mlr_measures_regr.pinball {mlr3} | R Documentation |
Average Pinball Loss
Description
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()
:
mlr_measures$get("regr.pinball") msr("regr.pinball")
Meta Information
Task type: “regr”
Range:
(-\infty, \infty)
Minimize: TRUE
Average: macro
Required Prediction: “quantiles”
Required Packages: mlr3
Parameters
Id | Type | Default | Range |
alpha | numeric | - | [0, 1] |
Super classes
mlr3::Measure
-> mlr3::MeasureRegr
-> MeasurePinball
Methods
Public methods
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
MeasureRegrPinball$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.
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.
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