Remove a comma that causes ambiguity in the doc of weighted scoring.
@@ -675,7 +675,7 @@ def f1_score(y_true, y_pred, labels=None, pos_label=1, average='binary',
Calculate metrics for each label, and find their unweighted
mean. This does not take label imbalance into account.
``'weighted'``:
- Calculate metrics for each label, and find their average, weighted
+ Calculate metrics for each label, and find their average weighted
by support (the number of true instances for each label). This
alters 'macro' to account for label imbalance; it can result in an
F-score that is not between precision and recall.
@@ -778,7 +778,7 @@ def fbeta_score(y_true, y_pred, beta, labels=None, pos_label=1,
Calculate metrics for each label, and find their unweighted
mean. This does not take label imbalance into account.
``'weighted'``:
- Calculate metrics for each label, and find their average, weighted
+ Calculate metrics for each label, and find their average weighted
by support (the number of true instances for each label). This
alters 'macro' to account for label imbalance; it can result in an
F-score that is not between precision and recall.
@@ -950,7 +950,7 @@ def precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None,
Calculate metrics for each label, and find their unweighted
mean. This does not take label imbalance into account.
``'weighted'``:
- Calculate metrics for each label, and find their average, weighted
+ Calculate metrics for each label, and find their average weighted
by support (the number of true instances for each label). This
alters 'macro' to account for label imbalance; it can result in an
F-score that is not between precision and recall.
@@ -1224,7 +1224,7 @@ def precision_score(y_true, y_pred, labels=None, pos_label=1,
Calculate metrics for each label, and find their unweighted
mean. This does not take label imbalance into account.
``'weighted'``:
- Calculate metrics for each label, and find their average, weighted
+ Calculate metrics for each label, and find their average weighted
by support (the number of true instances for each label). This
alters 'macro' to account for label imbalance; it can result in an
F-score that is not between precision and recall.
@@ -1323,7 +1323,7 @@ def recall_score(y_true, y_pred, labels=None, pos_label=1, average='binary',
Calculate metrics for each label, and find their unweighted
mean. This does not take label imbalance into account.
``'weighted'``:
- Calculate metrics for each label, and find their average, weighted
+ Calculate metrics for each label, and find their average weighted
by support (the number of true instances for each label). This
alters 'macro' to account for label imbalance; it can result in an
F-score that is not between precision and recall.
