On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
@@ -1051,7 +1051,7 @@ representation of the estimator is displayed:
this.parentElement.nextElementSibling)"
>
score_func
- <function chi...x7fb81b1ab7f0>
+ <function chi...x7f7a8437b7f0>
@@ -1871,14 +1871,14 @@ can use this information to dispatch the categorical columns to the
SimpleImputer(strategy='median')),
('scaler',
StandardScaler())]),
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb818bdaaa0>),
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a64abda50>),
('cat',
Pipeline(steps=[('encoder',
OneHotEncoder(handle_unknown='ignore')),
('selector',
SelectPercentile(percentile=50,
- score_func=<function chi2 at 0x7fb81b1ab7f0>))]),
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb818bdbb80>)])),
+ score_func=<function chi2 at 0x7f7a8437b7f0>))]),
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a64abc970>)])),
('classifier', LogisticRegression())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
@@ -2019,7 +2019,7 @@ can use this information to dispatch the categorical columns to the
<sklearn.compose._column_transformer.make_column_selector object at 0x7fb818bdaaa0>
+
<sklearn.compose._column_transformer.make_column_selector object at 0x7f7a64abda50>
Parameters
@@ -2129,7 +2129,7 @@ can use this information to dispatch the categorical columns to the<sklearn.compose._column_transformer.make_column_selector object at 0x7fb818bdbb80>
+
<sklearn.compose._column_transformer.make_column_selector object at 0x7f7a64abc970>
Parameters
@@ -2232,7 +2232,7 @@ can use this information to dispatch the categorical columns to the this.parentElement.nextElementSibling)" ><sklearn.compose._column_transformer.make_column_selector object at 0x7fb818bdaaa0>
+
<sklearn.compose._column_transformer.make_column_selector object at 0x7f7a64abda50>
Parameters
@@ -3368,7 +3368,7 @@ the parameter space will be evaluated.<sklearn.compose._column_transformer.make_column_selector object at 0x7fb818bdbb80>
+
<sklearn.compose._column_transformer.make_column_selector object at 0x7f7a64abc970>
Parameters
@@ -3471,7 +3471,7 @@ the parameter space will be evaluated. this.parentElement.nextElementSibling)" >On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
@@ -623,7 +623,7 @@ Illustration of ``Pipeline`` and ``GridSearchCV``
this.parentElement.nextElementSibling)"
>
param_grid
- [{'classify__C': [1, 10, ...], 'reduce_dim': [PCA(iterated_power=7), NMF(max_iter=1000)], 'reduce_dim__n_components': [2, 4, ...]}, {'classify__C': [1, 10, ...], 'reduce_dim': [SelectKBest(s...7fb81b1a9c60>)], 'reduce_dim__k': [2, 4, ...]}]
+ [{'classify__C': [1, 10, ...], 'reduce_dim': [PCA(iterated_power=7), NMF(max_iter=1000)], 'reduce_dim__n_components': [2, 4, ...]}, {'classify__C': [1, 10, ...], 'reduce_dim': [SelectKBest(s...7f7a84379c60>)], 'reduce_dim__k': [2, 4, ...]}]
@@ -1122,7 +1122,7 @@ a transformer is costly.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 48.015 seconds)
+ **Total running time of the script:** (0 minutes 45.156 seconds)
.. _sphx_glr_download_auto_examples_compose_plot_compare_reduction.py:
diff --git a/dev/_sources/auto_examples/compose/plot_digits_pipe.rst.txt b/dev/_sources/auto_examples/compose/plot_digits_pipe.rst.txt
index 2128c94a43028..f01449c1e3fe4 100644
--- a/dev/_sources/auto_examples/compose/plot_digits_pipe.rst.txt
+++ b/dev/_sources/auto_examples/compose/plot_digits_pipe.rst.txt
@@ -131,7 +131,7 @@ We use a GridSearchCV to set the dimensionality of the PCA
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.281 seconds)
+ **Total running time of the script:** (0 minutes 1.294 seconds)
.. _sphx_glr_download_auto_examples_compose_plot_digits_pipe.py:
diff --git a/dev/_sources/auto_examples/compose/plot_feature_union.rst.txt b/dev/_sources/auto_examples/compose/plot_feature_union.rst.txt
index 1acd8cb4b91a9..dae25e4e212be 100644
--- a/dev/_sources/auto_examples/compose/plot_feature_union.rst.txt
+++ b/dev/_sources/auto_examples/compose/plot_feature_union.rst.txt
@@ -287,7 +287,7 @@ dataset and is only used to illustrate the usage of FeatureUnion.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.484 seconds)
+ **Total running time of the script:** (0 minutes 0.472 seconds)
.. _sphx_glr_download_auto_examples_compose_plot_feature_union.py:
diff --git a/dev/_sources/auto_examples/compose/plot_transformed_target.rst.txt b/dev/_sources/auto_examples/compose/plot_transformed_target.rst.txt
index fcde4ca7c7f08..45fd8f24e1467 100644
--- a/dev/_sources/auto_examples/compose/plot_transformed_target.rst.txt
+++ b/dev/_sources/auto_examples/compose/plot_transformed_target.rst.txt
@@ -381,7 +381,7 @@ better model fit.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.643 seconds)
