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where to put adaboosting operator

YunDYunD Member Posts: 2 Contributor I
edited November 2018 in Help
Hello,

I have a simple question about adaboosting. I look it up in the message board and the web but did not find  relevant information about it.

My question is where to insert adaboosting operator into a modeling process. The main components in the process is the data and validation (with training and testing panel). In the training panel, I pick naive bayes. In the testing panel, I put apply model and then performance operator in it.

So if I want to add the adaBoost operator, where shall I insert this operator?

Thank you all.

Answers

  • homburghomburg Employee-RapidMiner, Member Posts: 114 RM Data Scientist
    Hi YunD,

    adding AdaBoost to your setup is really simple. It encapsulates a modeling operator in order to boost its usability. Since you are training multiple models when using AdaBoost a simple learning scheme like Naive-Bayes or a decision tree is recommended. Per default ten models are trained, adjusting example weights during each run. Finally a boosted model is returned, which is a weighted ensemble of the different models trained during the boosting procedure. Please have a look at the demo process: 

    <?xml version="1.0" encoding="UTF-8" standalone="no"?>
    <process version="6.0.008">
      <context>
        <input/>
        <output/>
        <macros/>
      </context>
      <operator activated="true" class="process" compatibility="6.0.002" expanded="true" name="Process">
        <process expanded="true">
          <operator activated="true" class="retrieve" compatibility="6.0.008" expanded="true" height="60" name="Sonar" width="90" x="246" y="30">
            <parameter key="repository_entry" value="//Samples/data/Sonar"/>
          </operator>
          <operator activated="true" class="split_validation" compatibility="6.0.008" expanded="true" height="112" name="Validation" width="90" x="447" y="30">
            <process expanded="true">
              <operator activated="true" class="adaboost" compatibility="6.0.008" expanded="true" height="76" name="AdaBoost" width="90" x="112" y="30">
                <process expanded="true">
                  <operator activated="true" class="decision_tree" compatibility="6.0.008" expanded="true" height="76" name="Decision Tree" width="90" x="316" y="30"/>
                  <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"/>
                </process>
              </operator>
              <connect from_port="training" to_op="AdaBoost" to_port="training set"/>
              <connect from_op="AdaBoost" from_port="model" to_port="model"/>
              <portSpacing port="source_training" 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="6.0.008" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
                <list key="application_parameters"/>
              </operator>
              <operator activated="true" class="performance_classification" compatibility="6.0.008" expanded="true" height="76" name="Performance" width="90" x="179" y="30">
                <list key="class_weights"/>
              </operator>
              <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="averagable 1"/>
              <portSpacing port="source_model" spacing="0"/>
              <portSpacing port="source_test set" spacing="0"/>
              <portSpacing port="source_through 1" spacing="0"/>
              <portSpacing port="sink_averagable 1" spacing="0"/>
              <portSpacing port="sink_averagable 2" spacing="0"/>
            </process>
          </operator>
          <connect from_op="Sonar" from_port="output" to_op="Validation" to_port="training"/>
          <connect from_op="Validation" from_port="model" to_port="result 1"/>
          <connect from_op="Validation" from_port="averagable 1" to_port="result 2"/>
          <portSpacing port="source_input 1" spacing="0"/>
          <portSpacing port="sink_result 1" spacing="0"/>
          <portSpacing port="sink_result 2" spacing="18"/>
          <portSpacing port="sink_result 3" spacing="0"/>
        </process>
      </operator>
    </process>
    Cheers,
    Helge
  • YunDYunD Member Posts: 2 Contributor I
    Thanks a lot! this helps!
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