title: Wrong F1-Score for resulting Precision and Recall in sklearn.metrics
body: <p>I used the sklearn.metrics.precision_recall_fscore_support() to output the evaluation of my classifier on a testset. I use average='macro'.
  The function results in</p>
  
  <p>precision = 0.153, recall = 0.144 and f1 = 0.122.</p>
  
  <p>As we can see the f1-score is wrong for the resulting precision and recall (if I'm not mistaken). If we take the formula for f1 = 2 * p * r / (p + r), the f1-score should be about 0.148.</p>
  
  <p>Does anyone know whether I'm wrong or why the function outputs that score?</p>