diff --git a/scikits/learn/metrics/metrics.py b/scikits/learn/metrics/metrics.py
index bb0f6fad81b7e70a13c46f553ed9aa2f1676919a..3aabe702411ea3fdbd94d3b02cdc6c639cf20e1e 100644
--- a/scikits/learn/metrics/metrics.py
+++ b/scikits/learn/metrics/metrics.py
@@ -257,7 +257,7 @@ def f1_score(y_true, y_pred, pos_label=1):
     score at 0. The relative contribution of precision and recall to the f1
     score are equal.
 
-        :math:`F_1 = 2 \cdot \frac{p \cdot r}{p + r}`
+        F_1 = 2 * (precision * recall) / (precision + recall)
 
     See: http://en.wikipedia.org/wiki/F1_score