title: KNeighborsClassifier query data dimension must match training data dimension
body: <p>I'm trying to find the training error on a model across different values for k. </p>
  
  <p>Here is my code:</p>
  
  <pre><code>x_eq = np.array(equal_scales[['x1','x2']])
  y_eq = np.array(equal_scales[['category']])
  y_eq = y_eq.ravel()
  
  train_error_list = []
  for i in range(30):
      i = i+1
      knn4 = KNeighborsClassifier(n_neighbors=i)
      knn4.fit(x_eq, y_eq)
      #evaluate accuracy training-error
      train_accuracy = knn2.score(x_eq, y_eq)
      train_error = 1 - train_accuracy
      train_error_list.append(train_error)
  </code></pre>
  
  <p><code>x_eq</code> is a numpy array with shape (100,2) looking like this: </p>
  
  <pre><code>array([[ -1.33255739e-01,  -1.68917051e+00],
         [  1.22342923e+00,  -4.58386227e-01],
         [ -7.50266300e-01,   1.84619283e-01],
         [ -5.75475318e-01,   7.98265338e-01],
         [ -2.59622198e-01,  -8.56879897e-01],
         [ -4.54110670e-01,   1.36136079e+00],
         [  6.87128005e-01,  -1.56213731e+00],
         [ -8.52919125e-01,  -2.15160667e-01],
         [  1.08863420e+00,   1.88649799e+00],
         [ -6.08777374e-05,   1.43126563e+00],
         [ -4.00791901e-01,   2.06030370e-01],
     [  7.36267212e-01,   2.29608975e-01],
  </code></pre>
  
  <p>y_eq is a numpy array with shape (100,) looking like this:</p>
  
  <pre><code>array(['B', 'C', 'B', 'B', 'B', 'B', 'C', 'B', 'C', 'B', 'B', 'C', 'B',
         'C', 'B', 'D', 'B', 'B', 'A', 'B', 'A', 'D', 'B', 'A', 'B', 'C',
         'B', 'B', 'B', 'C', 'B', 'C', 'B', 'B', 'C', 'B', 'B', 'B', '
  </code></pre>
  
  <p>I get the following error message: </p>
  
  <p><code>ValueError: query data dimension must match training data dimension</code></p>
  
  <p>I have fit a very similar model earlier and I am not sure how this one is different from my previous tests. Thank you for your help!</p>