DOC clarified hamming loss docstrings (scikit-learn#13760)

XavierSATTLER · jnothman · commit 3cdeb448648e · 2019-05-08T14:39:47.000+10:00
diff --git a/sklearn/metrics/classification.py b/sklearn/metrics/classification.py
@@ -1989,16 +1989,18 @@ def hamming_loss(y_true, y_pred, labels=None, sample_weight=None):
     -----
     In multiclass classification, the Hamming loss corresponds to the Hamming
     distance between ``y_true`` and ``y_pred`` which is equivalent to the
-    subset ``zero_one_loss`` function.
+    subset ``zero_one_loss`` function, when `normalize` parameter is set to
+    True.
 
     In multilabel classification, the Hamming loss is different from the
     subset zero-one loss. The zero-one loss considers the entire set of labels
     for a given sample incorrect if it does not entirely match the true set of
-    labels. Hamming loss is more forgiving in that it penalizes the individual
-    labels.
+    labels. Hamming loss is more forgiving in that it penalizes only the
+    individual labels.
 
-    The Hamming loss is upperbounded by the subset zero-one loss. When
-    normalized over samples, the Hamming loss is always between 0 and 1.
+    The Hamming loss is upperbounded by the subset zero-one loss, when
+    `normalize` parameter is set to True. It is always between 0 and 1,
+    lower being better.
 
     References
     ----------
