mlr_learners_regr.fairzlm: Regression Fair Regression With Covariance Constraints...

In mlr3fairness: Fairness Auditing and Debiasing for 'mlr3'

Regression Fair Regression With Covariance Constraints Learner

Description

Calls fairml::zlm from package fairml.

Details

Fair regression model from Zafar et al., 2019 implemented via package fairml. The 'unfairness' parameter is set to 0.05 as a default. The optimized fairness metric is statistical parity.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("regr.fairzlm")
lrn("regr.fairzlm")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, fairml

Parameters

Id	Type	Default	Levels	Range
unfairness	numeric	-		[0, 1]
intersect	logical	TRUE	TRUE, FALSE	-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrFairzlm

Methods

Public methods

• LearnerRegrFairzlm$new()

• LearnerRegrFairzlm$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrFairzlm$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrFairzlm$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

pfistfl

References

BJ Z, I V, M G, KP G (2019). “Fairness Constraints: a Flexible Approach for Fair Classification.” In Journal of Machine Learning Research, 30, 1-42.

See Also

Dictionary of Learners: mlr3::mlr_learners

Other fairness_learners: mlr_learners_classif.fairfgrrm, mlr_learners_classif.fairzlrm, mlr_learners_regr.fairfrrm, mlr_learners_regr.fairnclm

Examples

library("mlr3")
# stop example failing with warning if package not installed
learner = suppressWarnings(mlr3::lrn("regr.fairzlm"))
print(learner)

# available parameters:
learner$param_set$ids()

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