+ **Total running time of the script:** (0 minutes 1.424 seconds)
.. _sphx_glr_download_auto_examples_compose_plot_transformed_target.py:
diff --git a/dev/_sources/auto_examples/covariance/plot_covariance_estimation.rst.txt b/dev/_sources/auto_examples/covariance/plot_covariance_estimation.rst.txt
index 40ee7f23f3901..fe7d0ae9c2245 100644
--- a/dev/_sources/auto_examples/covariance/plot_covariance_estimation.rst.txt
+++ b/dev/_sources/auto_examples/covariance/plot_covariance_estimation.rst.txt
@@ -249,7 +249,7 @@ cross-validation, or with the LedoitWolf and OAS estimates.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.457 seconds)
+ **Total running time of the script:** (0 minutes 0.492 seconds)
.. _sphx_glr_download_auto_examples_covariance_plot_covariance_estimation.py:
diff --git a/dev/_sources/auto_examples/covariance/plot_lw_vs_oas.rst.txt b/dev/_sources/auto_examples/covariance/plot_lw_vs_oas.rst.txt
index 10fe1ac553389..7769161dad473 100644
--- a/dev/_sources/auto_examples/covariance/plot_lw_vs_oas.rst.txt
+++ b/dev/_sources/auto_examples/covariance/plot_lw_vs_oas.rst.txt
@@ -153,7 +153,7 @@ Chen et al., IEEE Trans. on Sign. Proc., Volume 58, Issue 10, October 2010.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 2.445 seconds)
+ **Total running time of the script:** (0 minutes 2.507 seconds)
.. _sphx_glr_download_auto_examples_covariance_plot_lw_vs_oas.py:
diff --git a/dev/_sources/auto_examples/covariance/plot_mahalanobis_distances.rst.txt b/dev/_sources/auto_examples/covariance/plot_mahalanobis_distances.rst.txt
index 24d0414ef5160..8f5caf09f477d 100644
--- a/dev/_sources/auto_examples/covariance/plot_mahalanobis_distances.rst.txt
+++ b/dev/_sources/auto_examples/covariance/plot_mahalanobis_distances.rst.txt
@@ -320,7 +320,7 @@ distribution of inlier samples for robust MCD based Mahalanobis distances.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.268 seconds)
+ **Total running time of the script:** (0 minutes 0.327 seconds)
.. _sphx_glr_download_auto_examples_covariance_plot_mahalanobis_distances.py:
diff --git a/dev/_sources/auto_examples/covariance/plot_robust_vs_empirical_covariance.rst.txt b/dev/_sources/auto_examples/covariance/plot_robust_vs_empirical_covariance.rst.txt
index 397dfedfe351d..fcd9787902776 100644
--- a/dev/_sources/auto_examples/covariance/plot_robust_vs_empirical_covariance.rst.txt
+++ b/dev/_sources/auto_examples/covariance/plot_robust_vs_empirical_covariance.rst.txt
@@ -224,7 +224,7 @@ References
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 2.756 seconds)
+ **Total running time of the script:** (0 minutes 3.010 seconds)
.. _sphx_glr_download_auto_examples_covariance_plot_robust_vs_empirical_covariance.py:
diff --git a/dev/_sources/auto_examples/covariance/plot_sparse_cov.rst.txt b/dev/_sources/auto_examples/covariance/plot_sparse_cov.rst.txt
index 6192895bdca29..d0123e8f9f6b0 100644
--- a/dev/_sources/auto_examples/covariance/plot_sparse_cov.rst.txt
+++ b/dev/_sources/auto_examples/covariance/plot_sparse_cov.rst.txt
@@ -247,7 +247,7 @@ Plot the results
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.453 seconds)
+ **Total running time of the script:** (0 minutes 0.495 seconds)
.. _sphx_glr_download_auto_examples_covariance_plot_sparse_cov.py:
diff --git a/dev/_sources/auto_examples/cross_decomposition/plot_compare_cross_decomposition.rst.txt b/dev/_sources/auto_examples/cross_decomposition/plot_compare_cross_decomposition.rst.txt
index eb1857fbd82a7..c808542581742 100644
--- a/dev/_sources/auto_examples/cross_decomposition/plot_compare_cross_decomposition.rst.txt
+++ b/dev/_sources/auto_examples/cross_decomposition/plot_compare_cross_decomposition.rst.txt
@@ -336,7 +336,7 @@ CCA (PLS mode B with symmetric deflation)
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.192 seconds)
+ **Total running time of the script:** (0 minutes 0.245 seconds)
.. _sphx_glr_download_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py:
diff --git a/dev/_sources/auto_examples/cross_decomposition/plot_pcr_vs_pls.rst.txt b/dev/_sources/auto_examples/cross_decomposition/plot_pcr_vs_pls.rst.txt
index 1466c9defe328..7218fde23b456 100644
--- a/dev/_sources/auto_examples/cross_decomposition/plot_pcr_vs_pls.rst.txt
+++ b/dev/_sources/auto_examples/cross_decomposition/plot_pcr_vs_pls.rst.txt
@@ -292,7 +292,7 @@ component which has the most preditive power on the target.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.522 seconds)
+ **Total running time of the script:** (0 minutes 0.535 seconds)
.. _sphx_glr_download_auto_examples_cross_decomposition_plot_pcr_vs_pls.py:
diff --git a/dev/_sources/auto_examples/datasets/plot_random_multilabel_dataset.rst.txt b/dev/_sources/auto_examples/datasets/plot_random_multilabel_dataset.rst.txt
