Bayesian boosting model application

jevin

Hello,

I am building prediction models using Bayesian boosting with decision trees technique. Below is the process I have set up:

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.2.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.2.000" expanded="true" name="Process">
    <process expanded="true" height="460" width="547">
      <operator activated="true" class="retrieve" compatibility="5.2.000" expanded="true" height="60" name="Retrieve" width="90" x="112" y="165">
        <parameter key="repository_entry" value="CounterStrike_V2"/>
      </operator>
      <operator activated="true" class="split_validation" compatibility="5.2.000" expanded="true" height="112" name="Validation" width="90" x="313" y="165">
        <process expanded="true" height="813" width="316">
          <operator activated="true" class="bayesian_boosting" compatibility="5.2.000" expanded="true" height="76" name="Bayesian Boosting" width="90" x="112" y="120">
            <process expanded="true" height="813" width="682">
              <operator activated="true" class="discretize_by_user_specification" compatibility="5.2.000" expanded="true" name="Discretize">
                <parameter key="attribute_filter_type" value="regular_expression"/>
                <parameter key="regular_expression" value="frozen"/>
                <parameter key="include_special_attributes" value="true"/>
                <list key="classes">
                  <parameter key="first" value="-Infinity"/>
                  <parameter key="last" value="Infinity"/>
                </list>
              </operator>
              <operator activated="true" class="decision_tree" compatibility="5.2.000" expanded="true" height="76" name="Decision Tree" width="90" x="296" y="30"/>
              <connect from_port="training set" to_op="Discretize" to_port="example set input"/>
              <connect from_op="Discretize" from_port="example set output" 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="Bayesian Boosting" to_port="training set"/>
          <connect from_op="Bayesian Boosting" 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" height="813" width="316">
          <operator activated="true" class="apply_model" compatibility="5.2.000" expanded="true" height="76" name="Apply Model" width="90" x="45" y="120">
            <list key="application_parameters"/>
          </operator>
          <operator activated="true" class="performance" compatibility="5.2.000" expanded="true" height="76" name="Performance" width="90" x="179" y="210"/>
          <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="72"/>
          <portSpacing port="source_test set" spacing="18"/>
          <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="Retrieve" 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="36"/>
      <portSpacing port="sink_result 2" spacing="72"/>
      <portSpacing port="sink_result 3" spacing="18"/>
    </process>
  </operator>
</process>

My question: Is there a way to extract final model (in this case decision tree) logic in either SQL or other coding version which I can use to implement?
Current output is set of 10 models. How do I convert those 10 models into a final tree scheme to implement in production?

Please pardon my format of question - this is my first post here.

Thanks,

MariusHelf

Hi,

if you are coding in java and your project license is compatible to AGPL, the easiest way would be to include RapidMiner in your project. Then you could programatically run a process to apply the trained Bayesian Boosting model on your data.

Best,
Marius