DOC Expand multilabel in decision function in glossary (scikit-learn#…

…24095)

doc/glossary.rst

@@ -1177,18 +1177,19 @@ Methods
             predicted class.  Columns are ordered according to
             :term:`classes_`.
         multilabel classification
-            Scikit-learn is inconsistent in its representation of multilabel
-            decision functions.  Some estimators represent it like multiclass
-            multioutput, i.e. a list of 2d arrays, each with two columns. Others
-            represent it with a single 2d array, whose columns correspond to
-            the individual binary classification decisions. The latter
-            representation is ambiguously identical to the multiclass
-            classification format, though its semantics differ: it should be
-            interpreted, like in the binary case, by thresholding at 0.
-
-            TODO: `This gist
-            <https://gist.github.com/jnothman/4807b1b0266613c20ba4d1f88d0f8cf5>`_
-            highlights the use of the different formats for multilabel.
+            Scikit-learn is inconsistent in its representation of :term:`multilabel`
+            decision functions. It may be represented one of two ways:
+
+            - List of 2d arrays, each array of shape: (`n_samples`, 2), like in
+              multiclass multioutput. List is of length `n_labels`.
+
+            - Single 2d array of shape (`n_samples`, `n_labels`), with each
+              'column' in the array corresponding to the individual binary
+              classification decisions. This is identical to the
+              multiclass classification format, though its semantics differ: it
+              should be interpreted, like in the binary case, by thresholding at
+              0.
+
         multioutput classification
             A list of 2d arrays, corresponding to each multiclass decision
             function.