index aee36f94392e8..d6b283c4100f2 100644
--- a/dev/_sources/auto_examples/datasets/plot_random_multilabel_dataset.rst.txt
+++ b/dev/_sources/auto_examples/datasets/plot_random_multilabel_dataset.rst.txt
@@ -158,7 +158,7 @@ feature distinguishes a particular class.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.134 seconds)
+ **Total running time of the script:** (0 minutes 0.148 seconds)
.. _sphx_glr_download_auto_examples_datasets_plot_random_multilabel_dataset.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_faces_decomposition.rst.txt b/dev/_sources/auto_examples/decomposition/plot_faces_decomposition.rst.txt
index 9ed08669c06e9..8dea270b5bf31 100644
--- a/dev/_sources/auto_examples/decomposition/plot_faces_decomposition.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_faces_decomposition.rst.txt
@@ -609,7 +609,7 @@ coefficients are positively constrained.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 7.936 seconds)
+ **Total running time of the script:** (0 minutes 8.719 seconds)
.. _sphx_glr_download_auto_examples_decomposition_plot_faces_decomposition.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_ica_blind_source_separation.rst.txt b/dev/_sources/auto_examples/decomposition/plot_ica_blind_source_separation.rst.txt
index 34285f6d323f0..ba237b1c3af50 100644
--- a/dev/_sources/auto_examples/decomposition/plot_ica_blind_source_separation.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_ica_blind_source_separation.rst.txt
@@ -159,7 +159,7 @@ Plot results
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.372 seconds)
+ **Total running time of the script:** (0 minutes 0.410 seconds)
.. _sphx_glr_download_auto_examples_decomposition_plot_ica_blind_source_separation.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_ica_vs_pca.rst.txt b/dev/_sources/auto_examples/decomposition/plot_ica_vs_pca.rst.txt
index d64977f82a860..475dda920eff0 100644
--- a/dev/_sources/auto_examples/decomposition/plot_ica_vs_pca.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_ica_vs_pca.rst.txt
@@ -176,7 +176,7 @@ Plot results
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.391 seconds)
+ **Total running time of the script:** (0 minutes 0.410 seconds)
.. _sphx_glr_download_auto_examples_decomposition_plot_ica_vs_pca.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_image_denoising.rst.txt b/dev/_sources/auto_examples/decomposition/plot_image_denoising.rst.txt
index 972dac9ab5bee..90c892f1be00a 100644
--- a/dev/_sources/auto_examples/decomposition/plot_image_denoising.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_image_denoising.rst.txt
@@ -244,7 +244,7 @@ Learn the dictionary from reference patches
.. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_002.png
- :alt: Dictionary learned from face patches Train time 15.1s on 22692 patches
+ :alt: Dictionary learned from face patches Train time 15.0s on 22692 patches
:srcset: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_002.png
:class: sphx-glr-single-img
@@ -254,7 +254,7 @@ Learn the dictionary from reference patches
.. code-block:: none
Learning the dictionary...
- 2.0 iterations / 125 steps in 15.06.
+ 2.0 iterations / 125 steps in 14.98.
@@ -316,7 +316,7 @@ Extract noisy patches and reconstruct them using the dictionary
*
.. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_003.png
- :alt: Orthogonal Matching Pursuit 1 atom (time: 0.6s), Image, Difference (norm: 10.70)
+ :alt: Orthogonal Matching Pursuit 1 atom (time: 0.5s), Image, Difference (norm: 10.70)
:srcset: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_003.png
:class: sphx-glr-multi-img
@@ -330,14 +330,14 @@ Extract noisy patches and reconstruct them using the dictionary
*
.. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_005.png
- :alt: Least-angle regression 4 atoms (time: 8.4s), Image, Difference (norm: 13.35)
+ :alt: Least-angle regression 4 atoms (time: 8.2s), Image, Difference (norm: 13.35)
:srcset: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_005.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_006.png
- :alt: Thresholding alpha=0.1 (time: 0.2s), Image, Difference (norm: 14.26)
+ :alt: Thresholding alpha=0.1 (time: 0.1s), Image, Difference (norm: 14.26)
:srcset: /auto_examples/decomposition/images/sphx_glr_plot_image_denoising_006.png
:class: sphx-glr-multi-img
@@ -347,19 +347,19 @@ Extract noisy patches and reconstruct them using the dictionary
.. code-block:: none
Extracting noisy patches...
- done in 0.01s.
+ done in 0.00s.
Orthogonal Matching Pursuit
1 atom...
- done in 0.56s.
+ done in 0.53s.
Orthogonal Matching Pursuit
2 atoms...
- done in 1.14s.
+ done in 1.09s.
Least-angle regression
4 atoms...
- done in 8.42s.
+ done in 8.25s.
Thresholding
alpha=0.1...
- done in 0.22s.
+ done in 0.09s.
