This Learner specializes Learner for regression problems:

  • task_type is set to "regr".

  • Creates Predictions of class PredictionRegr.

  • Possible values for predict_types are:

    • "response": Predicts a numeric response for each observation in the test set.

    • "se": Predicts the standard error for each value of response for each observation in the test set.

    • "distr": Probability distribution as distr6::VectorDistribution object (requires package distr6).

Predefined learners can be found in the dictionary mlr_learners. Essential regression learners can be found in this dictionary after loading mlr3learners. Additional learners are implement in the Github package https://github.com/mlr-org/mlr3extralearners.

See also

Super class

mlr3::Learner -> LearnerRegr

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegr$new(
  id,
  param_set = ParamSet$new(),
  predict_types = "response",
  feature_types = character(),
  properties = character(),
  data_formats = "data.table",
  packages = character(),
  man = NA_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).

data_formats

(character())
Set of supported data formats which can be processed during $train() and $predict(), e.g. "data.table".

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().

man

(character(1))
String in the format [pkg]::[topic] pointing to a manual page for this object. The referenced help package can be opened via method $help().


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegr$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# get all regression learners from mlr_learners: lrns = mlr_learners$mget(mlr_learners$keys("^regr")) names(lrns)
#> [1] "regr.featureless" "regr.rpart"
# get a specific learner from mlr_learners: mlr_learners$get("regr.rpart")
#> <LearnerRegrRpart:regr.rpart> #> * Model: - #> * Parameters: xval=0 #> * Packages: rpart #> * Predict Type: response #> * Feature types: logical, integer, numeric, factor, ordered #> * Properties: importance, missings, selected_features, weights
lrn("classif.featureless")
#> <LearnerClassifFeatureless:classif.featureless> #> * Model: - #> * Parameters: method=mode #> * Packages: - #> * Predict Type: response #> * Feature types: logical, integer, numeric, character, factor, ordered, #> POSIXct #> * Properties: featureless, importance, missings, multiclass, #> selected_features, twoclass