wip

Joan Massich · Joan Massich · commit b2d956340ff1 · 2018-07-11T00:42:22.000+02:00
diff --git a/doc/modules/multiclass.rst b/doc/modules/multiclass.rst
@@ -14,7 +14,7 @@ Multiclass and multilabel algorithms
 
 The :mod:`sklearn.multiclass` module implements *meta-estimators* to solve
 ``multiclass`` and ``multilabel`` classification problems
-by decomposing such problems into binary classification problems. Multioutput
+by decomposing such problems into binary classification problems. ``multioutput``
 regression is also supported.
 
 - **Multiclass classification** means a classification task with more than
@@ -23,24 +23,39 @@ regression is also supported.
   sample is assigned to one and only one label: a fruit can be either an
   apple or a pear but not both at the same time.
 
+  - accepted :func:`sklearn.utils.multiclass.type_of_target` (y):
+    'multiclass': `y` contains more than two discrete values, is not a
+    sequence of sequences, and is 1d or a column vector.
+
 - **Multilabel classification** assigns to each sample a set of target
   labels. This can be thought as predicting properties of a data-point
   that are not mutually exclusive, such as topics that are relevant for a
   document. A text might be about any of religion, politics, finance or
   education at the same time or none of these.
 
+  - accepted :func:`sklearn.utils.multiclass.type_of_target` (y):
+    'multilabel-indicator': `y` is a label indicator matrix, an array
+    of two dimensions with at least two columns, and at most 2 unique
+    values.
+
 - **Multioutput regression** assigns each sample a set of target
   values.  This can be thought of as predicting several properties
   for each data-point, such as wind direction and magnitude at a
   certain location.
 
+  - accepted :func:`sklearn.utils.multiclass.type_of_target` (y):
+    'continuous-multioutput': `y` is a 2d array of floats that are
+    not all integers, and both dimensions are of size > 1.
+
 - **Multioutput-multiclass classification** 
   (also known as **multi-task classification**)
   means that a single estimator has to handle several joint classification
   tasks. This is both a generalization of the multi-label classification
   task, which only considers binary classification, as well as a
-  generalization of the multi-class classification task.  *The output format
-  is a 2d numpy array or sparse matrix.*
+  generalization of the multi-class classification task. 
+
+  - accepted :func:`sklearn.utils.multiclass.type_of_target` (y):
+  *The output format is a 2d numpy array or sparse matrix.*
 
   The set of labels can be different for each output variable.
   For instance, a sample could be assigned "pear" for an output variable that
