title: log_loss in sklearn: Multioutput target data is not supported with label binarization
body: <p>The following code</p>
  
  <pre><code>from sklearn import metrics
  import numpy as np
  y_true = np.array([[0.2,0.8,0],[0.9,0.05,0.05]])
  y_predict = np.array([[0.5,0.5,0.0],[0.5,0.4,0.1]])
  metrics.log_loss(y_true, y_predict)
  </code></pre>
  
  <p>produces the following error:</p>
  
  <pre><code>   ---------------------------------------------------------------------------
  ValueError                                Traceback (most recent call last)
  &lt;ipython-input-32-24beeb19448b&gt; in &lt;module&gt;()
  ----&gt; 1 metrics.log_loss(y_true, y_predict)
  
  ~\AppData\Local\conda\conda\envs\tensorflow\lib\site-packages\sklearn\metrics\classification.py in log_loss(y_true, y_pred, eps, normalize, sample_weight, labels)
     1646         lb.fit(labels)
     1647     else:
  -&gt; 1648         lb.fit(y_true)
     1649 
     1650     if len(lb.classes_) == 1:
  
  ~\AppData\Local\conda\conda\envs\tensorflow\lib\site-packages\sklearn\preprocessing\label.py in fit(self, y)
      276         self.y_type_ = type_of_target(y)
      277         if 'multioutput' in self.y_type_:
  --&gt; 278             raise ValueError("Multioutput target data is not supported with "
      279                              "label binarization")
      280         if _num_samples(y) == 0:
  
  ValueError: Multioutput target data is not supported with label binarization
  </code></pre>
  
  <p>I am curious why. I am trying to re-read definition of log loss and cannot find anything that would make computations incorrect.</p>