DOC Fix minor quirks in multiclass docs
@@ -62,14 +62,14 @@ regression is also supported.
binary classification tasks, for example with
:class:`sklearn.multioutput.MultiOutputClassifier`. This approach treats
each label independently whereas multilabel classifiers *may* treat the
- multiple classes simultaneously, accounting for correlated behaviour amoung
+ multiple classes simultaneously, accounting for correlated behavior among
them.
For example, prediction of the topics relevant to a text document or video.
The document or video may be about one of 'religion', 'politics', 'finance'
or 'education', several of the topic classes or all of the topic classes.
- Valid representation of :term:`multilabel` `y` is either dense (or sparse)
+ A valid representation of :term:`multilabel` `y` is an either dense or sparse
:term:`binary` matrix of shape ``(n_samples, n_classes)``. Each column
represents a class. The ``1``'s in each row denote the positive classes a
sample has been labelled with. An example of a dense matrix ``y`` for 3
@@ -99,10 +99,10 @@ regression is also supported.
For example, prediction of both wind speed and wind direction, in degrees,
using data obtained at a certain location. Each sample would be data
- obtained at one location and both wind speed and directtion would be
+ obtained at one location and both wind speed and direction would be
output for each sample.
- Valid representation of :term:`multilabel` `y` is dense matrix of shape
+ A valid representation of :term:`multioutput` `y` is a dense matrix of shape
``(n_samples, n_classes)`` of floats. A column wise concatenation of
:term:`continuous` variables. An example of ``y`` for 3 samples:
@@ -130,8 +130,8 @@ regression is also supported.
image of a fruit, a label is output for both properties and each label is
one of the possible classes of the corresponding property.
- Valid representation of :term:`multilabel` `y` is dense matrix of shape
- ``(n_samples, n_classes)`` of floats. A column wise concatenation of 1d
+ A valid representation of :term:`multioutput` `y` is a dense matrix of shape
+ ``(n_samples, n_classes)`` of class labels. A column wise concatenation of 1d
:term:`multiclass` variables. An example of ``y`` for 3 samples:
>>> y = np.array([['apple', 'green'], ['orange', 'orange'], ['pear', 'green']])
@@ -140,7 +140,7 @@ regression is also supported.
['orange' 'orange']
['pear' 'green']]
- Note that any classifiers handling multioutput-multiclass (also known as
+ Note that all classifiers handling multioutput-multiclass (also known as
multitask classification) tasks, support the multilabel classification task
as a special case. Multitask classification is similar to the multioutput
classification task with different model formulations. For more information,
