title: ValueError: Unknown label type: 'continuous'
body: <p>I've seen other posts talking about this but anyone of these can help me. I am using jupyter notebook with Python 3.6.0 on windows x6 machine. 
  I have a large dataset but I keep only a piece of it to run my models: </p>
  
  <p>This is a piece of code that i used:  </p>
  
  <pre><code>df = loan_2.reindex(columns= ['term_clean','grade_clean', 'annual_inc', 'loan_amnt', 'int_rate','purpose_clean','installment','loan_status_clean'])
  df.fillna(method= 'ffill').astype(int)
  from sklearn.preprocessing import Imputer
  from sklearn.preprocessing import StandardScaler
  imp = Imputer(missing_values='NaN', strategy='median', axis=0)
  array = df.values
  y = df['loan_status_clean'].values
  imp.fit(array)
  array_imp = imp.transform(array)
  
  y2= y.reshape(1,-1)
  imp.fit(y2)
  y_imp= imp.transform(y2)
  X = array_imp[:,0:4]
  Y = array_imp[:,4]
  validation_size = 0.20
  seed = 7
  X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)
  seed = 7
  scoring = 'accuracy'
  
  from sklearn import model_selection
  from sklearn.metrics import classification_report
  from sklearn.metrics import confusion_matrix
  from sklearn.metrics import accuracy_score
  from sklearn.linear_model import LogisticRegression
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
  from sklearn.naive_bayes import  BernoulliNB
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.svm import SVC
  from sklearn.ensemble import AdaBoostClassifier
  from sklearn.neural_network import MLPClassifier
  
  # Spot Check Algorithms
  models = []
  models.append(('LR', LogisticRegression()))
  models.append(('LDA', LinearDiscriminantAnalysis()))
  models.append(('KNN', KNeighborsClassifier()))
  models.append(('CART', DecisionTreeClassifier()))
  models.append(('BNB', BernoulliNB()))
  models.append(('RF', RandomForestClassifier()))
  models.append(('GBM', AdaBoostClassifier()))
  models.append(('NN', MLPClassifier()))
  models.append(('SVM', SVC()))
  
  # evaluate each model in turn
  results = []
  names = []
  for name, model in models:
      kfold = model_selection.KFold(n_splits=10, random_state=seed)
      cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
      results.append(cv_results)
      names.append(name)
      msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
      print(msg)
  </code></pre>
  
  <p>When I run the last one piece of code this error comes up:</p>
  
  <hr>
  
  <pre><code>ValueError                                Traceback (most recent call last)
  &lt;ipython-input-262-1e6860ba615b&gt; in &lt;module&gt;()
        4 for name, model in models:
        5         kfold = model_selection.KFold(n_splits=10, random_state=seed)
  ----&gt; 6         cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
        7         results.append(cv_results)
        8         names.append(name)
  
  C:\Users\dalila\Anaconda\lib\site-packages\sklearn\model_selection\_validation.py in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)
      138                                               train, test, verbose, None,
      139                                               fit_params)
  --&gt; 140                       for train, test in cv_iter)
      141     return np.array(scores)[:, 0]
      142 
  
  C:\Users\dalila\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable)
      756             # was dispatched. In particular this covers the edge
      757             # case of Parallel used with an exhausted iterator.
  --&gt; 758             while self.dispatch_one_batch(iterator):
      759                 self._iterating = True
      760             else:
  
  C:\Users\dalila\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py in dispatch_one_batch(self, iterator)
      606                 return False
      607             else:
  --&gt; 608                 self._dispatch(tasks)
      609                 return True
      610 
  
  C:\Users\dalila\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py in _dispatch(self, batch)
      569         dispatch_timestamp = time.time()
      570         cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
  --&gt; 571         job = self._backend.apply_async(batch, callback=cb)
      572         self._jobs.append(job)
      573 
  
  C:\Users\dalila\Anaconda\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in apply_async(self, func, callback)
      107     def apply_async(self, func, callback=None):
      108         """Schedule a func to be run"""
  --&gt; 109         result = ImmediateResult(func)
      110         if callback:
      111             callback(result)
  
  C:\Users\dalila\Anaconda\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in __init__(self, batch)
      324         # Don't delay the application, to avoid keeping the input
      325         # arguments in memory
  --&gt; 326         self.results = batch()
      327 
      328     def get(self):
  
  C:\Users\dalila\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self)
      129 
      130     def __call__(self):
  --&gt; 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
      132 
      133     def __len__(self):
  
  C:\Users\dalila\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py in &lt;listcomp&gt;(.0)
      129 
      130     def __call__(self):
  --&gt; 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
      132 
      133     def __len__(self):
  
  C:\Users\dalila\Anaconda\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)
      236             estimator.fit(X_train, **fit_params)
      237         else:
  --&gt; 238             estimator.fit(X_train, y_train, **fit_params)
      239 
      240     except Exception as e:
  
  C:\Users\dalila\Anaconda\lib\site-packages\sklearn\linear_model\logistic.py in fit(self, X, y, sample_weight)
     1172         X, y = check_X_y(X, y, accept_sparse='csr', dtype=np.float64,
     1173                          order="C")
  -&gt; 1174         check_classification_targets(y)
     1175         self.classes_ = np.unique(y)
     1176         n_samples, n_features = X.shape
  
  C:\Users\dalila\Anaconda\lib\site-packages\sklearn\utils\multiclass.py in check_classification_targets(y)
      170     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',
      171             'multilabel-indicator', 'multilabel-sequences']:
  --&gt; 172         raise ValueError("Unknown label type: %r" % y_type)
      173 
      174 
  
  ValueError: Unknown label type: 'continuous'
  </code></pre>
  
  <p>Brief assumption: my data are clean from NaN and Missing Value in general. </p>