DOC Fix some glossary links
@@ -504,8 +504,9 @@ General Concepts
representation of missing values in float arrays. If the array has
integer dtype, NaN cannot be represented. For this reason, we support
specifying another ``missing_values`` value when :term:`imputation` or
- learning can be performed in integer space. :term:`Unlabeled data`
- is a special case of missing values in the :term:`target`.
+ learning can be performed in integer space.
+ :term:`Unlabeled data <unlabeled data>` is a special case of missing
+ values in the :term:`target`.
``n_features``
The number of :term:`features`.
@@ -980,7 +981,7 @@ such as:
Cross-validation estimators are named `EstimatorCV` and tend to be
roughly equivalent to `GridSearchCV(Estimator(), ...)`. The
advantage of using a cross-validation estimator over the canonical
- :term:`Estimator` class along with :ref:`grid search <grid_search>` is
+ :term:`estimator` class along with :ref:`grid search <grid_search>` is
that they can take advantage of warm-starting by reusing precomputed
results in the previous steps of the cross-validation process. This
generally leads to speed improvements. An exception is the
@@ -1206,8 +1207,8 @@ Methods
return the same value, wherein training data needs to be handled
differently (due to model blending in stacked ensembles, for instance;
such cases should be clearly documented).
- :term:`Transductive` transformers may also provide ``fit_transform``
- but not :term:`transform`.
+ :term:`Transductive <transductive>` transformers may also provide
+ ``fit_transform`` but not :term:`transform`.
One reason to implement ``fit_transform`` is that performing ``fit``
and ``transform`` separately would be less efficient than together.
@@ -1295,9 +1296,10 @@ Methods
classifier
An array of shape ``(n_samples,)`` ``(n_samples, n_outputs)``.
- :term:`Multilabel` data may be represented as a sparse matrix if
- a sparse matrix was used in fitting. Each element should be one
- of the values in the classifier's :term:`classes_` attribute.
+ :term:`Multilabel <multilabel>` data may be represented as a sparse
+ matrix if a sparse matrix was used in fitting. Each element should
+ be one of the values in the classifier's :term:`classes_`
+ attribute.
clusterer
An array of shape ``(n_samples,)`` where each value is from 0 to
