Decision Trees

Hi,

a decision tree is usually not build until a leaf is pure, but the process is either stopped (because of pre-pruning) or branches are cutted away afterwards because of pruning. Thus you will get leafs with contributions of different classes. This distribution is displayed in the color bars right at the leafs. You can see how many examples of a specific class are in that leaf. This numbers are by the way used for the confidence score.
i assume that you worked on a data set with a lot of classes in your label attribute. In this case it can look a bit messy. I would recommend running it on a problem with just 2 classes. Then you can see the results.
Attached is a process using the sonar data set which should be helpful.

~Martin

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="6.4.000">
 <context>
   <input/>
   <output/>
   <macros/>
 </context>
 <operator activated="true" class="process" compatibility="6.4.000" expanded="true" name="Process">
   <process expanded="true">
     <operator activated="true" class="retrieve" compatibility="6.4.000" expanded="true" height="60" name="Retrieve Sonar" width="90" x="179" y="120">
       <parameter key="repository_entry" value="//Samples/data/Sonar"/>
     </operator>
     <operator activated="true" class="parallel_decision_tree" compatibility="6.4.000" expanded="true" height="76" name="Decision Tree" width="90" x="380" y="120"/>
     <connect from_op="Retrieve Sonar" from_port="output" to_op="Decision Tree" to_port="training set"/>
     <connect from_op="Decision Tree" 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>