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<meta property="og:title" content="Plot fairness check — plot.fairness_check" />
<meta property="og:description" content="Plot fairness check enables to look how big differences are between base subgroup (privildged) and unpriviledged 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 check</h1>
    
    <div class="hidden name"><code>plot.fairness_check.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Plot fairness check enables to look how big differences are between base subgroup (privildged) and unpriviledged 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.</p>
    </div>

    <pre class="usage"><span class='co'># S3 method for fairness_check</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>fainress_check 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>ggplot 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/base/library.html'>library</a></span>(<span class='no'>DALEX</span>)
<span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>ranger</span>)

<span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span>(<span class='st'>"compas"</span>)

<span class='co'># chaging levels - positive situation here is not becoming recidivist</span>
<span class='no'>compas</span>$<span class='no'>Two_yr_Recidivism</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/factor.html'>factor</a></span>(<span class='no'>compas</span>$<span class='no'>Two_yr_Recidivism</span>, <span class='kw'>levels</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>0</span>))

<span class='no'>rf_compas</span>  <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/ranger/man/ranger.html'>ranger</a></span>(<span class='no'>Two_yr_Recidivism</span> ~<span class='no'>.</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='no'>compas</span>, <span class='kw'>probability</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
<span class='no'>glm_compas</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/glm.html'>glm</a></span>(<span class='no'>Two_yr_Recidivism</span>~<span class='no'>.</span>, <span class='kw'>data</span><span class='kw'>=</span><span class='no'>compas</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'>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'>compas</span>$<span class='no'>Two_yr_Recidivism</span>)-<span class='fl'>1</span>

<span class='no'>explainer_rf</span>  <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/DALEX/man/explain.html'>explain</a></span>(<span class='no'>rf_compas</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='no'>compas</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 style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; data              :  6172  rows  7  cols 
#&gt;   -&gt; target variable   :  6172  values 
#&gt;   -&gt; model_info        :  package ranger , ver. 0.12.1 , task classification ( </span><span style='color: #BBBB00;'> default </span><span> ) 
#&gt;   -&gt; predict function  :  yhat.ranger  will be used ( </span><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; predicted values  :  numerical, min =  0.1402599 , mean =  0.5448056 , max =  0.8718372  
#&gt;   -&gt; residual function :  difference between y and yhat ( </span><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; residuals         :  numerical, min =  -0.8468608 , mean =  7.450248e-05 , max =  0.7926691  
#&gt;  </span><span style='color: #00BB00;'> A new explainer has been created! </span><span> </div><div class='input'><span class='no'>explainer_glm</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/DALEX/man/explain.html'>explain</a></span>(<span class='no'>glm_compas</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='no'>compas</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><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; data              :  6172  rows  7  cols 
#&gt;   -&gt; target variable   :  6172  values 
#&gt;   -&gt; model_info        :  package stats , ver. 4.0.0 , task classification ( </span><span style='color: #BBBB00;'> default </span><span> ) 
#&gt;   -&gt; predict function  :  yhat.glm  will be used ( </span><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; predicted values  :  numerical, min =  0.004522979 , mean =  0.5448801 , max =  0.8855426  
#&gt;   -&gt; residual function :  difference between y and yhat ( </span><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; residuals         :  numerical, min =  -0.8822826 , mean =  -5.051528e-13 , max =  0.9767658  
#&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='create_fairness_object.html'>create_fairness_object</a></span>(<span class='no'>explainer_glm</span>, <span class='no'>explainer_rf</span>,
                                 <span class='kw'>outcome</span> <span class='kw'>=</span> <span class='st'>"Two_yr_Recidivism"</span>,
                                 <span class='kw'>data</span>  <span class='kw'>=</span> <span class='no'>compas</span>,
                                 <span class='kw'>group</span> <span class='kw'>=</span> <span class='st'>"Sex"</span>,
                                 <span class='kw'>base</span>  <span class='kw'>=</span> <span class='st'>"Female"</span>,
                                <span class='kw'>cutoff</span> <span class='kw'>=</span> <span class='fl'>0.5</span>)

<span class='no'>fc</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='fairness_check.html'>fairness_check</a></span>(<span class='no'>fobject</span>)
<span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>fc</span>)</div><div class='img'><img src='plot.fairness_check-1.png' alt='' width='700' height='433' /></div></span></pre>
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