DOC clarified hamming loss docstrings
@@ -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
----------
