R: Prediction Object for Density

PredictionDens {mlr3proba} | R Documentation

## Prediction Object for Density

### Description

This object stores the predictions returned by a learner of class LearnerDens.

The `task_type` is set to `"dens"`.

### Super class

`mlr3::Prediction` -> `PredictionDens`

### Active bindings

`pdf`

  (`numeric()`)
  Access the stored predicted probability density function.

`cdf`

  (`numeric()`)
  Access the stored predicted cumulative distribution function.

`distr`

  (Distribution)
  Access the stored estimated distribution.

### Methods

#### Public methods

- `PredictionDens$new()`

- `PredictionDens$clone()`

Inherited methods

- `mlr3::Prediction$format()`

- `mlr3::Prediction$help()`

- `mlr3::Prediction$print()`

- `mlr3::Prediction$score()`

#### Method `new()`

Creates a new instance of this R6 class.

##### Usage

    PredictionDens$new(
      task = NULL,
      row_ids = task$row_ids,
      pdf = NULL,
      cdf = NULL,
      distr = NULL,
      check = TRUE
    )

##### Arguments

`task`

  (TaskSurv)
  Task, used to extract defaults for `row_ids`.

`row_ids`

  (`integer()`)
  Row ids of the predicted observations, i.e. the row ids of the test set.

`pdf`

  (`numeric()`)
  Numeric vector of estimated probability density function, evaluated at values in test set. One element for each observation in the test set.

`cdf`

  (`numeric()`)
  Numeric vector of estimated cumulative distribution function, evaluated at values in test set. One element for each observation in the test set.

`distr`

  (Distribution)
  Distribution from distr6. The distribution from which `pdf` and `cdf` are derived.

`check`

  (`logical(1)`)
  If `TRUE`, performs argument checks and predict type conversions.

#### Method `clone()`

The objects of this class are cloneable with this method.

##### Usage

    PredictionDens$clone(deep = FALSE)

##### Arguments

`deep`

  Whether to make a deep clone.

### See Also

Other Prediction: `PredictionSurv`

### Examples

    library(mlr3)
    task = mlr_tasks$get("precip")
    learner = mlr_learners$get("dens.hist")
    p = learner$train(task)$predict(task)
    head(as.data.table(p))

[Package mlr3proba version 0.3.2 Index]