title: sci-kit learn output interpretation
body: <p>When using sklearn, I sometimes have issues correctly assigning the output to the right label. When calling different methods on the result of a fit, sklearn only returns numpy arrays with no further labeling. For example, fitting a simple LDA that is trying to classify into two different groups will give me this output.</p>
  
  <pre><code>result = sklearn_lda.fit(X_train, y_train)
  
  print "Prior probabilities are: \n", result.priors_
  print "Group means are: \n", result.means_
  </code></pre>
  
  <p><em>Output</em></p>
  
  <pre><code>Prior probabilities are: 
  [0.49198397 0.50801603]
  
  Group means are: 
  [[ 0.04279022  0.03389409]
   [-0.03954635 -0.03132544]]
  </code></pre>
  
  <p>How do I know which prior probability is associated with which class label? Same with the group means. For coefficients I know that sklearn outputs them in the same order as they are put in. In this case I am a little confused. </p>