title: Confusion about Precision and Recall in Spam detection
body: <p>I' m doing a project to detect spam accounts according to a <a href="http://streamhacker.com/2010/05/17/text-classification-sentiment-analysis-precision-recall/" rel="nofollow">tutorial</a>. Two labels —— "Spam" and "Not spam" are used to train and test. Classification have been finished and I'm heading for evaluation.</p>
  
  <p>The results are:</p>
  
  <pre><code>*Spam* precision: 0.962917933131
  *Spam* recall: 0.6336
  
  *Not spam* precision: 0.72697466468
  *Not spam* recall: 0.9756
  </code></pre>
  
  <p>I've read the <a href="http://en.wikipedia.org/wiki/Precision_and_recall" rel="nofollow">wiki</a> of precision and recall, still confused and have no idea how to use it for measurement. </p>
  
  <p>My purpose is to reduce the number of <strong>Normal</strong> accounts which is labelled as <strong>"Spam"</strong>. It doesn't matter that some "Spam" accounts could escape. So I want to know which result above should I focus to improve? Thanks.</p>