Rapidminer 中的属性不匹配

从我所看到的问题是，您没有将Nominal to Numerical运算符应用于您的测试集。在默认设置中，此运算符为在指定属性中找到的每个标称值创建一个虚拟编码。在您的情况下，您将有一个名为“Course1=A”的列/属性，其中每个示例的条目为 1，其中原始列是“A”，依此类推。

您需要做的是对测试数据应用与训练数据相同的编码。如您所见，Nominal to Numerical运算符有一个额外的输出端口，称为pre（预处理模型的缩写）。这可以用于在多个数据集上应用相同的预处理步骤（如规范化或编码）。

为了令人信服，您还可以使用Group Model运算符将多个模型组合为一个。

请参阅下面的流程 XML（只需将其 c&p 到 RapidMiner 的流程视图中）作为示例。

<?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 Golf" width="90" x="45" y="34">
    <parameter key="repository_entry" value="//Samples/data/Golf"/>
  </operator>
  <operator activated="true" class="nominal_to_numerical" compatibility="8.2.000" expanded="true" height="103" name="Nominal to Numerical" width="90" x="179" y="34">
    <list key="comparison_groups"/>
    <description align="center" color="purple" colored="true" width="126">Transform the nominal attributes into a dummy encoding with 0/1 for each expression.&lt;br&gt;This encoding is then also delivered via &amp;quot;pre&amp;quot; output port.</description>
  </operator>
  <operator activated="true" class="neural_net" compatibility="8.2.000" expanded="true" height="82" name="Neural Net" width="90" x="447" y="34">
    <list key="hidden_layers"/>
  </operator>
  <operator activated="true" class="retrieve" compatibility="8.2.000" expanded="true" height="68" name="Retrieve Golf-Testset" width="90" x="45" y="340">
    <parameter key="repository_entry" value="//Samples/data/Golf-Testset"/>
  </operator>
  <operator activated="true" class="apply_model" compatibility="8.2.000" expanded="true" height="82" name="Apply Model (2)" width="90" x="447" y="340">
    <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="340">
    <list key="application_parameters"/>
  </operator>
  <connect from_op="Retrieve Golf" from_port="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 Golf-Testset" 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_port="result 1"/>
  <portSpacing port="source_input 1" spacing="0"/>
  <portSpacing port="sink_result 1" spacing="0"/>
  <portSpacing port="sink_result 2" spacing="0"/>
  <description align="center" color="green" colored="true" height="103" resized="true" width="315" x="433" y="433">First apply the &amp;quot;preprocessing&amp;quot; model so the test data have the same structure&lt;br/&gt;&lt;br/&gt;Then apply the trained neural net</description>
</process>
</operator>
</process>

也可以随时在RapidMiner 社区论坛中进一步提问或重新发布问题。