在测试数据的性能评估中缺少预测标签

Question

我使用神经集运算符训练了一个模型，现在我想应用该模型并评估其在测试数据上的性能（带有out标签属性）。为此，我使用应用模型运算符，其第一个输入是我训练的建模数据输出，其中包含（预测值和置信度值），应用模型运算符的第二个输入是我的未标记测试数据，参考（How to test on testset using Rapidminer?）。以下是执行前原始模型的屏幕截图：

当我执行该过程时，它会抛出，输入示例集必须具有特殊属性标签，请参见下面的屏幕截图：

当我点击链接到帮助我解决问题时，它会添加set role operator我设置label属性，执行后会显示缺少预测标签属性，< / p>

更新： 请参阅以下XML：

<?xml version="1.0" encoding="UTF-8"?><process version="8.2.000">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="8.2.000" expanded="true" name="Process">
<process expanded="true">
  <operator activated="true" class="retrieve" compatibility="8.2.000" expanded="true" height="68" name="Retrieve" width="90" x="246" y="34">
    <parameter key="repository_entry" value="../data/neural"/>
  </operator>
  <operator activated="true" class="set_role" compatibility="8.2.000" expanded="true" height="82" name="Set Role (2)" width="90" x="380" y="34">
    <parameter key="attribute_name" value="Elective1"/>
    <parameter key="target_role" value="label"/>
    <list key="set_additional_roles"/>
  </operator>
  <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">
    <list key="comparison_groups"/>
  </operator>
  <operator activated="true" class="neural_net" compatibility="8.2.000" expanded="true" height="82" name="Neural Net" width="90" x="648" y="34">
    <list key="hidden_layers"/>
  </operator>
  <operator activated="true" class="retrieve" compatibility="8.2.000" expanded="true" height="68" name="Retrieve (2)" width="90" x="246" y="136">
    <parameter key="repository_entry" value="../data/testing neural"/>
  </operator>
  <operator activated="true" class="apply_model" compatibility="8.2.000" expanded="true" height="82" name="Apply Model (2)" width="90" x="447" y="187">
    <list key="application_parameters"/>
  </operator>
  <operator activated="true" class="apply_model" compatibility="8.2.000" expanded="true" height="82" name="Apply Model" width="90" x="648" y="187">
    <list key="application_parameters"/>
  </operator>
  <operator activated="true" class="set_role" compatibility="8.2.000" expanded="true" height="82" name="Set Role" width="90" x="916" y="85">
    <parameter key="attribute_name" value="prediction(Elective1)"/>
    <parameter key="target_role" value="label"/>
    <list key="set_additional_roles"/>
  </operator>
  <operator activated="true" class="performance" compatibility="8.2.000" expanded="true" height="82" name="Performance" width="90" x="1184" y="136"/>
  <connect from_op="Retrieve" from_port="output" to_op="Set Role (2)" to_port="example set input"/>
  <connect from_op="Set Role (2)" from_port="example set output" to_op="Nominal to Numerical" to_port="example set input"/>
  <connect from_op="Nominal to Numerical" from_port="example set output" to_op="Neural Net" to_port="training set"/>
  <connect from_op="Nominal to Numerical" from_port="preprocessing model" to_op="Apply Model (2)" to_port="model"/>
  <connect from_op="Neural Net" from_port="model" to_op="Apply Model" to_port="model"/>
  <connect from_op="Retrieve (2)" from_port="output" to_op="Apply Model (2)" to_port="unlabelled data"/>
  <connect from_op="Apply Model (2)" from_port="labelled data" to_op="Apply Model" to_port="unlabelled data"/>
  <connect from_op="Apply Model" from_port="labelled data" to_op="Set Role" to_port="example set input"/>
  <connect from_op="Set Role" from_port="example set output" to_op="Performance" to_port="labelled data"/>
  <connect from_op="Performance" from_port="performance" to_port="result 1"/>
  <portSpacing port="source_input 1" spacing="0"/>
  <portSpacing port="sink_result 1" spacing="0"/>
  <portSpacing port="sink_result 2" spacing="0"/>
   </process>
  </operator>
 </process>

有什么建议吗？

Answer 1

尽管我知道你不需要两个＆＃34;应用模型＆＃34;运营商。 。 。 尝试使用一个应用模型并将测试数据连接到 unl 并将数据训练到 mod