title: Error: Classification metrics can't handle a mix of multiclass-multioutput and multilabel-indicator targets
body: <p>I am newbie to machine learning in general.</p>
  
  <p>I am trying to do multilabel text classification. I have the original labels for these documents as well as the result of the classification (used mlknn classifier) represented as one hot encoding (19000 document x 200 label). Now I am trying to evaluate the classification with f1_score micro and macro but I am getting this error (on line 3) <code>ValueError: Classification metrics can't handle a mix of multiclass-multioutput and multilabel-indicator targets</code> and I dont know how I can solve it. This is my code:</p>
  
  <pre><code>1. y_true = np.loadtxt("target_matrix.txt")
  2. y_pred = np.loadtxt("classification_results.txt")
  
  3. print (f1_score(y_true, y_pred, average='macro'))
  4. print (f1_score(y_true, y_pred, average='micro'))
  </code></pre>
  
  <p>I also tried to use <code>cross_val_score</code> for the classification to get the evaluation right away but ran into another error (from <code>cross_val_score</code> line):</p>
  
  <pre><code>File "_csparsetools.pyx", line 20, in scipy.sparse._csparsetools.lil_get1
  File "_csparsetools.pyx", line 48, in scipy.sparse._csparsetools.lil_get1
  IndexError: column index (11) out of bounds
  </code></pre>
  
  <p>this is my code:</p>
  
  <pre><code>X = np.loadtxt("docvecs.txt", delimiter=",")
  y = np.loadtxt("target_matrix.txt", dtype='int')
  
  cv_scores = []
  mlknn = MLkNN(k=10)  
  scores = cross_val_score(mlknn, X, y, cv=5, scoring='f1_micro')
  cv_scores.append(scores)
  </code></pre>
  
  <p>any help with either one of the errors is much appreciated, thanks.</p>