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[MRG+1] minor sphinx fixes #9370

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Jul 15, 2017
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[MRG+1] minor sphinx fixes #9370

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4 changes: 0 additions & 4 deletions doc/modules/model_evaluation.rst
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Expand Up @@ -670,10 +670,6 @@ binary classification and multilabel indicator format.
for an example of :func:`precision_recall_curve` usage to evaluate
classifier output quality.

* See :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_recovery.py`
for an example of :func:`precision_recall_curve` usage to select
features for sparse linear models.

Binary classification
^^^^^^^^^^^^^^^^^^^^^

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5 changes: 3 additions & 2 deletions doc/modules/multiclass.rst
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Expand Up @@ -353,7 +353,7 @@ Classifier Chain

Classifier chains (see :class:`ClassifierChain`) are a way of combining a
number of binary classifiers into a single multi-label model that is capable
of exploiting correlations among targets.
of exploiting correlations among targets.

For a multi-label classification problem with N classes, N binary
classifiers are assigned an integer between 0 and N-1. These integers
Expand All @@ -373,5 +373,6 @@ typically many randomly ordered chains are fit and their predictions are
averaged together.

.. topic:: References:

Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank,
"Classifier Chains for Multi-label Classification", 2009.
"Classifier Chains for Multi-label Classification", 2009.
4 changes: 2 additions & 2 deletions doc/whats_new.rst
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Expand Up @@ -4350,7 +4350,7 @@ Highlights
- :ref:`out_of_bag` of generalization error for :ref:`ensemble`
by `Andreas Müller`_.

- :ref:`randomized_l1`: Randomized sparse linear models for feature
- Randomized sparse linear models for feature
selection, by `Alexandre Gramfort`_ and `Gael Varoquaux`_

- :ref:`label_propagation` for semi-supervised learning, by Clay
Expand Down Expand Up @@ -4811,7 +4811,7 @@ Changelog
`Mathieu Blondel`_ and `Lars Buitinck`_

- Documentation improvements: thumbnails in
:ref:`example gallery <examples-index>` by `Fabian Pedregosa`_.
example gallery by `Fabian Pedregosa`_.

- Important bugfixes in :ref:`svm` module (segfaults, bad
performance) by `Fabian Pedregosa`_.
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8 changes: 3 additions & 5 deletions sklearn/multioutput.py
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Expand Up @@ -14,8 +14,6 @@
#
# License: BSD 3 clause

from abc import ABCMeta

import numpy as np
import scipy.sparse as sp
from abc import ABCMeta, abstractmethod
Expand Down Expand Up @@ -309,7 +307,7 @@ class MultiOutputClassifier(MultiOutputEstimator, ClassifierMixin):

Attributes
----------
estimators_ : list of `n_output` estimators
estimators_ : list of ``n_output`` estimators
Estimators used for predictions.
"""

Expand Down Expand Up @@ -420,7 +418,7 @@ class ClassifierChain(BaseEstimator):
Attributes
----------
classes_ : list
A list of arrays of length len(estimators_) containing the
A list of arrays of length ``len(estimators_)`` containing the
class labels for each estimator in the chain.

estimators_ : list
Expand Down Expand Up @@ -456,7 +454,7 @@ def fit(self, X, Y):
self : object
Returns self.
"""
X, Y = check_X_y(X, Y, multi_output=True, accept_sparse=True)
X, Y = check_X_y(X, Y, multi_output=True, accept_sparse=True)

random_state = check_random_state(self.random_state)
check_array(X, accept_sparse=True)
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