PredictionRegr function

Prediction Object for Regression

This object wraps the predictions returned by a learner of class LearnerRegr , i.e. the predicted response and standard error. Additionally, probability distributions implemented in package distr6 are supported.

Examples

task = tsk("california_housing")
learner = lrn("regr.featureless", predict_type = "se")
p = learner$train(task)$predict(task)
p$predict_types
head(as.data.table(p))

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html

• Package list("mlr3viz") for some generic visualizations.

• Extension packages for additional task types:

  • list("mlr3proba") for probabilistic supervised regression and survival analysis.
  • list("mlr3cluster") for unsupervised clustering.

Other Prediction: Prediction, PredictionClassif

Super class

mlr3::Prediction -> PredictionRegr

Active bindings

• response: (numeric())

   Access the stored predicted response.
  

• se: (numeric())

   Access the stored standard error.
  

• quantiles: (matrix())

   Matrix of predicted quantiles. Observations are in rows, quantile (in ascending order) in columns.
  

• distr: (VectorDistribution)

   Access the stored vector distribution. Requires package `distr6`(in repository [https://raphaels1.r-universe.dev](https://raphaels1.r-universe.dev)) .
  

Methods

Public methods

• list("PredictionRegr$new()")

• list("PredictionRegr$clone()")

Method new()

Creates a new instance of this R6 class.

Usage

PredictionRegr$new(
  task = NULL,
  row_ids = task$row_ids,
  truth = task$truth(),
  response = NULL,
  se = NULL,
  quantiles = NULL,
  distr = NULL,
  check = TRUE
)

Arguments

• task: (TaskRegr )

   Task, used to extract defaults for `row_ids` and `truth`.
  

• row_ids: (integer())

   Row ids of the predicted observations, i.e. the row ids of the test set.
  

• truth: (numeric())

   True (observed) response.
  

• response: (numeric())

   Vector of numeric response values. One element for each observation in the test set.
  

• se: (numeric())

   Numeric vector of predicted standard errors. One element for each observation in the test set.
  

• quantiles: (matrix())

   Numeric matrix of predicted quantiles. One row per observation, one column per quantile.
  

• distr: (VectorDistribution)

   `VectorDistribution` from package distr6 (in repository [https://raphaels1.r-universe.dev](https://raphaels1.r-universe.dev)). Each individual distribution in the vector represents the random variable 'survival time' for an individual observation.
  

• check: (logical(1))

   If `TRUE`, performs some argument checks and predict type conversions.
  

Method clone()

The objects of this class are cloneable with this method.

Usage

PredictionRegr$clone(deep = FALSE)

Arguments

• deep: Whether to make a deep clone.