Need AUC for data which has label with polynomial value

varunmandalapu1

Hello,

 

I attached my data set with 205 Attributed in which Label is the last column named as Label. My label has 4 distinct values C1, C2, C3 & C4. I applied cross validation with Decision tree in training and Apply model and Performance in testing. The output has Accuracy and Kappa, but there is no AUC in the output. For AUC, Can I get AUC for polynomical value. Please suggest.

Thomas_Ott

Ah I read your post closer, to get AUC you need a max of two classes. You can't have four. 

Thomas_Ott

Please post your XML using the </> code button above.

 

Usually this is related to the type of Performance operator you use.

varunmandalapu1

<?xml version="1.0" encoding="UTF-8"?><process version="8.0.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="8.0.001" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="8.0.001" expanded="true" height="68" name="Retrieve 2 Clip Modified(Average X1 &amp; X2) Data" width="90" x="45" y="34">
        <parameter key="repository_entry" value="//My Repository/AIED Project/2 Clip Modified(Average X1 &amp; X2) Data"/>
      </operator>
      <operator activated="true" class="concurrency:cross_validation" compatibility="8.0.001" expanded="true" height="145" name="Cross Validation" width="90" x="246" y="34">
        <process expanded="true">
          <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="8.0.001" expanded="true" height="103" name="Decision Tree" width="90" x="112" y="34"/>
          <connect from_port="training set" to_op="Decision Tree" to_port="training set"/>
          <connect from_op="Decision Tree" from_port="model" to_port="model"/>
          <portSpacing port="source_training set" spacing="0"/>
          <portSpacing port="sink_model" spacing="0"/>
          <portSpacing port="sink_through 1" spacing="0"/>
        </process>
        <process expanded="true">
          <operator activated="true" class="apply_model" compatibility="8.0.001" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
            <list key="application_parameters"/>
          </operator>
          <operator activated="true" class="performance" compatibility="8.0.001" expanded="true" height="82" name="Performance" width="90" x="179" y="34"/>
          <connect from_port="model" to_op="Apply Model" to_port="model"/>
          <connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
          <connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
          <connect from_op="Performance" from_port="performance" to_port="performance 1"/>
          <portSpacing port="source_model" spacing="0"/>
          <portSpacing port="source_test set" spacing="0"/>
          <portSpacing port="source_through 1" spacing="0"/>
          <portSpacing port="sink_test set results" spacing="0"/>
          <portSpacing port="sink_performance 1" spacing="0"/>
          <portSpacing port="sink_performance 2" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Retrieve 2 Clip Modified(Average X1 &amp; X2) Data" from_port="output" to_op="Cross Validation" to_port="example set"/>
      <connect from_op="Cross Validation" from_port="performance 1" 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>

Telcontar120

If you want AUC, you will need to reformulate your existing polynominal label into a series of binominals: e.g., C1 vs not- C1, C2 vs not-C2, etc.  Then you will get 4 different AUCs, one for each predicted class.  Unfortunately RapidMiner won't do this automatically for you, but it is definitely possible to do on your own.

 

MartinLiebig

Hi,

 

you can build this with polynominal by binominal Classification and Remember-Recall.

 

Best,

Martin