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    <h1>Print group metric</h1>
    <small class="dont-index">Source: <a href='https://github.com/ModelOriented/fairmodels/blob/master/R/print.R'><code>R/print.R</code></a></small>
    <div class="hidden name"><code>print_group_metric.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Print group metric</p>
    </div>

    <pre class="usage"><span class='co'># S3 method for group_metric</span>
<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</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>group_metric</code> object</p></td>
    </tr>
    <tr>
      <th>...</th>
      <td><p>other print parameters</p></td>
    </tr>
    </table>


    <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 =  0.05112103 , mean =  0.697292 , max =  0.9984524  
#&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 =  -0.7416003 , mean =  0.002708007 , max =  0.6776488  
#&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='no'>gm</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='group_metric.html'>group_metric</a></span>(<span class='no'>fobject</span>, <span class='st'>"TPR"</span>, <span class='st'>"f1"</span>, <span class='kw'>parity_loss</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; 
#&gt; Creating object with: 
#&gt; Fairness metric:  TPR 
#&gt; Performance metric:  f1 
#&gt; </div><div class='input'>
<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>gm</span>)</div><div class='output co'>#&gt; Fairness data top rows for TPR parity loss 
#&gt;    group      score  model
#&gt; 1 female 0.08505568     lm
#&gt; 3 female 0.01202405 ranger
#&gt; 
#&gt; Performance data for f1 :                  
#&gt; 1     lm 0.8385382
#&gt; 2 ranger 0.9585635
#&gt; </div></span></pre>
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