Pushing the docs to dev/ for branch: main, commit fe52f1495ab0b6b8b7dfe676c33be4e046ef9e29

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.
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+config: f3d7c520561ae0f01f04da6002cd6c98
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dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -158,8 +158,3 @@
 # :class:`TargetEncoder` is a part of a :class:`~sklearn.pipeline.Pipeline` and the
 # pipeline is fitted, the pipeline will correctly call
 # :meth:`TargetEncoder.fit_transform` and pass the encoding along.
-
-# %%
-# This resets `transform_output` to its default value to avoid impacting other
-# examples when generating the scikit-learn documentation
-sklearn.set_config(transform_output="default")

dev/_downloads/4f6558a73e0c79834afc005bac34dc13/plot_target_encoder_cross_val.py

@@ -193,24 +193,6 @@
       "source": [
         "## Conclusion\nThis example demonstrates the importance of :class:`TargetEncoder`'s interval cross\nvalidation. It is important to use :meth:`TargetEncoder.fit_transform` to encode\ntraining data before passing it to a machine learning model. When a\n:class:`TargetEncoder` is a part of a :class:`~sklearn.pipeline.Pipeline` and the\npipeline is fitted, the pipeline will correctly call\n:meth:`TargetEncoder.fit_transform` and pass the encoding along.\n\n"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This resets `transform_output` to its default value to avoid impacting other\nexamples when generating the scikit-learn documentation\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "sklearn.set_config(transform_output=\"default\")"
-      ]
     }
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