mlr3 (version 1.0.0)

mlr_measures_regr.pinball: Average Pinball Loss

Description

Measure to compare true observed response with predicted response in regression tasks.

Dictionary

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():

mlr_measures$get("regr.pinball")
msr("regr.pinball")

Meta Information

• Task type: “regr”

• Range: \((-\infty, \infty)\)

• Minimize: TRUE

• Average: macro

• Required Prediction: “quantiles”

• Required Packages: mlr3

Parameters

Id	Type	Default	Range
alpha	numeric	-	\([0, 1]\)

Super classes

mlr3::Measure -> mlr3::MeasureRegr -> MeasurePinball

Methods

Public methods

• MeasureRegrPinball$new()

• MeasureRegrPinball$clone()

Inherited methods

• mlr3::Measure$aggregate()
• mlr3::Measure$format()
• mlr3::Measure$help()
• mlr3::Measure$print()
• mlr3::Measure$score()

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Method new()

Creates a new instance of this R6 class.

Usage

MeasureRegrPinball$new(alpha = 0.5)

Arguments

alpha

numeric(1)
The quantile to compute the pinball loss. Must be one of the quantiles that the Learner was trained on.

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Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureRegrPinball$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Details

The pinball loss for quantile regression is defined as $$ \text{Average Pinball Loss} = \frac{1}{n} \sum_{i=1}^{n} w_{i} \begin{cases} q \cdot (t_i - r_i) & \text{if } t_i \geq r_i \\ (1 - q) \cdot (r_i - t_i) & \text{if } t_i < r_i \end{cases} $$ where \(q\) is the quantile and \(w_i\) are normalized sample weights.

See Also

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

• Package mlr3measures for the scoring functions. Dictionary of Measures: mlr_measures as.data.table(mlr_measures) for a table of available Measures in the running session (depending on the loaded packages).

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

Other Measure: Measure, MeasureClassif, MeasureRegr, MeasureSimilarity, mlr_measures, mlr_measures_aic, mlr_measures_bic, mlr_measures_classif.costs, mlr_measures_debug_classif, mlr_measures_elapsed_time, mlr_measures_internal_valid_score, mlr_measures_oob_error, mlr_measures_regr.rsq, mlr_measures_selected_features