title: ValueError: error_score must be the string 'raise' or a numeric value. (Hint: if using 'raise', please make sure that it has been spelled correctly.)
body: <p>I am working on decision tree regression in Python.</p>
  <p>Got a weird error:</p>
  <pre><code>ValueError: error_score must be the string 'raise' or a numeric value. (Hint: if using 'raise', please make sure that it has been spelled correctly.)
  </code></pre>
  <p>My data frame has 330000 * 91 columns approximately.</p>
  <p>My code looks like this:</p>
  <pre><code># Read data
  y = my_train2.iloc[:,0]
  x = my_train2.iloc[:, 1:93]
  
  (x_train, x_test, y_train, y_test) = cv.train_test_split(x, y, test_size=.20)
  # Instantiate dt
  dt = DecisionTreeRegressor(# max_depth = 8,
                         min_samples_leaf = 1,
                         random_state = 1)
  
  # Fit dt to the training set
  dt.fit(x_train, y_train)
  </code></pre>
  <p>This chunk below give an error</p>
  <pre><code>from sklearn.model_selection import cross_val_score
  # Compute the array containing the 10-folds CV MSEs
  MSE_CV_scores = - cross_val_score(dt, x_train, y_train, cv=10, 
                                scoring='neg_mean_squared_error', 
                                n_jobs=-1) 
  
  # Compute the 10-folds CV RMSE
  RMSE_CV = (MSE_CV_scores.mean())**(1/2)
  
  # Print RMSE_CV
  print('CV RMSE: {:.2f}'.format(RMSE_CV))
  </code></pre>
  <p>I can print here the class of each column if necessary.</p>
  <p>Thanks!</p>