task_summary

Create the general task documentation in a dataframe for fairness report.
The information includes<ul>
<li>Audit Date</li>
<li>Task Name</li>
<li>Number of observations</li>
<li>Number of features</li>
<li>Target Name</li>
<li>Feature Names</li>
<li>The Protected Attribute</li>
</ul>

Integrates fairness auditing and bias mitigation methods for
the 'mlr3' ecosystem. This includes fairness metrics, reporting
tools, visualizations and bias mitigation techniques such as
"Reweighing" described in 'Kamiran, Calders' (2012)
<doi:10.1007/s10115-011-0463-8> and "Equalized Odds" described in
'Hardt et al.' (2016)
<https://papers.nips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf>.
Integration with 'mlr3' allows for auditing of ML models as well as
convenient joint tuning of machine learning algorithms and debiasing
methods.

Florian Pfisterer

mlr3fairness

Fairness Auditing and Debiasing for 'mlr3'

Wei Siyi

Michel Lang

task_summary function

Arguments

Task summary for fairness report — task_summary

task_summary: Task summary for fairness report

Description

Usage

Value

Arguments

Examples