# Buggy code
def is_label_indicator_matrix(y):
    """Check if ``y`` is in the label indicator matrix format (multilabel).

    Parameters
    ----------
    y : numpy array of shape [n_samples] or sequence of sequences
        Target values. In the multilabel case the nested sequences can
        have variable lengths.

    Returns
    -------
    out : bool,
        Return ``True``, if ``y`` is in a label indicator matrix format,
        else ``False``.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.utils.multiclass import is_label_indicator_matrix
    >>> is_label_indicator_matrix([0, 1, 0, 1])
    False
    >>> is_label_indicator_matrix([[1], [0, 2], []])
    False
    >>> is_label_indicator_matrix(np.array([[1, 0], [0, 0]]))
    True
    >>> is_label_indicator_matrix(np.array([[1], [0], [0]]))
    False
    >>> is_label_indicator_matrix(np.array([[1, 0, 0]]))
    True

    """
    if not (hasattr(y, "shape") and y.ndim == 2 and y.shape[1] > 1):
        return False
    labels = np.unique(y)
    return len(labels) <= 2 and (
        y.dtype.kind in "biu"  # bool, int, uint
        or _is_integral_float(labels)
    )


# Traceback (IndexError)
======================================================================
ERROR: sklearn.tests.test_common.test_regressors_train('RANSACRegressor', <class 'sklearn.linear_model.ransac.RANSACRegr
essor'>, array([[-0.44836249, -0.47282444, -1.20608008, ..., -0.75500806,
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
self.test(*self.arg)
File "C:\Python27\lib\site-packages\sklearn\tests\test_common.py", line 850, in check_regressors_train
assert_raises(ValueError, regressor.fit, X, y[:-1])
File "C:\Python27\lib\unittest\case.py", line 473, in assertRaises
callableObj(*args, **kwargs)
File "C:\Python27\lib\site-packages\sklearn\linear_model\ransac.py", line 261, in fit
y_subset = y[subset_idxs]
IndexError: index 199 is out of bounds for axis 0 with size 199

# Fixed code
def is_label_indicator_matrix(y):
    """Check if ``y`` is in the label indicator matrix format (multilabel).

    Parameters
    ----------
    y : numpy array of shape [n_samples] or sequence of sequences
        Target values. In the multilabel case the nested sequences can
        have variable lengths.

    Returns
    -------
    out : bool,
        Return ``True``, if ``y`` is in a label indicator matrix format,
        else ``False``.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.utils.multiclass import is_label_indicator_matrix
    >>> is_label_indicator_matrix([0, 1, 0, 1])
    False
    >>> is_label_indicator_matrix([[1], [0, 2], []])
    False
    >>> is_label_indicator_matrix(np.array([[1, 0], [0, 0]]))
    True
    >>> is_label_indicator_matrix(np.array([[1], [0], [0]]))
    False
    >>> is_label_indicator_matrix(np.array([[1, 0, 0]]))
    True

    """
    if not (hasattr(y, "shape") and y.ndim == 2 and y.shape[1] > 1):
        return False

    if issparse(y):
        if isinstance(y, (dok_matrix, lil_matrix)):
            y = y.tocsr()
        return (
            len(y.data) == 0
            or np.ptp(y.data) == 0
            and (
                y.dtype.kind in "biu"  # bool, int, uint
                or _is_integral_float(np.unique(y.data))
            )
        )
    else:
        labels = np.unique(y)

        return len(labels) < 3 and (
            y.dtype.kind in "biu"  # bool, int, uint
            or _is_integral_float(labels)
        )