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    <h1>Plot fairness and performance</h1>
    
    <div class="hidden name"><code>plot_performance_with_fairness.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Plot fairness and performance</p>
    </div>

    <pre class="usage"><span class='fu'>plot_performance_with_fairness</span>(
  <span class='no'>x</span>,
  <span class='kw'>fairness_metric</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
  <span class='kw'>performance_metric</span> <span class='kw'>=</span> <span class='kw'>NULL</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>fairness object</p></td>
    </tr>
    <tr>
      <th>fairness_metric</th>
      <td><p>name of fairness metric</p></td>
    </tr>
    <tr>
      <th>performance_metric</th>
      <td><p>name of DALEX performance metric</p></td>
    </tr>
    </table>

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

    



    <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='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'>rf_compas_1</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'>Number_of_Priors</span>+<span class='no'>Age_Below_TwentyFive</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'>lr_compas_1</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'>rf_compas_2</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'>rf_compas_3</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'>Age_Above_FourtyFive</span>+<span class='no'>Misdemeanor</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'>df</span> <span class='kw'>&lt;-</span> <span class='no'>compas</span>
<span class='no'>df</span>$<span class='no'>Two_yr_Recidivism</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>as.numeric</a></span>(<span class='no'>compas</span>$<span class='no'>Two_yr_Recidivism</span>)-<span class='fl'>1</span>

<span class='no'>explainer_1</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_1</span>,  <span class='kw'>data</span> <span class='kw'>=</span> <span class='no'>compas</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 style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; data              :  6172  rows  6  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 =  0.2165051 , mean =  0.5445997 , max =  0.7034106  
#&gt;   -&gt; residual function :  difference between y and yhat ( </span><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; residuals         :  numerical, min =  -0.7034106 , mean =  0.0002803824 , max =  0.7557015  
#&gt;  </span><span style='color: #00BB00;'> A new explainer has been created! </span><span> </div><div class='input'><span class='no'>explainer_2</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'>lr_compas_1</span>,  <span class='kw'>data</span> <span class='kw'>=</span> <span class='no'>compas</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><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; data              :  6172  rows  6  cols 
#&gt;   -&gt; target variable   :  6172  values 
#&gt;   -&gt; model_info        :  package stats , ver. 3.6.3 , task regression ( </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'>explainer_3</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_2</span>,  <span class='kw'>data</span> <span class='kw'>=</span> <span class='no'>compas</span>[,-<span class='fl'>1</span>], <span class='kw'>y</span> <span class='kw'>=</span> <span class='no'>y_numeric</span>, <span class='kw'>label</span> <span class='kw'>=</span> <span class='st'>"ranger_2"</span>)</div><div class='output co'>#&gt; Preparation of a new explainer is initiated
#&gt;   -&gt; model label       :  ranger_2 
#&gt;   -&gt; data              :  6172  rows  6  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 =  0.1424671 , mean =  0.5448657 , max =  0.8696381  
#&gt;   -&gt; residual function :  difference between y and yhat ( </span><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; residuals         :  numerical, min =  -0.8464878 , mean =  1.440858e-05 , max =  0.7959197  
#&gt;  </span><span style='color: #00BB00;'> A new explainer has been created! </span><span> </div><div class='input'><span class='no'>explainer_4</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_3</span>,  <span class='kw'>data</span> <span class='kw'>=</span> <span class='no'>compas</span>[,-<span class='fl'>1</span>], <span class='kw'>y</span> <span class='kw'>=</span> <span class='no'>y_numeric</span>, <span class='kw'>label</span> <span class='kw'>=</span> <span class='st'>"ranger_3"</span>)</div><div class='output co'>#&gt; Preparation of a new explainer is initiated
#&gt;   -&gt; model label       :  ranger_3 
#&gt;   -&gt; data              :  6172  rows  6  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 =  0.4891646 , mean =  0.5447624 , max =  0.6955387  
#&gt;   -&gt; residual function :  difference between y and yhat ( </span><span style='color: #BBBB00;'> default </span><span> )
#&gt;   -&gt; residuals         :  numerical, min =  -0.6955387 , mean =  0.0001177473 , max =  0.5108354  
#&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_1</span>,<span class='no'>explainer_2</span>,<span class='no'>explainer_3</span>,<span class='no'>explainer_4</span>,
                                    <span class='kw'>data</span> <span class='kw'>=</span> <span class='no'>compas</span>,
                                    <span class='kw'>outcome</span> <span class='kw'>=</span> <span class='st'>"Two_yr_Recidivism"</span>,
                                    <span class='kw'>group</span>  <span class='kw'>=</span> <span class='st'>"Ethnicity"</span>,
                                    <span class='kw'>base</span>   <span class='kw'>=</span> <span class='st'>"Caucasian"</span>)

<span class='fu'>plot_performance_with_fairness</span>(<span class='no'>fobject</span>, <span class='st'>"fpr_parity"</span>, <span class='st'>"auc"</span>)</div><div class='output co'>#&gt; 
#&gt; Creating object with: 
#&gt; Fairness metric </span><span style='color: #00BBBB;'>fpr_parity</span><span> 
#&gt; Performance metric  </span><span style='color: #00BBBB;'>auc</span><span> </div><div class='img'><img src='plot_performance_with_fairness-1.png' alt='' width='700' height='433' /></div><div class='input'>
</div></span></pre>
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