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<meta property="og:title" content="Plot fairness object — plot.fairness_object" />
<meta property="og:description" content="Plot fairness check enables to look how big differences are between base subgroup (privileged) and unprivileged ones.
If barplot reaches red zone it means that for this subgroup fairness goal is not satisfied. Multiple subgroups and models can be plotted.
Red and green zone boundary can be moved through epsilon parameter, that needs to be passed through fairness_check." />
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    <h1>Plot fairness object</h1>
    <small class="dont-index">Source: <a href='https://github.com/ModelOriented/fairmodels/blob/master/R/plot_fairness_object.R'><code>R/plot_fairness_object.R</code></a></small>
    <div class="hidden name"><code>plot_fairness_object.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Plot fairness check enables to look how big differences are between base subgroup (privileged) and unprivileged ones.
If barplot reaches red zone it means that for this subgroup fairness goal is not satisfied. Multiple subgroups and models can be plotted.
Red and green zone boundary can be moved through epsilon parameter, that needs to be passed through <code>fairness_check</code>.</p>
    </div>

    <pre class="usage"><span class='co'># S3 method for fairness_object</span>
<span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>x</span>, <span class='no'>...</span>)</pre>

    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
    <table class="ref-arguments">
    <colgroup><col class="name" /><col class="desc" /></colgroup>
    <tr>
      <th>x</th>
      <td><p><code>fairness_check</code> object</p></td>
    </tr>
    <tr>
      <th>...</th>
      <td><p>other plot parameters</p></td>
    </tr>
    </table>

    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>

    <p><code>ggplot2</code> object</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'>
<span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span>(<span class='st'>"german"</span>)

<span class='no'>y_numeric</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>as.numeric</a></span>(<span class='no'>german</span>$<span class='no'>Risk</span>) -<span class='fl'>1</span>

<span class='no'>lm_model</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/glm.html'>glm</a></span>(<span class='no'>Risk</span>~<span class='no'>.</span>,
                <span class='kw'>data</span> <span class='kw'>=</span> <span class='no'>german</span>,
                <span class='kw'>family</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/stats/family.html'>binomial</a></span>(<span class='kw'>link</span><span class='kw'>=</span><span class='st'>"logit"</span>))

<span class='no'>rf_model</span> <span class='kw'>&lt;-</span> <span class='kw pkg'>ranger</span><span class='kw ns'>::</span><span class='fu'><a href='https://rdrr.io/pkg/ranger/man/ranger.html'>ranger</a></span>(<span class='no'>Risk</span> ~<span class='no'>.</span>,
                           <span class='kw'>data</span> <span class='kw'>=</span> <span class='no'>german</span>,
                           <span class='kw'>probability</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
                           <span class='kw'>num.trees</span> <span class='kw'>=</span> <span class='fl'>200</span>)

<span class='no'>explainer_lm</span> <span class='kw'>&lt;-</span> <span class='kw pkg'>DALEX</span><span class='kw ns'>::</span><span class='fu'><a href='https://rdrr.io/pkg/DALEX/man/explain.html'>explain</a></span>(<span class='no'>lm_model</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='no'>german</span>[,-<span class='fl'>1</span>], <span class='kw'>y</span> <span class='kw'>=</span> <span class='no'>y_numeric</span>)</div><div class='output co'>#&gt; Preparation of a new explainer is initiated
#&gt;   -&gt; model label       :  lm  ( <span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; data              :  1000  rows  9  cols 
#&gt;   -&gt; target variable   :  1000  values 
#&gt;   -&gt; predict function  :  yhat.glm  will be used ( </span><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; predicted values  :  numerical, min =  0.1369187 , mean =  0.7 , max =  0.9832426  
#&gt;   -&gt; model_info        :  package stats , ver. 4.0.2 , task classification ( </span><span style='color: #BBBB00;'> default </span><span> ) 
#&gt;   -&gt; residual function :  difference between y and yhat ( </span><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; residuals         :  numerical, min =  -0.9572803 , mean =  1.940006e-17 , max =  0.8283475  
#&gt;  </span><span style='color: #00BB00;'> A new explainer has been created! </span><span> </div><div class='input'><span class='no'>explainer_rf</span> <span class='kw'>&lt;-</span> <span class='kw pkg'>DALEX</span><span class='kw ns'>::</span><span class='fu'><a href='https://rdrr.io/pkg/DALEX/man/explain.html'>explain</a></span>(<span class='no'>rf_model</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='no'>german</span>[,-<span class='fl'>1</span>], <span class='kw'>y</span> <span class='kw'>=</span> <span class='no'>y_numeric</span>)</div><div class='output co'>#&gt; Preparation of a new explainer is initiated
#&gt;   -&gt; model label       :  ranger  ( </span><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; data              :  1000  rows  9  cols 
#&gt;   -&gt; target variable   :  1000  values 
#&gt;   -&gt; predict function  :  yhat.ranger  will be used ( </span><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; predicted values  :  numerical, min =  0.07711706 , mean =  0.6968145 , max =  0.9955159  
#&gt;   -&gt; model_info        :  package ranger , ver. 0.12.1 , task classification ( </span><span style='color: #BBBB00;'> default </span><span> ) 
#&gt;   -&gt; residual function :  difference between y and yhat ( </span><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; residuals         :  numerical, min =  -0.7220284 , mean =  0.003185536 , max =  0.7047361  
#&gt;  </span><span style='color: #00BB00;'> A new explainer has been created! </span><span> </div><div class='input'>
<span class='no'>fobject</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='fairness_check.html'>fairness_check</a></span>(<span class='no'>explainer_lm</span>, <span class='no'>explainer_rf</span>,
                          <span class='kw'>protected</span> <span class='kw'>=</span> <span class='no'>german</span>$<span class='no'>Sex</span>,
                          <span class='kw'>privileged</span> <span class='kw'>=</span> <span class='st'>"male"</span>)</div><div class='output co'>#&gt; Creating fairness object
#&gt; -&gt; Privileged subgroup		: character (</span><span style='color: #00BB00;'> Ok </span><span> )
#&gt; -&gt; Protected variable		: factor (</span><span style='color: #00BB00;'> Ok </span><span> ) 
#&gt; -&gt; Cutoff values for explainers	: 0.5 ( for all subgroups ) 
#&gt; -&gt; Fairness objects		: 0 objects 
#&gt; -&gt; Checking explainers		: 2 in total ( </span><span style='color: #00BB00;'> compatible </span><span> )
#&gt; -&gt; Metric calculation		: 13/13 metrics calculated for all models
#&gt; </span><span style='color: #00BB00;'> Fairness object created succesfully </span><span> </div><div class='input'>
<span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>fobject</span>)</div><div class='img'><img src='plot_fairness_object-1.png' alt='' width='700' height='433' /></div></span></pre>
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