title: Missing predicted label in performance evaluation of test data
body: <p>I have trained a model using <strong>neural set</strong> operator, now I want to apply that model and evaluate its performance on test data (with out label attribute). For this, I used <strong>apply model</strong> operator with its first input is my trained modeled data's output that contains (predicted and confidence values) and the second input of apply model operator is my Unlabelled test data, for referencce (<a href="https://stackoverflow.com/questions/15878053/how-to-test-on-testset-using-rapidminer">How to test on testset using Rapidminer?</a> ). Below is the screenshot of my original model before execution:</p>
  
  <p><a href="https://i.stack.imgur.com/1FT7p.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/1FT7p.png" alt="Original Model"></a></p>
  
  <p>When I execute the process, it throws, <strong>Input example set must have special attribute label</strong>, see the below screenshot:</p>
  
  <p><a href="https://i.stack.imgur.com/n6VdL.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/n6VdL.png" alt="Missing Label"></a></p>
  
  <p>When I click link to <strong>Help me solve the problem</strong> , it adds set role operator where I set my label attribute, after execution it displays <strong>missing predicted label attribute</strong>,</p>
  
  <p><a href="https://i.stack.imgur.com/WzWTn.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/WzWTn.png" alt="Missing Predicted Labelled Attribute"></a></p>
  
  <p><strong>UPDATED:</strong>
  please see the XML below:</p>
  
  <pre><code>&lt;?xml version="1.0" encoding="UTF-8"?&gt;&lt;process version="8.2.000"&gt;
  &lt;context&gt;
  &lt;input/&gt;
  &lt;output/&gt;
  &lt;macros/&gt;
  &lt;/context&gt;
  &lt;operator activated="true" class="process" compatibility="8.2.000" expanded="true" name="Process"&gt;
  &lt;process expanded="true"&gt;
    &lt;operator activated="true" class="retrieve" compatibility="8.2.000" expanded="true" height="68" name="Retrieve" width="90" x="246" y="34"&gt;
      &lt;parameter key="repository_entry" value="../data/neural"/&gt;
    &lt;/operator&gt;
    &lt;operator activated="true" class="set_role" compatibility="8.2.000" expanded="true" height="82" name="Set Role (2)" width="90" x="380" y="34"&gt;
      &lt;parameter key="attribute_name" value="Elective1"/&gt;
      &lt;parameter key="target_role" value="label"/&gt;
      &lt;list key="set_additional_roles"/&gt;
    &lt;/operator&gt;
    &lt;operator activated="true" class="nominal_to_numerical" compatibility="8.2.000" expanded="true" height="103" name="Nominal to Numerical" width="90" x="514" y="34"&gt;
      &lt;list key="comparison_groups"/&gt;
    &lt;/operator&gt;
    &lt;operator activated="true" class="neural_net" compatibility="8.2.000" expanded="true" height="82" name="Neural Net" width="90" x="648" y="34"&gt;
      &lt;list key="hidden_layers"/&gt;
    &lt;/operator&gt;
    &lt;operator activated="true" class="retrieve" compatibility="8.2.000" expanded="true" height="68" name="Retrieve (2)" width="90" x="246" y="136"&gt;
      &lt;parameter key="repository_entry" value="../data/testing neural"/&gt;
    &lt;/operator&gt;
    &lt;operator activated="true" class="apply_model" compatibility="8.2.000" expanded="true" height="82" name="Apply Model (2)" width="90" x="447" y="187"&gt;
      &lt;list key="application_parameters"/&gt;
    &lt;/operator&gt;
    &lt;operator activated="true" class="apply_model" compatibility="8.2.000" expanded="true" height="82" name="Apply Model" width="90" x="648" y="187"&gt;
      &lt;list key="application_parameters"/&gt;
    &lt;/operator&gt;
    &lt;operator activated="true" class="set_role" compatibility="8.2.000" expanded="true" height="82" name="Set Role" width="90" x="916" y="85"&gt;
      &lt;parameter key="attribute_name" value="prediction(Elective1)"/&gt;
      &lt;parameter key="target_role" value="label"/&gt;
      &lt;list key="set_additional_roles"/&gt;
    &lt;/operator&gt;
    &lt;operator activated="true" class="performance" compatibility="8.2.000" expanded="true" height="82" name="Performance" width="90" x="1184" y="136"/&gt;
    &lt;connect from_op="Retrieve" from_port="output" to_op="Set Role (2)" to_port="example set input"/&gt;
    &lt;connect from_op="Set Role (2)" from_port="example set output" to_op="Nominal to Numerical" to_port="example set input"/&gt;
    &lt;connect from_op="Nominal to Numerical" from_port="example set output" to_op="Neural Net" to_port="training set"/&gt;
    &lt;connect from_op="Nominal to Numerical" from_port="preprocessing model" to_op="Apply Model (2)" to_port="model"/&gt;
    &lt;connect from_op="Neural Net" from_port="model" to_op="Apply Model" to_port="model"/&gt;
    &lt;connect from_op="Retrieve (2)" from_port="output" to_op="Apply Model (2)" to_port="unlabelled data"/&gt;
    &lt;connect from_op="Apply Model (2)" from_port="labelled data" to_op="Apply Model" to_port="unlabelled data"/&gt;
    &lt;connect from_op="Apply Model" from_port="labelled data" to_op="Set Role" to_port="example set input"/&gt;
    &lt;connect from_op="Set Role" from_port="example set output" to_op="Performance" to_port="labelled data"/&gt;
    &lt;connect from_op="Performance" from_port="performance" to_port="result 1"/&gt;
    &lt;portSpacing port="source_input 1" spacing="0"/&gt;
    &lt;portSpacing port="sink_result 1" spacing="0"/&gt;
    &lt;portSpacing port="sink_result 2" spacing="0"/&gt;
     &lt;/process&gt;
    &lt;/operator&gt;
   &lt;/process&gt;
  </code></pre>
  
  <p>Any suggestions??</p>