@@ -367,7 +367,7 @@ Extract noisy patches and reconstruct them using the dictionary
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 26.410 seconds)
+ **Total running time of the script:** (0 minutes 25.907 seconds)
.. _sphx_glr_download_auto_examples_decomposition_plot_image_denoising.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_incremental_pca.rst.txt b/dev/_sources/auto_examples/decomposition/plot_incremental_pca.rst.txt
index e4a5c464c18ed..2e1e685855d73 100644
--- a/dev/_sources/auto_examples/decomposition/plot_incremental_pca.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_incremental_pca.rst.txt
@@ -109,7 +109,7 @@ incremental approaches.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.228 seconds)
+ **Total running time of the script:** (0 minutes 0.224 seconds)
.. _sphx_glr_download_auto_examples_decomposition_plot_incremental_pca.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_kernel_pca.rst.txt b/dev/_sources/auto_examples/decomposition/plot_kernel_pca.rst.txt
index ad5c1750b0bbd..38f70436a1d58 100644
--- a/dev/_sources/auto_examples/decomposition/plot_kernel_pca.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_kernel_pca.rst.txt
@@ -283,7 +283,7 @@ the mapping.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.578 seconds)
+ **Total running time of the script:** (0 minutes 0.555 seconds)
.. _sphx_glr_download_auto_examples_decomposition_plot_kernel_pca.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_pca_iris.rst.txt b/dev/_sources/auto_examples/decomposition/plot_pca_iris.rst.txt
index 8f61bd75ecfc9..3780716840c9c 100644
--- a/dev/_sources/auto_examples/decomposition/plot_pca_iris.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_pca_iris.rst.txt
@@ -193,7 +193,7 @@ transformation, we see that we can identify each species using only the first fe
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 2.088 seconds)
+ **Total running time of the script:** (0 minutes 2.027 seconds)
.. _sphx_glr_download_auto_examples_decomposition_plot_pca_iris.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_pca_vs_fa_model_selection.rst.txt b/dev/_sources/auto_examples/decomposition/plot_pca_vs_fa_model_selection.rst.txt
index 3faedb57990b2..e92c994d9e749 100644
--- a/dev/_sources/auto_examples/decomposition/plot_pca_vs_fa_model_selection.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_pca_vs_fa_model_selection.rst.txt
@@ -227,7 +227,7 @@ Fit the models
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 3.052 seconds)
+ **Total running time of the script:** (0 minutes 2.953 seconds)
.. _sphx_glr_download_auto_examples_decomposition_plot_pca_vs_fa_model_selection.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_pca_vs_lda.rst.txt b/dev/_sources/auto_examples/decomposition/plot_pca_vs_lda.rst.txt
index 6fb2fd495b8ce..bda71e5665bd8 100644
--- a/dev/_sources/auto_examples/decomposition/plot_pca_vs_lda.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_pca_vs_lda.rst.txt
@@ -124,7 +124,7 @@ LDA, in contrast to PCA, is a supervised method, using known class labels.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.197 seconds)
+ **Total running time of the script:** (0 minutes 0.218 seconds)
.. _sphx_glr_download_auto_examples_decomposition_plot_pca_vs_lda.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_sparse_coding.rst.txt b/dev/_sources/auto_examples/decomposition/plot_sparse_coding.rst.txt
index 1131766db6a86..bd9fa733d7529 100644
--- a/dev/_sources/auto_examples/decomposition/plot_sparse_coding.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_sparse_coding.rst.txt
@@ -158,7 +158,7 @@ is performed in order to stay on the same order of magnitude.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.293 seconds)
+ **Total running time of the script:** (0 minutes 0.256 seconds)
.. _sphx_glr_download_auto_examples_decomposition_plot_sparse_coding.py:
diff --git a/dev/_sources/auto_examples/decomposition/plot_varimax_fa.rst.txt b/dev/_sources/auto_examples/decomposition/plot_varimax_fa.rst.txt
index 48d9643ee1a46..db96db0ec39a6 100644
--- a/dev/_sources/auto_examples/decomposition/plot_varimax_fa.rst.txt
+++ b/dev/_sources/auto_examples/decomposition/plot_varimax_fa.rst.txt
@@ -186,7 +186,7 @@ Run factor analysis with Varimax rotation
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.432 seconds)
+ **Total running time of the script:** (0 minutes 0.436 seconds)
.. _sphx_glr_download_auto_examples_decomposition_plot_varimax_fa.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_adaboost_multiclass.rst.txt b/dev/_sources/auto_examples/ensemble/plot_adaboost_multiclass.rst.txt
index f064875478c3a..fa9f6dd53c257 100644
--- a/dev/_sources/auto_examples/ensemble/plot_adaboost_multiclass.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_adaboost_multiclass.rst.txt
@@ -380,7 +380,7 @@ weights are built to counter-balance the worse performing weak learners.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 4.214 seconds)
+ **Total running time of the script:** (0 minutes 4.243 seconds)
.. _sphx_glr_download_auto_examples_ensemble_plot_adaboost_multiclass.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_adaboost_regression.rst.txt b/dev/_sources/auto_examples/ensemble/plot_adaboost_regression.rst.txt
index 540fc389b198a..9685dc6791b02 100644
--- a/dev/_sources/auto_examples/ensemble/plot_adaboost_regression.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_adaboost_regression.rst.txt
@@ -141,7 +141,7 @@ single decision tree regressor and AdaBoost regressor, could fit the data.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.432 seconds)
+ **Total running time of the script:** (0 minutes 0.442 seconds)
.. _sphx_glr_download_auto_examples_ensemble_plot_adaboost_regression.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_adaboost_twoclass.rst.txt b/dev/_sources/auto_examples/ensemble/plot_adaboost_twoclass.rst.txt
index 800a85ef2b5c7..4c739c8c93dd7 100644
--- a/dev/_sources/auto_examples/ensemble/plot_adaboost_twoclass.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_adaboost_twoclass.rst.txt
@@ -141,7 +141,7 @@ with a decision score above some value.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.633 seconds)
+ **Total running time of the script:** (0 minutes 0.635 seconds)
.. _sphx_glr_download_auto_examples_ensemble_plot_adaboost_twoclass.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_bias_variance.rst.txt b/dev/_sources/auto_examples/ensemble/plot_bias_variance.rst.txt
index 297949c25a823..185b7d62c1f7c 100644
--- a/dev/_sources/auto_examples/ensemble/plot_bias_variance.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_bias_variance.rst.txt
@@ -237,7 +237,7 @@ References
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.082 seconds)
+ **Total running time of the script:** (0 minutes 1.104 seconds)
.. _sphx_glr_download_auto_examples_ensemble_plot_bias_variance.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_ensemble_oob.rst.txt b/dev/_sources/auto_examples/ensemble/plot_ensemble_oob.rst.txt
index 7dfa3b315fd5c..66064a47a70d6 100644
--- a/dev/_sources/auto_examples/ensemble/plot_ensemble_oob.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_ensemble_oob.rst.txt
@@ -137,7 +137,7 @@ error stabilizes.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 3.506 seconds)
+ **Total running time of the script:** (0 minutes 3.623 seconds)
.. _sphx_glr_download_auto_examples_ensemble_plot_ensemble_oob.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_feature_transformation.rst.txt b/dev/_sources/auto_examples/ensemble/plot_feature_transformation.rst.txt
index 42e54b0b9060d..57fe90d5be8b6 100644
--- a/dev/_sources/auto_examples/ensemble/plot_feature_transformation.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_feature_transformation.rst.txt
@@ -1574,7 +1574,7 @@ Therefore, we wrapped the call to `apply` within a `FunctionTransformer`.
