diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
index b4316053c0f74..e95ca48257aec 100644
--- a/sklearn/metrics/_classification.py
+++ b/sklearn/metrics/_classification.py
@@ -991,7 +991,7 @@ def f1_score(
 ):
     """Compute the F1 score, also known as balanced F-score or F-measure.
 
-    The F1 score can be interpreted as a weighted average of the precision and
+    The F1 score can be interpreted as a harmonic mean of the precision and
     recall, where an F1 score reaches its best value at 1 and worst score at 0.
     The relative contribution of precision and recall to the F1 score are
     equal. The formula for the F1 score is::