For the moment, this raised an error, because it is an ambiguous case. Either it is a multilabel output in binary indicator format or a binary classification problem.

```
In [4]: import numpy as np

In [5]: accuracy_score(np.array([[0, 1, 2]]) ,  np.array([[0], [1], [2]]))
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-5-c70daa59450a> in <module>()
----> 1 accuracy_score(np.array([[0, 1, 2]]) ,  np.array([[0], [1], [2]]))

/home/ajoly/git/scikit-learn/sklearn/metrics/metrics.pyc in accuracy_score(y_true, y_pred, normalize)
    932 
    933     """
--> 934     y_true, y_pred = check_arrays(y_true, y_pred, allow_lists=True)
    935 
    936     # Compute accuracy for each possible representation

/home/ajoly/git/scikit-learn/sklearn/utils/validation.pyc in check_arrays(*arrays, **options)
    192         if size != n_samples:
    193             raise ValueError("Found array with dim %d. Expected %d"
--> 194                              % (size, n_samples))
    195 
    196         if not allow_lists or hasattr(array, "shape"):

ValueError: Found array with dim 3. Expected 1

```