cursor: pointer;
}
Pipeline(steps=[('functiontransformer',
- FunctionTransformer(func=<function rf_apply at 0x7fb7fb8b3ac0>,
+ FunctionTransformer(func=<function rf_apply at 0x7f7a65563ac0>,
kw_args={'model': RandomForestClassifier(max_depth=3,
n_estimators=10,
random_state=10)})),
@@ -1642,7 +1642,7 @@ Therefore, we wrapped the call to `apply` within a `FunctionTransformer`.
this.parentElement.nextElementSibling)"
>
func
- <function rf_...x7fb7fb8b3ac0>
+ <function rf_...x7f7a65563ac0>
@@ -2521,7 +2521,7 @@ Therefore, we wrapped the call to `apply` within a `FunctionTransformer`.
cursor: pointer;
}
Pipeline(steps=[('functiontransformer',
- FunctionTransformer(func=<function gbdt_apply at 0x7fb7fb8b2170>,
+ FunctionTransformer(func=<function gbdt_apply at 0x7f7a65562170>,
kw_args={'model': GradientBoostingClassifier(n_estimators=10,
random_state=10)})),
('onehotencoder', OneHotEncoder(handle_unknown='ignore')),
@@ -2588,7 +2588,7 @@ Therefore, we wrapped the call to `apply` within a `FunctionTransformer`.
this.parentElement.nextElementSibling)"
>
func
- <function gbd...x7fb7fb8b2170>
+ <function gbd...x7f7a65562170>
@@ -3030,7 +3030,7 @@ We can finally show the different ROC curves for all the models.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 2.606 seconds)
+ **Total running time of the script:** (0 minutes 2.649 seconds)
.. _sphx_glr_download_auto_examples_ensemble_plot_feature_transformation.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.rst.txt b/dev/_sources/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.rst.txt
index 80278c8367f66..25e59a4be9d71 100644
--- a/dev/_sources/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.rst.txt
@@ -307,7 +307,7 @@ folds of the cross-validation.
@@ -343,7 +343,7 @@ accuracy trade-off of the RF models in this case.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 52.578 seconds)
+ **Total running time of the script:** (0 minutes 53.366 seconds)
.. _sphx_glr_download_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_forest_importances.rst.txt b/dev/_sources/auto_examples/ensemble/plot_forest_importances.rst.txt
index b7be3651bf137..927eba24e8492 100644
--- a/dev/_sources/auto_examples/ensemble/plot_forest_importances.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_forest_importances.rst.txt
@@ -932,7 +932,7 @@ and can be computed on a left-out test set.
.. code-block:: none
- Elapsed time to compute the importances: 0.383 seconds
+ Elapsed time to compute the importances: 0.408 seconds
@@ -977,7 +977,7 @@ permutation importance to fully omit a feature.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.864 seconds)
+ **Total running time of the script:** (0 minutes 0.903 seconds)
.. _sphx_glr_download_auto_examples_ensemble_plot_forest_importances.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_forest_iris.rst.txt b/dev/_sources/auto_examples/ensemble/plot_forest_iris.rst.txt
index c293dace2e96a..57505ad8eab6a 100644
--- a/dev/_sources/auto_examples/ensemble/plot_forest_iris.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_forest_iris.rst.txt
@@ -232,7 +232,7 @@ samples are built sequentially and so do not use multiple cores.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 6.000 seconds)
+ **Total running time of the script:** (0 minutes 6.157 seconds)
.. _sphx_glr_download_auto_examples_ensemble_plot_forest_iris.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_categorical.rst.txt b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_categorical.rst.txt
index ab34b8f1928a6..c14409a748bea 100644
--- a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_categorical.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_categorical.rst.txt
@@ -639,7 +639,7 @@ dropped:
Pipeline(steps=[('columntransformer',
ColumnTransformer(remainder='passthrough',
transformers=[('drop', 'drop',
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb80abe0940>)])),
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a8570fc10>)])),
('histgradientboostingregressor',
HistGradientBoostingRegressor(random_state=42))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
@@ -781,7 +781,7 @@ dropped:
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb80abe0940>
drop
passthrough
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a8570fc10>
drop
passthrough
Parameters
@@ -1565,7 +1565,7 @@ and let the rest of the numerical data to passthrough:
transformers=[('onehotencoder',
OneHotEncoder(handle_unknown='ignore',
sparse_output=False),
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb80abe3dc0>)])),
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a8570e5c0>)])),
('histgradientboostingregressor',
HistGradientBoostingRegressor(random_state=42))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
@@ -1707,7 +1707,7 @@ and let the rest of the numerical data to passthrough:
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb80abe3dc0>
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a8570e5c0>
Parameters
@@ -2588,7 +2588,7 @@ etc., and treated as continuous features.
transformers=[('ordinalencoder',
OrdinalEncoder(handle_unknown='use_encoded_value',
unknown_value=nan),
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb80abe2020>)],
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a8570f340>)],
verbose_feature_names_out=False)),
('histgradientboostingregressor',
HistGradientBoostingRegressor(random_state=42))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
@@ -2731,7 +2731,7 @@ etc., and treated as continuous features.
