title: How does c5.0() choose decision and root nodes?
body: <p>I am learning Decision Tree method for machine learning. Right now, the most important piece of code I use is <code>c5. 0</code>. Got to admit, it is a genius' work. But i couldn't understand how it chooses the root and decision nodes.
  Example: I have a database named 'credit'. here is first few columns:</p>
  <pre><code> str(credit)
      'data.frame':   1000 obs. of  21 variables:
   $ checking_balance    : Factor w/ 4 levels &quot;&lt; 0 DM&quot;,&quot;&gt; 200 DM&quot;,..: 1 3 4 1 1 4 4 3 4 3 ...
   $ months_loan_duration: int  6 48 12 42 24 36 24 36 12 30 ...
   $ credit_history      : Factor w/ 5 levels &quot;critical&quot;,&quot;delayed&quot;,..: 1 5 1 5 2 5 5 5 5 1 ...
   $ purpose             : Factor w/ 10 levels &quot;business&quot;,&quot;car (new)&quot;,..: 8 8 5 6 2 5 6 3 8 2 ...
   $ amount              : int  1169 5951 2096 7882 4870 9055 2835 6948 3059 5234 ...
  </code></pre>
  <p>So when i look at the decision tree after having applied c5.0, i see that the root node is <code>$cheking balance</code>, then the next decision node is <code>$credit_history.</code>
  What is the strategy or the trajectory c5.0 follows when creating a decision tree? In other words, how does it determine the order of decision nodes?</p>