mlr_learners_regr.fairzlm {mlr3fairness}R Documentation

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 mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

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

Meta Information

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

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerRegrFairzlm$new()

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()


[Package mlr3fairness version 0.4.0 Index]