Measure to compare true observed response with predicted response in regression tasks.
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.
Dictionary
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():
Meta Information
Task type: “regr”
Range: \((-\infty, \infty)\)
Minimize: TRUE
Average: macro
Required Prediction: “quantiles”
Required Packages: mlr3
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.rqr,
mlr_measures_regr.rsq,
mlr_measures_selected_features
Super classes
Measure -> MeasureRegr -> MeasureRegrPinball
Methods
MeasureRegrPinball$new()
Creates a new instance of this R6 class.
Usage
MeasureRegrPinball$new(alpha = 0.5)