LearnerMultioutput: Multioutput Learner

In mlr-org/mlr3multioutput: Multioutput extension for 'mlr3'

Description

This Learner specializes mlr3::Learner for Multioutputer problems:

• task_type is set to "Multioutput".

• Creates Predictions of class PredictionMultioutput.

• Possible values for predict_types are all predict_types available for other supervised Tasks.

Predefined learners can be found in the mlr3misc::Dictionary mlr3::mlr_learners.

Super class

mlr3::Learner -> LearnerMultioutput

Methods

Public methods

• LearnerMultioutput$new()

• LearnerMultioutput$clone()

Inherited methods

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerMultioutput$new(
  id,
  param_set = ParamSet$new(),
  predict_types = character(),
  feature_types = character(),
  properties = character(),
  packages = character()
)

Arguments

id

(character(1))
Identifier for the new instance.

param_set

(paradox::ParamSet)
Set of hyperparameters.

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. The following properties are currently standardized and understood by learners in mlr3:

• "missings": The learner can handle missing values in the data.

• "weights": The learner supports observation weights.

• "importance": The learner supports extraction of importance scores, i.e. comes with an $importance() extractor function (see section on optional extractors in Learner).

• "selected_features": The learner supports extraction of the set of selected features, i.e. comes with a $selected_features() extractor function (see section on optional extractors in Learner).

• "oob_error": The learner supports extraction of estimated out of bag error, i.e. comes with a oob_error() extractor function (see section on optional extractors in Learner).

packages

(character())
Set of required packages. A warning is signaled by the constructor if at least one of the packages is not installed, but loaded (not attached) later on-demand via requireNamespace().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerMultioutput$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)
ids = mlr_learners$keys("^Multioutput")
ids

# get a specific learner from mlr_learners:

mlr-org/mlr3multioutput documentation built on Nov. 22, 2020, 1:17 p.m.