DOC correct formatting of multilabel confusion matrix
@@ -363,12 +363,12 @@ def multilabel_confusion_matrix(y_true, y_pred, *, sample_weight=None,
Parameters
----------
- y_true : 1d array-like, or label indicator array / sparse matrix
- of shape (n_samples, n_outputs) or (n_samples,)
+ y_true : {array-like, sparse matrix} of shape (n_samples, n_outputs) or \
+ (n_samples,)
Ground truth (correct) target values.
- y_pred : 1d array-like, or label indicator array / sparse matrix
- of shape (n_samples, n_outputs) or (n_samples,)
+ y_pred : {array-like, sparse matrix} of shape (n_samples, n_outputs) or \
+ (n_samples,)
Estimated targets as returned by a classifier
sample_weight : array-like of shape (n_samples,), default=None
@@ -383,7 +383,7 @@ def multilabel_confusion_matrix(y_true, y_pred, *, sample_weight=None,
Returns
-------
- multi_confusion : array, shape (n_outputs, 2, 2)
+ multi_confusion : ndarray of shape (n_outputs, 2, 2)
A 2x2 confusion matrix corresponding to each output in the input.
When calculating class-wise multi_confusion (default), then
n_outputs = n_labels; when calculating sample-wise multi_confusion
@@ -391,7 +391,7 @@ def multilabel_confusion_matrix(y_true, y_pred, *, sample_weight=None,
the results will be returned in the order specified in ``labels``,
otherwise the results will be returned in sorted order by default.
- See also
+ See Also
--------
confusion_matrix
@@ -437,7 +437,6 @@ def multilabel_confusion_matrix(y_true, y_pred, *, sample_weight=None,
<BLANKLINE>
[[2, 1],
[1, 2]]])
-
"""
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
if sample_weight is not None:
