Python scikit learn multi-class multi-label performance metrics?
<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>