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb80abe2020>
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a8570f340>
Parameters
@@ -4082,7 +4082,7 @@ to the baseline model that just dropped the categorical features altogether.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 3.974 seconds)
+ **Total running time of the script:** (0 minutes 3.809 seconds)
.. _sphx_glr_download_auto_examples_ensemble_plot_gradient_boosting_categorical.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_early_stopping.rst.txt b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_early_stopping.rst.txt
index b673100e4f6dd..b4333761b9704 100644
--- a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_early_stopping.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_early_stopping.rst.txt
@@ -273,7 +273,7 @@ practical benefits of early stopping:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 3.182 seconds)
+ **Total running time of the script:** (0 minutes 3.222 seconds)
.. _sphx_glr_download_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_oob.rst.txt b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_oob.rst.txt
index 558b0b8f9c713..7af9c69b1959b 100644
--- a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_oob.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_oob.rst.txt
@@ -189,7 +189,7 @@ but is computationally more demanding.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 9.728 seconds)
+ **Total running time of the script:** (0 minutes 9.503 seconds)
.. _sphx_glr_download_auto_examples_ensemble_plot_gradient_boosting_oob.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_quantile.rst.txt b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_quantile.rst.txt
index 42c0beb5a2087..936d8e30ec450 100644
--- a/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_quantile.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_gradient_boosting_quantile.rst.txt
@@ -292,18 +292,18 @@ Measure the models with :func:`~sklearn.metrics.mean_squared_error` and
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198fa560>
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc3670>
Parameters
@@ -1500,10 +1500,10 @@ is a linear model.
StandardScaler()),
('simpleimputer',
SimpleImputer(add_indicator=True))]),
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198f80d0>),
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc2aa0>),
('onehotencoder',
OneHotEncoder(handle_unknown='ignore'),
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198fa560>)])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc3670>)])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Parameters
@@ -1593,7 +1593,7 @@ is a linear model.
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198f80d0>
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc2aa0>
Parameters
@@ -1703,7 +1703,7 @@ is a linear model.
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198fa560>
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc3670>
Parameters
@@ -2362,10 +2362,10 @@ to combine their outputs together.
StandardScaler()),
('simpleimputer',
SimpleImputer(add_indicator=True))]),
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198f80d0>),
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc2aa0>),
('onehotencoder',
OneHotEncoder(handle_unknown='ignore'),
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198fa560>)])),
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc3670>)])),
('lassocv', LassoCV())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
@@ -2506,7 +2506,7 @@ to combine their outputs together.
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198f80d0>
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc2aa0>
Parameters
@@ -2616,7 +2616,7 @@ to combine their outputs together.
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198fa560>
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc3670>
Parameters
@@ -3400,12 +3400,12 @@ to combine their outputs together.
Pipeline(steps=[('columntransformer',
ColumnTransformer(transformers=[('simpleimputer',
SimpleImputer(add_indicator=True),
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198f80d0>),
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc2aa0>),
('ordinalencoder',
OrdinalEncoder(encoded_missing_value=-2,
handle_unknown='use_encoded_value',
unknown_value=-1),
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198fa560>)])),
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc3670>)])),
('randomforestregressor',
RandomForestRegressor(random_state=42))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
@@ -3547,7 +3547,7 @@ to combine their outputs together.
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198f80d0>
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc2aa0>
Parameters
@@ -3617,7 +3617,7 @@ to combine their outputs together.
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198fa560>
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc3670>
Parameters
@@ -4433,12 +4433,12 @@ to combine their outputs together.
Pipeline(steps=[('columntransformer',
ColumnTransformer(transformers=[('simpleimputer',
SimpleImputer(add_indicator=True),
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198f80d0>),
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc2aa0>),
('ordinalencoder',
OrdinalEncoder(encoded_missing_value=-2,
handle_unknown='use_encoded_value',
unknown_value=-1),
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198fa560>)])),
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc3670>)])),
('histgradientboostingregressor',
HistGradientBoostingRegressor(random_state=0))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
@@ -4580,7 +4580,7 @@ to combine their outputs together.
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198f80d0>
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc2aa0>
Parameters
@@ -4650,7 +4650,7 @@ to combine their outputs together.
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198fa560>
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc3670>
Parameters
@@ -5502,17 +5502,17 @@ to combine their outputs together.
Pipeline(steps=[('columntransformer',
ColumnTransformer(transformers=[('simpleimputer',
SimpleImputer(add_indicator=True),
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198f80d0>),
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc2aa0>),
('ordinalencoder',
OrdinalEncoder(encoded_missing_value=-2,
handle_unknown='use_encoded_value',
unknown_v...
