R: Density Learner

LearnerDens {mlr3proba} | R Documentation

## Density Learner

### Description

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.

### Super class

`mlr3::Learner` -> `LearnerDens`

### Methods

#### Public methods

- `LearnerDens$new()`

- `LearnerDens$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

    LearnerDens$new(
      id,
      param_set = ParamSet$new(),
      predict_types = "cdf",
      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

    LearnerDens$clone(deep = FALSE)

##### Arguments

`deep`

  Whether to make a deep clone.

### See Also

Other Learner: `LearnerSurv`

### Examples

    library(mlr3)
    # get all density learners from mlr_learners:
    lrns = mlr_learners$mget(mlr_learners$keys("^dens"))
    names(lrns)

    # get a specific learner from mlr_learners:
    mlr_learners$get("dens.hist")
    lrn("dens.hist")

[Package mlr3proba version 0.4.0 Index]