diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
index 2f2092d3376ab..bff8f1e8d87fe 100644
--- a/sklearn/metrics/_classification.py
+++ b/sklearn/metrics/_classification.py
@@ -1274,10 +1274,12 @@ def fbeta_score(
     The F-beta score is the weighted harmonic mean of precision and recall,
     reaching its optimal value at 1 and its worst value at 0.
 
-    The `beta` parameter determines the weight of recall in the combined
-    score. ``beta < 1`` lends more weight to precision, while ``beta > 1``
-    favors recall (``beta -> 0`` considers only precision, ``beta -> +inf``
-    only recall).
+    The `beta` parameter represents the ratio of recall importance to
+    precision importance. `beta > 1` gives more weight to recall, while
+    `beta < 1` favors precision. For example, `beta = 2` makes recall twice
+    as important as precision, while `beta = 0.5` does the opposite.
+    Asymptotically, `beta -> +inf` considers only recall, and `beta -> 0`
+    only precision.
 
     Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`.