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<title>Metric scores — metric_scores • fairmodels</title>

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<meta property="og:title" content="Metric scores — metric_scores" />
<meta property="og:description" content="Creates metric_scores object to facilitate visualization. Check how the metric scores differ among models, what is this score, and how it changes
for example after applying bias mitigation technique. The vertical black lines
denote the scores for privileged subgroup. It is best to use only few metrics (using fairness_metrics parameter)" />
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    <h1>Metric scores</h1>
    <small class="dont-index">Source: <a href='https://github.com/ModelOriented/fairmodels/blob/master/R/metric_scores.R'><code>R/metric_scores.R</code></a></small>
    <div class="hidden name"><code>metric_scores.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Creates <code>metric_scores</code> object to facilitate visualization. Check how the metric scores differ among models, what is this score, and how it changes
for example after applying bias mitigation technique. The vertical black lines
denote the scores for privileged subgroup. It is best to use only few metrics (using <code>fairness_metrics</code> parameter)</p>
    </div>

    <pre class="usage"><span class='fu'>metric_scores</span>(<span class='no'>x</span>, <span class='kw'>fairness_metrics</span> <span class='kw'>=</span> <span class='fu'>unique_metrics</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>object of class <code>fairness_object</code></p></td>
    </tr>
    <tr>
      <th>fairness_metrics</th>
      <td><p>character, vector with fairness metric names</p></td>
    </tr>
    </table>

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

    <p><code>metric_scores</code> object.
It is a list containing:</p><ul>
<li><p>metric_scores_data - <code>data.frame</code> with information about score in particular subgroup, metric, and model</p></li>
<li><p>privileged - name of privileged subgroup</p></li>
</ul>


    <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 =  0.06871032 , mean =  0.697272 , max =  0.9978889  
#&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 =  -0.7162897 , mean =  0.002728039 , max =  0.6699699  
#&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'>ms</span> <span class='kw'>&lt;-</span> <span class='fu'>metric_scores</span>(<span class='no'>fobject</span>, <span class='kw'>fairness_metrics</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"TPR"</span>,<span class='st'>"STP"</span>,<span class='st'>"ACC"</span>))
<span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>ms</span>)</div><div class='img'><img src='metric_scores-1.png' alt='' width='700' height='433' /></div><div class='input'>
</div></span></pre>
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