Pushing the docs to dev/ for branch: master, commit 5b8c367b7acca78c074bf7d4ad5eb3e7ec7ec681

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -3,6 +3,8 @@
 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 @@
 # 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 @@
 # 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 @@
 # 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>`.

dev/_downloads/7ee55c12f8d3eb1dd8d2005d9dd7b6f1/plot_release_highlights_0_22_0.py

@@ -15,14 +15,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n========================================\nRelease Highlights for scikit-learn 0.22\n========================================\n\nWe are pleased to announce the release of scikit-learn 0.22, which comes\nwith many bug fixes and new features! We detail below a few of the major\nfeatures of this release. For an exhaustive list of all the changes, please\nrefer to the `release notes <changes_0_22>`.\n\nTo install the latest version (with pip)::\n\n    pip install -U scikit-learn --upgrade\n\nor with conda::\n\n    conda install scikit-learn\n"
+        "\n========================================\nRelease Highlights for scikit-learn 0.22\n========================================\n\n.. currentmodule:: sklearn\n\nWe are pleased to announce the release of scikit-learn 0.22, which comes\nwith many bug fixes and new features! We detail below a few of the major\nfeatures of this release. For an exhaustive list of all the changes, please\nrefer to the `release notes <changes_0_22>`.\n\nTo install the latest version (with pip)::\n\n    pip install -U scikit-learn --upgrade\n\nor with conda::\n\n    conda install scikit-learn\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Permutation-based feature importance\n------------------------------------\n\nThe :func:`~sklearn.inspection.permutation_importance` can be used to get an\nestimate of the importance of each feature, for any fitted estimator:\n\n"
+        "Permutation-based feature importance\n------------------------------------\n\nThe :func:`inspection.permutation_importance` can be used to get an\nestimate of the importance of each feature, for any fitted estimator:\n\n"
       ]
     },
     {
@@ -40,7 +40,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Native support for missing values for gradient boosting\n-------------------------------------------------------\n\nThe :class:`~sklearn.ensemble.HistGradientBoostingClassifier`\nand :class:`~sklearn.ensemble.HistGradientBoostingRegressor` now have native\nsupport for missing values (NaNs). This means that there is no need for\nimputing data when training or predicting.\n\n"
+        "Native support for missing values for gradient boosting\n-------------------------------------------------------\n\nThe :class:`ensemble.HistGradientBoostingClassifier`\nand :class:`ensemble.HistGradientBoostingRegressor` now have native\nsupport for missing values (NaNs). This means that there is no need for\nimputing data when training or predicting.\n\n"
       ]
     },
     {
@@ -112,7 +112,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Precomputed sparse nearest neighbors graph\n------------------------------------------\nMost estimators based on nearest neighbors graphs now accept precomputed\nsparse graphs as input, to reuse the same graph for multiple estimator fits.\nTo use this feature in a pipeline, one can use the `memory` parameter, along\nwith one of the two new transformers,\n:class:`~sklearn.neighbors.KNeighborsTransformer` and\n:class:`~sklearn.neighbors.RadiusNeighborsTransformer`. The precomputation\ncan also be performed by custom estimators to use alternative\nimplementations, such as approximate nearest neighbors methods.\nSee more details in the `User Guide <neighbors_transformer>`.\n\n"
+        "Precomputed sparse nearest neighbors graph\n------------------------------------------\nMost estimators based on nearest neighbors graphs now accept precomputed\nsparse graphs as input, to reuse the same graph for multiple estimator fits.\nTo use this feature in a pipeline, one can use the `memory` parameter, along\nwith one of the two new transformers,\n:class:`neighbors.KNeighborsTransformer` and\n:class:`neighbors.RadiusNeighborsTransformer`. The precomputation\ncan also be performed by custom estimators to use alternative\nimplementations, such as approximate nearest neighbors methods.\nSee more details in the `User Guide <neighbors_transformer>`.\n\n"
       ]
     },
     {