title: ValueError: continuous-multioutput is not supported
body: <p>I want to run several regression types (Lasso, Ridge, ElasticNet and SVR) on a dataset with around 5,000 rows and 6 features. Linear regression. Use GridSearchCV for cross validation. The code is extensive but here are some critical parts:</p>
  
  <pre><code>def splitTrainTestAdv(df):
  
      y = df.iloc[:,-5:]  # last 5 columns
      X = df.iloc[:,:-5]  # Except for last 5 columns
  
  
      #Scaling and Sampling
  
      X = StandardScaler().fit_transform(X)
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.8, random_state=0)
  
  
  return X_train, X_test, y_train, y_test
  
  def performSVR(x_train, y_train, X_test, parameter):
  
  
  
      C = parameter[0]
      epsilon = parameter[1] 
      kernel = parameter[2]
  
      model = svm.SVR(C = C, epsilon = epsilon, kernel = kernel)
      model.fit(x_train, y_train)
  
  
  
  return model.predict(X_test)  #prediction for the test
  
  def performRidge(X_train, y_train, X_test, parameter):
  
      alpha = parameter[0]
  
      model = linear_model.Ridge(alpha=alpha, normalize=True) 
      model.fit(X_train, y_train)
  
  
  
  return model.predict(X_test)  #prediction for the test
  
  MODELS = {
      'lasso': (
          linear_model.Lasso(),
          {'alpha': [0.95]}
      ),
      'ridge': (
          linear_model.Ridge(),
          {'alpha': [0.01]}
          ),
      )
  }
  
  
  def performParameterSelection(model_name, feature, X_test, y_test, X_train, y_train):
  
  
      print("# Tuning hyper-parameters for %s" % feature)
      print()
  
      model, param_grid = MODELS[model_name]
      gs = GridSearchCV(model, param_grid, n_jobs= 1, cv=5, verbose=1, scoring='%s_weighted' % feature)
  
  
      gs.fit(X_train, y_train) 
  
  
      print("Best parameters set found on development set:")
  
      print(gs.best_params_)
      print()
      print("Grid scores on development set:")
      print()
      for params, mean_score, scores in gs.grid_scores_:
          print("%0.3f (+/-%0.03f) for %r"
            % (mean_score, scores.std() * 2, params))
  
      print("Detailed classification report:")
      print()
      print("The model is trained on the full development set.")
      print("The scores are computed on the full evaluation set.")
  
      y_true, y_pred = y_test, gs.predict(X_test)
      print(classification_report(y_true, y_pred))
  
  soil = pd.read_csv('C:/training.csv', index_col=0)
  soil = getDummiedSoilDepth(soil)
  np.random.seed(2015)
  soil = shuffleData(soil)
  soil = soil.drop('Depth', 1)
  
  X_train, X_test, y_train, y_test = splitTrainTestAdv(soil)
  
  
  scores = ['precision', 'recall']
  
  for score in scores:
  
  
  
  
      for model in MODELS.keys():
  
          print '####################'
          print model, score
          print '####################'
          performParameterSelection(model, score, X_test, y_test, X_train, y_train)
  </code></pre>
  
  <p>You can assume that all required imports are done</p>
  
  <p>I am getting this error and do not know why:</p>
  
  <pre><code>ValueError                                Traceback (most recent call last)
  </code></pre>
  
  <p> in ()
           18         print model, score
           19         print '####################'
      ---> 20         performParameterSelection(model, score, X_test, y_test, X_train, y_train)
           21 </p>
  
  <pre><code>&lt;ipython-input-27-304555776e21&gt; in performParameterSelection(model_name,  feature, X_test, y_test, X_train, y_train)
       12     # cv=5 - constant; verbose - keep writing
       13 
  ---&gt; 14     gs.fit(X_train, y_train) # Will get grid scores with outputs from ALL models described above
       15 
       16         #pprint(sorted(gs.grid_scores_, key=lambda x: -x.mean_validation_score))
  
  C:\Users\Tony\Anaconda\lib\site-packages\sklearn\grid_search.pyc in fit(self, X, y)
  
  C:\Users\Tony\Anaconda\lib\site-packages\sklearn\metrics\classification.pyc in _check_targets(y_true, y_pred)
       90     if (y_type not in ["binary", "multiclass", "multilabel-indicator",
       91                        "multilabel-sequences"]):
  ---&gt; 92         raise ValueError("{0} is not supported".format(y_type))
       93 
       94     if y_type in ["binary", "multiclass"]:
  
  ValueError: continuous-multioutput is not supported
  </code></pre>
  
  <p>I am still very new to Python and this error puzzles me. This should not because I have 6 features, of course. I tried to follow standard buil-in functions.</p>
  
  <p>Please, help</p>