mlr_measures_positive_probability: Positive Probability Measure

In mlr3fairness: Fairness Auditing and Debiasing for 'mlr3'

Positive Probability Measure

Description

Return the probabiliy of a positive prediction, often known as 'Calders-Wevers' gap. This is defined as count of positive predictions divided by the number of observations.

Super class

mlr3::Measure -> MeasurePositiveProbability

Methods

Public methods

• MeasurePositiveProbability$new()

• MeasurePositiveProbability$clone()

Inherited methods

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

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Method new()

Initialize a Measure Positive Probability Object

Usage

MeasurePositiveProbability$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

MeasurePositiveProbability$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library("mlr3")
# Create Positive Probability Measure
t = tsk("adult_train")
learner = lrn("classif.rpart", cp = .01)
learner$train(t)
measure = msr("classif.pp")
predictions = learner$predict(t)
predictions$score(measure, task = t)

mlr3fairness documentation built on May 31, 2023, 7:22 p.m.