Evaluate a metric on a subgroup

Allows for calculation of arbitrary mlr3::Measure()s on a selected sub-group.

See also

MeasureFairness, groupwise_metrics

Super class

mlr3::Measure -> MeasureSubgroup

Public fields

base_measure

(Measure())
The base measure to be used by the fairness measures, e.g. mlr_measures_classif.fpr for the false positive rate.

subgroup

(character)|(integer)
Subgroup identifier.

intersect

(logical)
Should groups be intersected?

Methods

Public methods

• MeasureSubgroup$new()

• MeasureSubgroup$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

MeasureSubgroup$new(id = NULL, base_measure, subgroup, intersect = TRUE)

Arguments

id

(character)
The measure's id. Set to 'fairness.<base_measure_id>' if ommited.

base_measure

(Measure())
The measure used to measure fairness.

subgroup

(character)|(integer)
Subgroup identifier. Either value for the protected attribute or position in task$levels.

intersect

logical
Should multiple pta groups be intersected? Defaults to TRUE. Only relevant if more than one pta columns are provided.

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Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureSubgroup$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library("mlr3")
# Create MeasureFairness to measure the Predictive Parity.
t = tsk("adult_train")
learner = lrn("classif.rpart", cp = .01)
learner$train(t)
measure = msr("subgroup", base_measure = msr("classif.acc"), subgroup = "Female")
predictions = learner$predict(t)
predictions$score(measure, task = t)
#> subgroup.acc_Female 
#>           0.9232628