title: Scikit-Learn: Label not x is present in all training examples
body: <p>I'm trying to do multilabel classification with SVM.
  I have nearly 8k features and also have y vector of length with nearly 400. I already have binarized Y vectors, so I didn't use <code>MultiLabelBinarizer()</code> but when I use it with my Y data's raw form, it still gives same thing.</p>
  
  <p>I'm running this code:</p>
  
  <pre><code>X = np.genfromtxt('data_X', delimiter=";")
  Y = np.genfromtxt('data_y', delimiter=";")
  training_X = X[:2600,:]
  training_y = Y[:2600,:]
  
  test_sample = X[2600:2601,:]
  test_result = Y[2600:2601,:]
  
  classif = OneVsRestClassifier(SVC(kernel='rbf'))
  classif.fit(training_X, training_y)
  print(classif.predict(test_sample))
  print(test_result)
  </code></pre>
  
  <p>After all fitting process when it comes to prediction part, it says <code>Label not x is present in all training examples</code> (x is a few different numbers in range of my y vector length which is 400). After that it gives predicted y vector which is always zero vector with length of 400(y vector length).
  I'm new at scikit-learn and also in machine learning. I couldn't figure out the problem here. What's the problem and what should I do to fix it?
  Thanks.</p>