback :: (Learner | NULL)
    Optionally stores a second Learner which is activated as fallback if this first Learner fails during train or predict. This mechanism is disabled unless you explicitly assign a learner to this slot. Additionally, you need to catch raised exceptions via encapsulation, see mlr_control().

  • feature_types :: character()
    Stores the feature types the learner can handle, e.g. "logical", "numeric", or "factor". A complete list of candidate feature types, grouped by task type, is stored in mlr_reflections$task_feature_types.

  • hash :: character(1)
    Hash (unique identifier) for this object.

  • id :: character(1)
    Stores the identifier of the learner.

  • packages :: character()
    Stores the names of required packages.

  • param_set :: paradox::ParamSet
    Description of available hyperparameters and hyperparameter settings.

  • predict_type :: character(1)
    Stores the currently selected predict type. Must be an element of l$predict_types.

  • predict_types :: character()
    Stores the possible predict types the learner is capable of. A complete list of candidate predict types, grouped by task type, is stored in mlr_reflections$learner_predict_types.

  • properties :: character()
    Stores a set of properties/capabilities the learner has. A complete list of candidate properties, grouped by task type, is stored in mlr_reflections$learner_properties.

  • task_type :: character(1)
    Stores the type of class this learner can operate on, e.g. "classif" or "regr". A complete list of task types is stored in mlr_reflections$task_types.

Methods

  • params(tag)
    character(1) -> named list()
    Returns a list of hyperparameter settings from param_set where the corresponding parameters in param_set are tagged with tag. I.e., l$params("train") returns all settings of hyperparameters used in the training step.

  • train(task)
    Task -> self
    Train the learner on the complete Task. The resulting model is stored in l$model.

  • predict(task)
    Task -> Prediction
    Uses l$model (fitted during train()) to return a Prediction object.

Optional Extractors

Specific learner implementations are free to implement additional getters to ease the access of certain parts of the model in the inherited subclasses.

For the following operations, extractors are standardized:

  • importance(...): Returns a feature importance score as numeric(). The learner must be tagged with property "importance".

    The higher the score, the more important the variable. The returned vector is named with feature names and sorted in decreasing order. Note that the model might omit features it has not used at all.

  • selected_features(...): Returns a subset of selected features as character(). The learner must be tagged with property "selected_features".

See also