title: What is the difference between evaluation metrics and features in relation to binary classification?
body: <p>I'm having a hard time trying to separate these two concepts in my mind.  </p>
  
  <p>I know that evaluation metrics such as <a href="http://en.wikipedia.org/wiki/BLEU" rel="nofollow">BLEU</a> can be used to measure the quality of a given input against a reference (as in Machine Translation).  But can this score be leveraged into classifying sentences into two categories? For example, a sentence with a certain evaluation metric score above 0.50 would be given a 'yes' while everything below 0.50 given a 'no'.  </p>
  
  <p>Also, it this possibly related to features used in machine learning algorithms? For example, say the phrase, "in the past" is a possible feature of the data which then could be used to classify inputs into having this feature or not.</p>