title: python code that was working stopped working
body: <p>this was working fine </p>
  
  <p>cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)</p>
  
  <pre><code>    import pandas
      from pandas.plotting import scatter_matrix
      import matplotlib.pyplot as plt
      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 GaussianNB
      from sklearn.svm import SVC
  
      # Load dataset (contains floats and one boolean)
      url = "\\File\\Path.csv"
      names = ['Headers', 'Here', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'T/F']
      dataset = pandas.read_csv(url, names=names)
  
      # Split-out validation dataset
      array = dataset.values
      X = array[:,0:12]
      Y = array[:,12]
      validation_size = 0.10
      seed = 7
      X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)
  
      # Test options and evaluation metric
      seed = 7
      scoring = 'accuracy'
  
      # Spot check algorithms
      models = []
      models.append(('LR', LogisticRegression()))
      models.append(('LDA', LinearDiscriminantAnalysis()))
      models.append(('KNN', KNeighborsClassifier()))
      models.append(('CART', DecisionTreeClassifier()))
      models.append(('NB', GaussianNB()))
      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)
  
      # Compare Algorithms
      fig = plt.figure()
      fig.suptitle('Algorithm Comparison')
      ax = fig.add_subplot(111)
      plt.boxplot(results)
      ax.set_xticklabels(names)
      plt.show()
  
  </code></pre>
  
  <p>it stops when it reaches this part </p>
  
  <p>cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)</p>
  
  <pre><code>
      Warning (from warnings module):
        File "C:\Python\Python37-32\lib\site-packages\sklearn\linear_model\logistic.py", line 433
          FutureWarning)
      FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
  
      Warning (from warnings module):
        File "C:\Python\Python37-32\lib\site-packages\sklearn\model_selection\_validation.py", line 542
          FutureWarning)
      FutureWarning: From version 0.22, errors during fit will result in a cross validation score of NaN by default. Use error_score='raise' if you want an exception raised or error_score=np.nan to adopt the behavior from version 0.22.
      Traceback (most recent call last):
        File "/test.py", line 46, in &lt;module&gt;
          cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
        File "C:\Python\Python37-32\lib\site-packages\sklearn\model_selection\_validation.py", line 402, in cross_val_score
          error_score=error_score)
        File "C:\Python\Python37-32\lib\site-packages\sklearn\model_selection\_validation.py", line 240, in cross_validate
          for train, test in cv.split(X, y, groups))
        File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\parallel.py", line 917, in __call__
          if self.dispatch_one_batch(iterator):
        File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\parallel.py", line 759, in dispatch_one_batch
          self._dispatch(tasks)
        File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\parallel.py", line 716, in _dispatch
          job = self._backend.apply_async(batch, callback=cb)
        File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 182, in apply_async
          result = ImmediateResult(func)
        File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 549, in __init__
          self.results = batch()
        File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\parallel.py", line 225, in __call__
          for func, args, kwargs in self.items]
        File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\parallel.py", line 225, in &lt;listcomp&gt;
          for func, args, kwargs in self.items]
        File "C:\Python\Python37-32\lib\site-packages\sklearn\model_selection\_validation.py", line 528, in _fit_and_score
          estimator.fit(X_train, y_train, **fit_params)
        File "C:\Python\Python37-32\lib\site-packages\sklearn\linear_model\logistic.py", line 1289, in fit
          check_classification_targets(y)
        File "C:\Python\Python37-32\lib\site-packages\sklearn\utils\multiclass.py", line 171, in check_classification_targets
          raise ValueError("Unknown label type: %r" % y_type)
      ValueError: Unknown label type: 'unknown'
  
  </code></pre>
  
  <p>it runs fine until it reaches</p>
  
  <p>cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)</p>