Bagging Boosting and Voting

anujsingh

Hi All I am new to the rapidminer 5 and I have a task at hand I have to learn the techniques bagging boosting and voting

can any one please tell me how to perform these operators what kind of input data is needed for these task

Please help as its kind of urgent

MariusHelf

Hi,

do you know the basic concepts of these techniques? You can read about them (and many many other algorithms) for example in the book The Elements of Statistical Learning by Tibshirani et.al. which gives a very good (but also detailed and probably challenging) introduction to machine learning.
If you know how the algorithms are working, you can use them in RapidMiner with a process similar to the attached one. As input you can use any dataset with a label which is compatible to the inner learner. In the example I use a Decision Tree as base classifier and train a boosting model on some randomly generated data. To see or change the inner learner, just double click the Boosting operator to open its subprocess.

Best regards,
Marius

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.1.014">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.1.014" expanded="true" name="Process">
    <process expanded="true" height="477" width="706">
      <operator activated="true" class="generate_data" compatibility="5.1.014" expanded="true" height="60" name="Generate Data" width="90" x="112" y="30">
        <parameter key="target_function" value="simple non linear classification"/>
        <parameter key="number_examples" value="1000"/>
      </operator>
      <operator activated="true" class="bayesian_boosting" compatibility="5.1.014" expanded="true" height="76" name="Bayesian Boosting" width="90" x="246" y="30">
        <process expanded="true" height="477" width="706">
          <operator activated="true" class="decision_tree" compatibility="5.1.014" expanded="true" height="76" name="Decision Tree" width="90" x="112" 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_op="Generate Data" from_port="output" to_op="Bayesian Boosting" to_port="training set"/>
      <connect from_op="Bayesian Boosting" from_port="model" 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>