# Buggy code
def fit_transform(self, y):
    """Fit the label sets binarizer and transform the given label sets

    Parameters
    ----------
    y : iterable of iterables
        A set of labels (any orderable and hashable object) for each
        sample. If the `classes` parameter is set, `y` will not be
        iterated.

    Returns
    -------
    y_indicator : array, shape (n_samples, n_classes)
        A matrix such that `y_indicator[i, j] = 1` iff `classes_[j]` is in
        `y[i]`, and 0 otherwise.
    """
    if self.classes is not None:
        return self.fit(y).transform(y)

    # Automatically increment on new class
    class_mapping = defaultdict(int)
    class_mapping.default_factory = class_mapping.__len__
    yt = self._transform(y, class_mapping)

    # sort classes and reorder columns
    tmp = sorted(class_mapping, key=class_mapping.get)

    # (make safe for tuples)
    dtype = np.int if all(isinstance(c, int) for c in tmp) else object
    class_mapping = np.empty(len(tmp), dtype=dtype)
    class_mapping[:] = tmp
    self.classes_, inverse = np.unique(class_mapping, return_inverse=True)
    yt.indices = np.take(inverse, yt.indices)
    return yt.toarray()


# 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 fit_transform(self, y):
    """Fit the label sets binarizer and transform the given label sets

    Parameters
    ----------
    y : iterable of iterables
        A set of labels (any orderable and hashable object) for each
        sample. If the `classes` parameter is set, `y` will not be
        iterated.

    Returns
    -------
    y_indicator : array or CSR matrix, shape (n_samples, n_classes)
        A matrix such that `y_indicator[i, j] = 1` iff `classes_[j]` is in
        `y[i]`, and 0 otherwise.
    """
    if self.classes is not None:
        return self.fit(y).transform(y)

    # Automatically increment on new class
    class_mapping = defaultdict(int)
    class_mapping.default_factory = class_mapping.__len__
    yt = self._transform(y, class_mapping)

    # sort classes and reorder columns
    tmp = sorted(class_mapping, key=class_mapping.get)

    # (make safe for tuples)
    dtype = np.int if all(isinstance(c, int) for c in tmp) else object
    class_mapping = np.empty(len(tmp), dtype=dtype)
    class_mapping[:] = tmp
    self.classes_, inverse = np.unique(class_mapping, return_inverse=True)
    yt.indices = np.take(inverse, yt.indices)

    if not self.sparse_output:
        yt = yt.toarray()

    return yt