DOC Adds currentmodule into release highlights
Release Highlights for scikit-learn 0.22
========================================
+.. currentmodule:: sklearn
+
We are pleased to announce the release of scikit-learn 0.22, which comes
with many bug fixes and new features! We detail below a few of the major
features of this release. For an exhaustive list of all the changes, please
@@ -21,7 +23,7 @@ or with conda::
# Permutation-based feature importance
# ------------------------------------
#
-# The :func:`~sklearn.inspection.permutation_importance` can be used to get an
+# The :func:`inspection.permutation_importance` can be used to get an
# estimate of the importance of each feature, for any fitted estimator:
from sklearn.ensemble import RandomForestClassifier
@@ -47,8 +49,8 @@ plt.show()
# Native support for missing values for gradient boosting
# -------------------------------------------------------
#
-# The :class:`~sklearn.ensemble.HistGradientBoostingClassifier`
-# and :class:`~sklearn.ensemble.HistGradientBoostingRegressor` now have native
+# The :class:`ensemble.HistGradientBoostingClassifier`
+# and :class:`ensemble.HistGradientBoostingRegressor` now have native
# support for missing values (NaNs). This means that there is no need for
# imputing data when training or predicting.
@@ -125,8 +127,8 @@ print(titanic.data.head()[['pclass', 'embarked']])
# sparse graphs as input, to reuse the same graph for multiple estimator fits.
# To use this feature in a pipeline, one can use the `memory` parameter, along
# with one of the two new transformers,
-# :class:`~sklearn.neighbors.KNeighborsTransformer` and
-# :class:`~sklearn.neighbors.RadiusNeighborsTransformer`. The precomputation
+# :class:`neighbors.KNeighborsTransformer` and
+# :class:`neighbors.RadiusNeighborsTransformer`. The precomputation
# can also be performed by custom estimators to use alternative
# implementations, such as approximate nearest neighbors methods.
# See more details in the :ref:`User Guide <neighbors_transformer>`.