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198f80d0>),
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc2aa0>),
('ordinalencoder',
OrdinalEncoder(encoded_missing_value=-2,
handle_unknown='use_encoded_value',
unknown_value=-1),
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198fa560>)])),
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc3670>)])),
('histgradientboostingregressor',
HistGradientBoostingRegressor(random_state=0))]))],
final_estimator=RidgeCV())In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
@@ -5675,7 +5675,7 @@ to combine their outputs together.
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198f80d0>
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc2aa0>
Parameters
@@ -5745,7 +5745,7 @@ to combine their outputs together.
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198fa560>
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc3670>
Parameters
@@ -6105,7 +6105,7 @@ to combine their outputs together.
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198f80d0>
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc2aa0>
Parameters
@@ -6215,7 +6215,7 @@ to combine their outputs together.
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198fa560>
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc3670>
Parameters
@@ -6545,7 +6545,7 @@ to combine their outputs together.
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198f80d0>
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc2aa0>
Parameters
@@ -6615,7 +6615,7 @@ to combine their outputs together.
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb8198fa560>
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a5fbc3670>
Parameters
@@ -7110,7 +7110,7 @@ regressors.
.. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_stack_predictors_001.png
- :alt: Single predictors versus stacked predictors, Random Forest Evaluation in 1.12 seconds, Lasso Evaluation in 0.24 seconds, Gradient Boosting Evaluation in 0.46 seconds, Stacking Regressor Evaluation in 9.03 seconds
+ :alt: Single predictors versus stacked predictors, Random Forest Evaluation in 1.04 seconds, Lasso Evaluation in 0.23 seconds, Gradient Boosting Evaluation in 0.45 seconds, Stacking Regressor Evaluation in 8.92 seconds
:srcset: /auto_examples/ensemble/images/sphx_glr_plot_stack_predictors_001.png
:class: sphx-glr-single-img
@@ -7127,7 +7127,7 @@ computationally expensive.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 22.090 seconds)
+ **Total running time of the script:** (0 minutes 21.883 seconds)
.. _sphx_glr_download_auto_examples_ensemble_plot_stack_predictors.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_voting_decision_regions.rst.txt b/dev/_sources/auto_examples/ensemble/plot_voting_decision_regions.rst.txt
index e984332a7bb14..4c182ab5e15a9 100644
--- a/dev/_sources/auto_examples/ensemble/plot_voting_decision_regions.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_voting_decision_regions.rst.txt
@@ -1920,7 +1920,7 @@ probability.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.630 seconds)
+ **Total running time of the script:** (0 minutes 0.638 seconds)
.. _sphx_glr_download_auto_examples_ensemble_plot_voting_decision_regions.py:
diff --git a/dev/_sources/auto_examples/ensemble/plot_voting_regressor.rst.txt b/dev/_sources/auto_examples/ensemble/plot_voting_regressor.rst.txt
index 4a011dd0022e6..0a317a1035ccd 100644
--- a/dev/_sources/auto_examples/ensemble/plot_voting_regressor.rst.txt
+++ b/dev/_sources/auto_examples/ensemble/plot_voting_regressor.rst.txt
@@ -1214,7 +1214,7 @@ prediction made by :class:`~ensemble.VotingRegressor`.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.928 seconds)
+ **Total running time of the script:** (0 minutes 0.968 seconds)
.. _sphx_glr_download_auto_examples_ensemble_plot_voting_regressor.py:
diff --git a/dev/_sources/auto_examples/feature_selection/plot_f_test_vs_mi.rst.txt b/dev/_sources/auto_examples/feature_selection/plot_f_test_vs_mi.rst.txt
index ed089b3196f54..aa89bade91aa6 100644
--- a/dev/_sources/auto_examples/feature_selection/plot_f_test_vs_mi.rst.txt
+++ b/dev/_sources/auto_examples/feature_selection/plot_f_test_vs_mi.rst.txt
@@ -87,7 +87,7 @@ perception for this example. Both methods correctly mark x_3 as irrelevant.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.222 seconds)
+ **Total running time of the script:** (0 minutes 0.259 seconds)
.. _sphx_glr_download_auto_examples_feature_selection_plot_f_test_vs_mi.py:
diff --git a/dev/_sources/auto_examples/feature_selection/plot_feature_selection.rst.txt b/dev/_sources/auto_examples/feature_selection/plot_feature_selection.rst.txt
index cfd0a77a1d936..2efb8da3015ca 100644
--- a/dev/_sources/auto_examples/feature_selection/plot_feature_selection.rst.txt
+++ b/dev/_sources/auto_examples/feature_selection/plot_feature_selection.rst.txt
@@ -260,7 +260,7 @@ and will thus improve classification.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.187 seconds)
+ **Total running time of the script:** (0 minutes 0.189 seconds)
.. _sphx_glr_download_auto_examples_feature_selection_plot_feature_selection.py:
diff --git a/dev/_sources/auto_examples/feature_selection/plot_feature_selection_pipeline.rst.txt b/dev/_sources/auto_examples/feature_selection/plot_feature_selection_pipeline.rst.txt
index 55e8ab3f90043..f7393fd3d261d 100644
--- a/dev/_sources/auto_examples/feature_selection/plot_feature_selection_pipeline.rst.txt
+++ b/dev/_sources/auto_examples/feature_selection/plot_feature_selection_pipeline.rst.txt
@@ -649,7 +649,7 @@ classifier which will be trained.
