title: AssertionError is raised when running an example from sklearn library
body: <pre><code>import pandas as pd
  import numpy as np
  from sklearn.learning_curve import learning_curve
  import matplotlib.pyplot as plt
  
  
  def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
                          n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
      """
      Generate a simple plot of the test and traning learning curve.
  
      Parameters
      ----------
      estimator : object type that implements the "fit" and "predict" methods
          An object of that type which is cloned for each validation.
  
      title : string
          Title for the chart.
  
      X : array-like, shape (n_samples, n_features)
          Training vector, where n_samples is the number of samples and
          n_features is the number of features.
  
      y : array-like, shape (n_samples) or (n_samples, n_features), optional
          Target relative to X for classification or regression;
          None for unsupervised learning.
  
      ylim : tuple, shape (ymin, ymax), optional
          Defines minimum and maximum yvalues plotted.
  
      cv : integer, cross-validation generator, optional
          If an integer is passed, it is the number of folds (defaults to 3).
          Specific cross-validation objects can be passed, see
          sklearn.cross_validation module for the list of possible objects
  
      n_jobs : integer, optional
          Number of jobs to run in parallel (default 1).
      """
      plt.figure()
      plt.title(title)
      if ylim is not None:
          plt.ylim(*ylim)
      plt.xlabel("Training examples")
      plt.ylabel("Score")
      train_sizes, train_scores, test_scores = learning_curve(
          estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
      train_scores_mean = np.mean(train_scores, axis=1)
      train_scores_std = np.std(train_scores, axis=1)
      test_scores_mean = np.mean(test_scores, axis=1)
      test_scores_std = np.std(test_scores, axis=1)
      plt.grid()
  
      plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
                       train_scores_mean + train_scores_std, alpha=0.1,
                       color="r")
      plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
                       test_scores_mean + test_scores_std, alpha=0.1, color="g")
      plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
               label="Training score")
      plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
               label="Cross-validation score")
  
      plt.legend(loc="best")
      return plt
  
  
  
  forest = ensemble.RandomForestClassifier(bootstrap=True, class_weight=None, max_depth=None, max_features='auto', max_leaf_nodes=None,min_samples_leaf=1, min_samples_split=6,min_weight_fraction_leaf=0.0, n_estimators=300, n_jobs=-1,oob_score=False, random_state=111, verbose=0, warm_start=False)
  
  cv = cross_validation.ShuffleSplit(alldata.shape[0], n_iter=10,
                                     test_size=0.2, random_state=0)
  
  title = "Learning Curve (Random Forest)"
  plot_learning_curve(forest, title, alldata, y, ylim=None, cv=cv, n_jobs=-1)
  
  plt.show()
  </code></pre>
  
  <p>When I run this code in IPython Notebook (Python 2.7), the following error can be seen from <code>cmd</code>. I took the function <code>plot_learning_curve</code> from <a href="http://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html" rel="nofollow noreferrer">the following website</a>.</p>
  
  <p><a href="https://i.stack.imgur.com/Bw2X3.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Bw2X3.jpg" alt="enter image description here"></a></p>