Bayesian boosting model application

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,