Question

据说，我需要构建具有高精度和低召回率的分类器。我应该在RapidMiner中使用哪个运算符？例如，ApplyModel运算符不支持阈值的任何干预？它还不清楚相同的ApplyModel运算符如何与不同建模的模型一起工作：NeuralNetworks，SVN等。

Answer 1

您可以使用Select Recall运算符。这将找到获得所选召回所需的阈值。

此运算符的输入是包含置信度的标记示例集，输出是使用Apply Threshold运算符应用于标记数据的阈值。

Apply Model运算符需要一个示例集和一个模型作为输入。

我做了一个简单的例子，说明了所有这些。

<?xml version="1.0" encoding="UTF-8"?><process version="7.2.003">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="7.2.003" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="7.2.003" expanded="true" height="68" name="Retrieve Sonar" width="90" x="45" y="34">
    <parameter key="repository_entry" value="//Samples/data/Sonar"/>
      </operator>
      <operator activated="true" class="naive_bayes" compatibility="7.2.003" expanded="true" height="82" name="Naive Bayes" width="90" x="45" y="289"/>
      <operator activated="true" class="apply_model" compatibility="7.2.003" expanded="true" height="82" name="Apply Model" width="90" x="179" y="289">
    <list key="application_parameters"/>
      </operator>
      <operator activated="true" class="multiply" compatibility="7.2.003" expanded="true" height="103" name="Multiply" width="90" x="246" y="136"/>
      <operator activated="true" class="materialize_data" compatibility="7.2.003" expanded="true" height="82" name="Materialize Data" width="90" x="380" y="136"/>
      <operator activated="true" class="select_recall" compatibility="7.2.003" expanded="true" height="82" name="Select Recall" width="90" x="313" y="34">
    <parameter key="min_recall" value="0.1"/>
      </operator>
      <operator activated="true" class="apply_threshold" compatibility="7.2.003" expanded="true" height="82" name="Apply Threshold" width="90" x="447" y="34"/>
      <operator activated="true" class="performance_binominal_classification" compatibility="7.2.003" expanded="true" height="82" name="Performance" width="90" x="581" y="34"/>
      <operator activated="true" class="performance_binominal_classification" compatibility="7.2.003" expanded="true" height="82" name="Performance (2)" width="90" x="581" y="136"/>
      <connect from_op="Retrieve Sonar" from_port="output" to_op="Naive Bayes" to_port="training set"/>
      <connect from_op="Naive Bayes" from_port="model" to_op="Apply Model" to_port="model"/>
      <connect from_op="Naive Bayes" from_port="exampleSet" to_op="Apply Model" to_port="unlabelled data"/>
      <connect from_op="Apply Model" from_port="labelled data" to_op="Multiply" to_port="input"/>
      <connect from_op="Multiply" from_port="output 1" to_op="Select Recall" to_port="example set"/>
      <connect from_op="Multiply" from_port="output 2" to_op="Materialize Data" to_port="example set input"/>
      <connect from_op="Materialize Data" from_port="example set output" to_op="Performance (2)" to_port="labelled data"/>
      <connect from_op="Select Recall" from_port="example set" to_op="Apply Threshold" to_port="example set"/>
      <connect from_op="Select Recall" from_port="threshold" to_op="Apply Threshold" to_port="threshold"/>
      <connect from_op="Apply Threshold" from_port="example set" to_op="Performance" to_port="labelled data"/>
      <connect from_op="Performance" from_port="performance" to_port="result 1"/>
      <connect from_op="Performance (2)" from_port="performance" to_port="result 2"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
      <portSpacing port="sink_result 3" spacing="0"/>
    </process>
  </operator>
</process>

希望这有助于开始。

使用RapidMiner构建高精度分类器

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