DOC Expand multilabel in decision function in glossary
@@ -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.
