Pushing the docs to dev/ for branch: main, commit 5fee5ad33c2a3857422ed950b32add46243339ce

Author: sklearn-ci

dev/_downloads/02192f99342d6d1323161978b6c80bfc/plot_ols_ridge.zip

@@ -11,7 +11,7 @@
 
     See also :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py`
 
-"""
+"""  # noqa: E501
 
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause

dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip

@@ -40,7 +40,7 @@
 # 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
 

dev/_downloads/0358b1921962fd7c6f5a94c5abc91476/plot_hashing_vs_dict_vectorizer.zip

@@ -60,7 +60,7 @@
     Proceedings of the National Academy of Sciences of the United States
     of America, 17, 684-688.
 
-"""
+"""  # noqa: E501
 
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause

dev/_downloads/038d1885eb5f5ea53aca42da3031fe38/plot_document_clustering.zip

@@ -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/03c018a16384c69f3a89e473650d57ee/plot_svm_margin.zip

@@ -356,7 +356,7 @@ 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.ensemble import RandomForestClassifier

dev/_downloads/04b9a7769df5b331f4d94e1d065b4311/plot_voting_decision_regions.zip

@@ -16,7 +16,7 @@
 
     See also :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py`
 
-"""
+"""  # noqa: E501
 
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause

dev/_downloads/053b58bbfc8177072856c743b2c93424/plot_varimax_fa.zip

@@ -40,7 +40,7 @@
       "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"
       ]
     },
     {