pre_process_data

Function aggregates all pre-processing algorithms for bias mitigation. User passes unified arguments and specifies type to receive transformed <code>data.frame</code>

Measure fairness metrics in one place for many models. Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various pre-processing and post-processing bias mitigation algorithms implemented. Package also supports calculating fairness metrics for regression models. Find more details in (Wiśniewski, Biecek (2021)) <arXiv:2104.00507>.

Jakub Wi<c5><9b>niewski

fairmodels

Flexible Tool for Bias Detection, Visualization, and Mitigation

Jakub Wiśniewski

Przemysław Biecek

pre_process_data function

<dl><dt>data</dt>
<dd><code>data.frame</code></dd>
<dt>protected</dt>
<dd>factor, protected attribute (sensitive variable) containing information about gender, race etc...</dd>
<dt>y</dt>
<dd>numeric, numeric values of predicted variable. 1 should denote favorable outcome.</dd>
<dt>type</dt>
<dd>character, type of pre-processing algorithm to be used, one of:<ul>
<li>resample_uniform</li>
<li>resample_preferential</li>
<li>reweight</li>
<li>disparate_impact_remover</li>
</ul></dd>
<dt>...</dt>
<dd>other parameters passed to pre-processing algorithms</dd></dl>

Arguments

Pre-process data — pre_process_data

<dl>

<dt>data</dt>
<dd><code>data.frame</code></dd>


<dt>protected</dt>
<dd>factor, protected attribute (sensitive variable) containing information about gender, race etc...</dd>


<dt>y</dt>
<dd>numeric, numeric values of predicted variable. 1 should denote favorable outcome.</dd>


<dt>type</dt>
<dd>character, type of pre-processing algorithm to be used, one of:<ul>
<li>resample_uniform</li>
<li>resample_preferential</li>
<li>reweight</li>
<li>disparate_impact_remover</li>
</ul></dd>


<dt>...</dt>
<dd>other parameters passed to pre-processing algorithms</dd>

</dl>

pre_process_data: Pre-process data

Description

Usage

Value

Arguments

Examples