Density Learner

This Learner specializes Learner for density estimation problems:

• task_type is set to "dens"

• Creates Predictions of class PredictionDens.

• Possible values for predict_types are:

  • "pdf": Evaluates estimated probability density function for each value in the test set.

  • "cdf": Evaluates estimated cumulative distribution function for each value in the test set.

See also

Other Learner: LearnerSurv

Super class

mlr3::Learner -> LearnerDens

Methods

Public methods

• LearnerDens$new()

• LearnerDens$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$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

LearnerDens$new(
  id,
  param_set = ps(),
  predict_types = "cdf",
  feature_types = character(),
  properties = character(),
  data_formats = "data.table",
  packages = character(),
  label = NA_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().

label

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

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

LearnerDens$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)
# get all density learners from mlr_learners:
lrns = mlr_learners$mget(mlr_learners$keys("^dens"))
names(lrns)
#> [1] "dens.hist" "dens.kde" 

# get a specific learner from mlr_learners:
mlr_learners$get("dens.hist")
#> <LearnerDensHistogram:dens.hist>: Histogram Density Estimator
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, distr6
#> * Predict Types:  [pdf], cdf, distr
#> * Feature Types: integer, numeric
#> * Properties: -
lrn("dens.hist")
#> <LearnerDensHistogram:dens.hist>: Histogram Density Estimator
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, distr6
#> * Predict Types:  [pdf], cdf, distr
#> * Feature Types: integer, numeric
#> * Properties: -