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Computes a composite measure from multiple fairness metrics and aggregates them using aggfun (defaulting to mean()).

Super class

mlr3::Measure -> MeasureFairnessComposite

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage

MeasureFairnessComposite$new(
  id = NULL,
  measures,
  aggfun = function(x) mean(x, na.rm = TRUE),
  operation = groupdiff_absdiff,
  minimize = TRUE,
  range = c(-Inf, Inf)
)

Arguments

id

(character(1))
Id of the measure. Defaults to the concatenation of ids in measure.

measures

(list of MeasureFairness)
List of fairness measures to aggregate.

aggfun

(function())
Aggregation function used to aggregate results from respective measures. Defaults to sum.

operation

(function())
The operation used to compute the difference. A function that returns a single value given input: computed metric for each subgroup. Defaults to groupdiff_absdiff. See MeasureFairness for more information.

minimize

(logical(1))
Should the measure be minimized? Defaults to TRUE.

range

(numeric(2))
Range of the resulting measure. Defaults to c(-Inf, Inf).


Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureFairnessComposite$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Equalized Odds Metric
MeasureFairnessComposite$new(measures = msrs(c("fairness.fpr", "fairness.tpr")))
#> <MeasureFairnessComposite:fairness.fpr_tpr>
#> * Packages: mlr3, mlr3fairness
#> * Range: [-Inf, Inf]
#> * Minimize: TRUE
#> * Average: macro
#> * Parameters: list()
#> * Properties: requires_task
#> * Predict type: response

# Other metrics e.g. based on negative rates
MeasureFairnessComposite$new(measures = msrs(c("fairness.fnr", "fairness.tnr")))
#> <MeasureFairnessComposite:fairness.fnr_tnr>
#> * Packages: mlr3, mlr3fairness
#> * Range: [-Inf, Inf]
#> * Minimize: TRUE
#> * Average: macro
#> * Parameters: list()
#> * Properties: requires_task
#> * Predict type: response