Pushing the docs to dev/ for branch: main, commit 0040de8c0be2fe3d7885919b9384c163b7c38c81

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: ffce940479d0726dd5730943b17d4cc9
+config: af1f7b72b83f6232ab845ab368d0cdb2
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -137,7 +137,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Metadata Routing Support\nMany meta-estimators and cross-validation routines now support metadata\nrouting, which are listed in the `user guide\n<_metadata_routing_models>`. For instance, this is how you can do a nested\ncross-validation with sample weights and :class:`~model_selection.GroupKFold`:\n\n"
+        "## Metadata Routing Support\nMany meta-estimators and cross-validation routines now support metadata\nrouting, which are listed in the `user guide\n<metadata_routing_models>`. For instance, this is how you can do a nested\ncross-validation with sample weights and :class:`~model_selection.GroupKFold`:\n\n"
       ]
     },
     {

dev/_downloads/53490cdb42c3c07ba8cccd1c4ed4dca4/plot_release_highlights_1_4_0.ipynb

@@ -155,7 +155,7 @@
 # ------------------------
 # Many meta-estimators and cross-validation routines now support metadata
 # routing, which are listed in the :ref:`user guide
-# <_metadata_routing_models>`. For instance, this is how you can do a nested
+# <metadata_routing_models>`. For instance, this is how you can do a nested
 # cross-validation with sample weights and :class:`~model_selection.GroupKFold`:
 import sklearn
 from sklearn.metrics import get_scorer