DOC fix typos in metrics/_classification.py
@@ -1174,7 +1174,7 @@ def fbeta_score(
setting ``labels=[pos_label]`` and ``average != 'binary'`` will report
scores for that label only.
- average : {'micro', 'macro', 'samples', 'weighted', 'binary'} or None \
+ average : {'micro', 'macro', 'samples', 'weighted', 'binary'} or None, \
default='binary'
This parameter is required for multiclass/multilabel targets.
If ``None``, the scores for each class are returned. Otherwise, this
@@ -1475,7 +1475,7 @@ def precision_recall_fscore_support(
precision : float (if average is not None) or array of float, shape =\
[n_unique_labels]
- recall : float (if average is not None) or array of float, , shape =\
+ recall : float (if average is not None) or array of float, shape =\
[n_unique_labels]
fbeta_score : float (if average is not None) or array of float, shape =\
@@ -1662,7 +1662,7 @@ def precision_score(
setting ``labels=[pos_label]`` and ``average != 'binary'`` will report
scores for that label only.
- average : {'micro', 'macro', 'samples', 'weighted', 'binary'} or None \
+ average : {'micro', 'macro', 'samples', 'weighted', 'binary'} or None, \
default='binary'
This parameter is required for multiclass/multilabel targets.
If ``None``, the scores for each class are returned. Otherwise, this
@@ -1696,7 +1696,7 @@ def precision_score(
Returns
-------
- precision : float (if average is not None) or array of float of shape
+ precision : float (if average is not None) or array of float of shape \
(n_unique_labels,)
Precision of the positive class in binary classification or weighted
average of the precision of each class for the multiclass task.
@@ -1794,7 +1794,7 @@ def recall_score(
setting ``labels=[pos_label]`` and ``average != 'binary'`` will report
scores for that label only.
- average : {'micro', 'macro', 'samples', 'weighted', 'binary'} or None \
+ average : {'micro', 'macro', 'samples', 'weighted', 'binary'} or None, \
default='binary'
This parameter is required for multiclass/multilabel targets.
If ``None``, the scores for each class are returned. Otherwise, this
@@ -1829,7 +1829,7 @@ def recall_score(
Returns
-------
- recall : float (if average is not None) or array of float of shape
+ recall : float (if average is not None) or array of float of shape \
(n_unique_labels,)
Recall of the positive class in binary classification or weighted
average of the recall of each class for the multiclass task.
