title: ValueError: Unknown label type: 'continuous-multioutput' when fitting data
body: <p>I want to predict multiple outcomes based on one input parameter with <code>scikit-learn</code>'s <code>MultiOutputClassifier</code>.
  For some reason I always get this error, and I do not know whats the problem:</p>
  
  <p><code>ValueError: Unknown label type: 'continuous-multioutput'</code></p>
  
  <p>I have tried to make <code>my_data['Clicked']</code> to be categorical data, I have tried this <code>my_data['Clicked'] = my_data['Clicked'].astype('category')</code>, but it gives me the same error.</p>
  
  <p>I have tried the same code on some simple dummy database and it works perfectly.
  This is the code that works:</p>
  
  <pre><code>from sklearn.multioutput import MultiOutputClassifier
  from sklearn.linear_model import LogisticRegression
  
  dic = {'par_1': [10, 30, 13, 19, 25, 33, 23],
         'par_2': [1, 3, 1, 2, 3, 3, 2],
         'outcome': [101, 905, 182, 268, 646, 624, 465]}
  
  df = pd.DataFrame(dic)
  
  variables = df.iloc[:,:-1]
  results = df.iloc[:,-1]
  
  multi_output_clf = MultiOutputClassifier(LogisticRegression(solver='lbfgs'))
  multi_output_clf.fit(results.values.reshape(-1, 1),variables)
  
  x = multi_output_clf.predict([[100]])
  print(x)
  </code></pre>
  
  <p>For the code above, everything works, but for the code below, i get the error.
  I do not know whats the problem because i have just used bigger data set and values based on which I predict the parameters are only 0's and 1's. Zeros and Ones should be classes(categories) like <code>yes</code> and <code>no</code> but if i change them to 'yes' and 'no'i get the error that <code>cannot convert string to float</code>.  Why this is not continuous 'outcome': [101, 905, 182, 268, 646, 624, 465] but series of 0's and 1's are continuous?</p>
  
  <pre><code>from sklearn.multioutput import MultiOutputClassifier
  from sklearn.linear_model import LogisticRegression
  
  variables = my_data[['Clicked']] #values are integers, only 0 and 1 (0 = not clicked , 1 = clicked)
  results = my_data[['Daily Time on Site', 'Age', 'Gender']] #values are integers and floats
  
  multi_output_clf = MultiOutputClassifier(LogisticRegression(solver='lbfgs'))
  multi_output_clf.fit(variables.values.reshape(-1, 1),results)
  
  x = multi_output_clf.predict([1])
  print(x)
  </code></pre>
  
  <p>Below is the part of full dataset that I have used (it gives me the same error):  </p>
  
  <pre><code>dic = {'Daily Time on Site': [59.99, 88.91, 66.00, 74.53, 69.88, 47.64, 83.07, 69.57],
         'Age': [23,33,48,30,20,49,37,48],
         'Gender': [1, 0, 1, 1, 1, 0, 1, 1],
         'Clicked': [0, 0, 1, 0, 0, 1, 0, 1]}
  
  my_data = pd.DataFrame(dic)
  
  variables = my_data[['Clicked']] #values are only 0 and 1 (0 = not clicked , 1 = clicked)
  results = my_data[['Daily Time on Site', 'Age', 'Gender']] #values are integers and floats
  
  multi_output_clf = MultiOutputClassifier(LogisticRegression(solver='lbfgs', multi_class='ovr'))
  multi_output_clf.fit(variables.values.reshape(-1, 1),results)
  
  x = multi_output_clf.predict([1])
  print(x)
  </code></pre>