[MRG+1] Add a note in coverage_error doc to highlight the difference from original coverage formula (scikit-learn#7915)

Author: neerajgangwar

doc/modules/model_evaluation.rst

@@ -1133,6 +1133,12 @@ are predicted. This is useful if you want to know how many top-scored-labels
 you have to predict in average without missing any true one. The best value
 of this metrics is thus the average number of true labels.
 
+.. note::
+
+    Our implementation's score is 1 greater than the one given in Tsoumakas
+    et al., 2010. This extends it to handle the degenerate case in which an
+    instance has 0 true labels.
+
 Formally, given a binary indicator matrix of the ground truth labels
 :math:`y \in \left\{0, 1\right\}^{n_\text{samples} \times n_\text{labels}}` and the
 score associated with each label
@@ -1236,6 +1242,12 @@ Here is a small example of usage of this function::
     >>> label_ranking_loss(y_true, y_score)
     0.0
 
+
+.. topic:: References:
+
+  * Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010). Mining multi-label data. In
+    Data mining and knowledge discovery handbook (pp. 667-685). Springer US.
+
 .. _regression_metrics:
 
 Regression metrics

sklearn/metrics/ranking.py

@@ -633,6 +633,10 @@ def coverage_error(y_true, y_score, sample_weight=None):
     Ties in ``y_scores`` are broken by giving maximal rank that would have
     been assigned to all tied values.
 
+    Note: Our implementation's score is 1 greater than the one given in
+    Tsoumakas et al., 2010. This extends it to handle the degenerate case
+    in which an instance has 0 true labels.
+
     Read more in the :ref:`User Guide <coverage_error>`.
 
     Parameters