DOC add that recall with weighted average is equal to accuracy
@@ -1801,7 +1801,8 @@ def recall_score(
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.
+ F-score that is not between precision and recall. Weighted recall
+ is equal to accuracy.
``'samples'``:
Calculate metrics for each instance, and find their average (only
meaningful for multilabel classification where this differs from
