title: How to solve "ValueError: Supported target types are: ('binary', 'multiclass'). Got 'multiclass-multioutput' instead." error in pyton 3.7
body: <p>I want to create a program to do emotion classification using TF-IDF and SVM. Before classify the data, I have to split a dataset into data training and testing using stratified KFold. I've used the numpy array to store the texts (X) and labels (Y)</p>
  <p>But it ended up with this error:</p>
  <pre><code>ValueError: Supported target types are: ('binary', 'multiclass'). Got 'multiclass-multioutput' instead'
  </code></pre>
  <hr />
  <p>this code is running on python 3.7:</p>
  <pre><code>labels = []
  
  with open(path, encoding='utf-8') as in_file:
      data = csv.reader(in_file)
      for line in data:
          labels.append(line[1])
  
  label_np = np.array(labels)
  lp = label_np.reshape(20,20)
  # lp = label_np.transpose(0)
  # print(lp)
  
  result_preprocess_np = np.array(result_preprocess)
  hp = result_preprocess_np.reshape(20,20)
  
  model = LinearSVC(multi_class='crammer_singer')
  total_svm = []
  
  total_mat_svm = np.zeros((20,20))
  
  kf = StratifiedKFold(n_splits=3)
  kf.get_n_splits(hp, lp)
  
  for train_index, test_index in kf.split(hp, lp):
      # print('Train : ', test_index, 'Test : ', test_index)
      x_train, x_test = hp[train_index], hp[test_index]
      y_train, y_test = lp[train_index], lp[test_index]
  
  
  vectorizer = TfidfVectorizer(min_df=5,
                               max_df=0.8,
                               sublinear_tf=True,
                               use_idf=True)
  train_vector = vectorizer.fit_transform(x_train)
  test_vector = vectorizer.transform(x_test)
  
  model.fit(x_train, y_train)
  result_svm = model.score(x_test, y_test)
  print(result_svm)
  </code></pre>
  <p>I expect the result is the accuracy of classification.</p>