Pushing the docs to dev/ for branch: main, commit 23a0dcbac95526c2234241775b8bfb28681aafe5

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: 117d219e83ee5bcd8e704d323124bae3
+config: 0aed4683969c946022019f9fec75b9e1
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -43,7 +43,7 @@
 # were already standardized.
 # For a more complete example on the interpretations of the coefficients of
 # linear models, you may refer to
-# :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`.
+# :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`.  # noqa: E501
 import matplotlib.pyplot as plt
 import numpy as np
 from sklearn.linear_model import RidgeCV

dev/_downloads/53e76f761ef04e8d06fa5757554513b0/plot_select_from_model_diabetes.py

@@ -191,7 +191,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We now train and test the datasets with 8 different classification models and\nget performance results for each model. The goal of this study is to highlight\nthe computation/accuracy tradeoffs of different types of classifiers for\nsuch a multi-class text classification problem.\n\nNotice that the most important hyperparameters values were tuned using a grid\nsearch procedure not shown in this notebook for the sake of simplicity. See\nthe example script\n`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`\nfor a demo on how such tuning can be done.\n\n"
+        "We now train and test the datasets with 8 different classification models and\nget performance results for each model. The goal of this study is to highlight\nthe computation/accuracy tradeoffs of different types of classifiers for\nsuch a multi-class text classification problem.\n\nNotice that the most important hyperparameters values were tuned using a grid\nsearch procedure not shown in this notebook for the sake of simplicity. See\nthe example script\n`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`  # noqa: E501\nfor a demo on how such tuning can be done.\n\n"
       ]
     },
     {

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -358,7 +358,7 @@ def benchmark(clf, custom_name=False):
 # Notice that the most important hyperparameters values were tuned using a grid
 # search procedure not shown in this notebook for the sake of simplicity. See
 # the example script
-# :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`
+# :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`  # noqa: E501
 # for a demo on how such tuning can be done.
 
 from sklearn.linear_model import LogisticRegression

dev/_downloads/7ff1697c60d48929305821f39296dbb9/plot_document_classification_20newsgroups.ipynb

@@ -29,7 +29,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Feature importance from coefficients\n\nTo get an idea of the importance of the features, we are going to use the\n:class:`~sklearn.linear_model.RidgeCV` estimator. The features with the\nhighest absolute `coef_` value are considered the most important.\nWe can observe the coefficients directly without needing to scale them (or\nscale the data) because from the description above, we know that the features\nwere already standardized.\nFor a more complete example on the interpretations of the coefficients of\nlinear models, you may refer to\n`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`.\n\n"
+        "## Feature importance from coefficients\n\nTo get an idea of the importance of the features, we are going to use the\n:class:`~sklearn.linear_model.RidgeCV` estimator. The features with the\nhighest absolute `coef_` value are considered the most important.\nWe can observe the coefficients directly without needing to scale them (or\nscale the data) because from the description above, we know that the features\nwere already standardized.\nFor a more complete example on the interpretations of the coefficients of\nlinear models, you may refer to\n`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`.  # noqa: E501\n\n"
       ]
     },
     {