title: Python scikit learn multi-class multi-label performance metrics?
body: <p>I ran Random Forest classifier for my multi-class multi-label output variable. I got below output.</p>
  
  <pre><code>My y_test values
  
  
       Degree  Nature
  762721       1       7                              
  548912       0       6
  727126       1      12
  14880        1      12
  189505       1      12
  657486       1      12
  461004       1       0
  31548        0       6
  296674       1       7
  121330       0      17
  
  
  predicted output :
  
  [[  1.   7.]
   [  0.   6.]
   [  1.  12.]
   [  1.  12.]
   [  1.  12.]
   [  1.  12.]
   [  1.   0.]
   [  0.   6.]
   [  1.   7.]
   [  0.  17.]]
  </code></pre>
  
  <p>Now I want to check the performance of my classifier. I found that for multiclass multilabel "Hamming loss or jaccard_similarity_score" is the good metrics. I tried to calculate it but I was getting value error.</p>
  
  <pre><code>Error:
  ValueError: multiclass-multioutput is not supported
  </code></pre>
  
  <p>Below line I tried:</p>
  
  <pre><code>print hamming_loss(y_test, RF_predicted)
  print jaccard_similarity_score(y_test, RF_predicted)
  </code></pre>
  
  <p>Thanks,</p>