title: Determine importance of individual variables in WEKA
body: <p>I am trying to determine the importance of individual variables in the WEKA implementation of an LMT(Logistic Model Trees) DT (Decision Tree).</p>
  
  <p>I would like to know the contribution that each individual variable has in a classification task, hence the need to determine the importance of each individual variable. This is to conduct a more in depth analysis of my results.</p>
  
  <p>I have already looked into the "select attributes" tab, and corresponding algorithms (i.e. Principal Components, Info Gain, Ranker, etc.); however, these algorithms provide information regarding which combinatons or ranks of variables will contribute to the best (or most effective, or quickest, depending on your end goal, classifier). </p>
  
  <p>However, I am not interested in ranking or selecting the most significant variables. I am interested in determining how much (in percentage form, for example) each variable contributed to the final classification score of my DT. </p>
  
  <p>I have contemplated removing each variable one by one to determine how the score changes; but I  am not sure that this can be manually done as the final score may depend on some underlying correlations, which is why I want to make this determination using all of the variables together (even if the contribution of one is zero).</p>
  
  <p>So, the question is: Is there a way to measure the contribution of each INDIVIDUAL variable used in a classifier (even if that contribution is zero)?</p>
  
  <p>Thanks in advance for any assistance.</p>