this.parentElement.nextElementSibling)"
>
score_func
- <function f_c...x7fb81b1ab640>
+ <function f_c...x7f7a8437b640>
diff --git a/dev/_sources/auto_examples/feature_selection/plot_rfe_digits.rst.txt b/dev/_sources/auto_examples/feature_selection/plot_rfe_digits.rst.txt
index 185aeb3061aa6..79a67149a348a 100644
--- a/dev/_sources/auto_examples/feature_selection/plot_rfe_digits.rst.txt
+++ b/dev/_sources/auto_examples/feature_selection/plot_rfe_digits.rst.txt
@@ -92,7 +92,7 @@ at the center of the image tend to be more predictive than those near the edges.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 3.301 seconds)
+ **Total running time of the script:** (0 minutes 2.928 seconds)
.. _sphx_glr_download_auto_examples_feature_selection_plot_rfe_digits.py:
diff --git a/dev/_sources/auto_examples/feature_selection/plot_rfe_with_cross_validation.rst.txt b/dev/_sources/auto_examples/feature_selection/plot_rfe_with_cross_validation.rst.txt
index 6a9419a2775bd..9a0cc5b13a546 100644
--- a/dev/_sources/auto_examples/feature_selection/plot_rfe_with_cross_validation.rst.txt
+++ b/dev/_sources/auto_examples/feature_selection/plot_rfe_with_cross_validation.rst.txt
@@ -218,7 +218,7 @@ these features are the most informative ones.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.915 seconds)
+ **Total running time of the script:** (0 minutes 0.797 seconds)
.. _sphx_glr_download_auto_examples_feature_selection_plot_rfe_with_cross_validation.py:
diff --git a/dev/_sources/auto_examples/feature_selection/plot_select_from_model_diabetes.rst.txt b/dev/_sources/auto_examples/feature_selection/plot_select_from_model_diabetes.rst.txt
index 9299bf50bf205..ed3fa19bfcad0 100644
--- a/dev/_sources/auto_examples/feature_selection/plot_select_from_model_diabetes.rst.txt
+++ b/dev/_sources/auto_examples/feature_selection/plot_select_from_model_diabetes.rst.txt
@@ -258,9 +258,9 @@ both approaches here.
.. code-block:: none
Features selected by forward sequential selection: ['bmi' 's5']
- Done in 0.190s
+ Done in 0.193s
Features selected by backward sequential selection: ['bmi' 's5']
- Done in 0.603s
+ Done in 0.554s
@@ -488,13 +488,13 @@ to perform the feature selection.
tol: -0.01
Features selected: ['worst perimeter']
ROC AUC score: 0.975
- Done in 19.969s
+ Done in 20.170s
tol: -0.001
Features selected: ['radius error' 'fractal dimension error' 'worst texture'
'worst perimeter' 'worst concave points']
ROC AUC score: 0.997
- Done in 20.192s
+ Done in 19.396s
tol: -0.0001
Features selected: ['mean compactness' 'mean concavity' 'mean concave points' 'radius error'
@@ -502,7 +502,7 @@ to perform the feature selection.
'fractal dimension error' 'worst texture' 'worst perimeter' 'worst area'
'worst concave points' 'worst symmetry']
ROC AUC score: 0.998
- Done in 17.312s
+ Done in 16.554s
@@ -516,7 +516,7 @@ decreases as the values of `tol` come closer to zero.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 58.378 seconds)
+ **Total running time of the script:** (0 minutes 56.967 seconds)
.. _sphx_glr_download_auto_examples_feature_selection_plot_select_from_model_diabetes.py:
diff --git a/dev/_sources/auto_examples/frozen/plot_frozen_examples.rst.txt b/dev/_sources/auto_examples/frozen/plot_frozen_examples.rst.txt
index 349421fe57a44..535c0cca55cdb 100644
--- a/dev/_sources/auto_examples/frozen/plot_frozen_examples.rst.txt
+++ b/dev/_sources/auto_examples/frozen/plot_frozen_examples.rst.txt
@@ -211,7 +211,7 @@ classifier using :class:`~sklearn.calibration.CalibratedClassifierCV`.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.017 seconds)
+ **Total running time of the script:** (0 minutes 0.018 seconds)
.. _sphx_glr_download_auto_examples_frozen_plot_frozen_examples.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_compare_gpr_krr.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_compare_gpr_krr.rst.txt
index 3e823f6588bad..04c6d7f30df0c 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_compare_gpr_krr.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_compare_gpr_krr.rst.txt
@@ -352,7 +352,7 @@ parameter and the kernel parameters.
.. code-block:: none
- Time for KernelRidge fitting: 3.893 seconds
+ Time for KernelRidge fitting: 4.022 seconds
@@ -714,7 +714,7 @@ also increases.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 4.538 seconds)
+ **Total running time of the script:** (0 minutes 4.722 seconds)
.. _sphx_glr_download_auto_examples_gaussian_process_plot_compare_gpr_krr.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpc.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpc.rst.txt
index 2ed2a58cda776..f91f147414a1a 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpc.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpc.rst.txt
@@ -187,7 +187,7 @@ hyperparameters used in the first figure by black dots.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 2.311 seconds)
+ **Total running time of the script:** (0 minutes 2.341 seconds)
.. _sphx_glr_download_auto_examples_gaussian_process_plot_gpc.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpc_iris.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpc_iris.rst.txt
index 5f4ae6e7891f3..558ce62db716f 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpc_iris.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpc_iris.rst.txt
@@ -101,7 +101,7 @@ assigning different length-scales to the two feature dimensions.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 12.152 seconds)
+ **Total running time of the script:** (0 minutes 8.146 seconds)
.. _sphx_glr_download_auto_examples_gaussian_process_plot_gpc_iris.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpc_isoprobability.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpc_isoprobability.rst.txt
index 5cff9f202b240..501aab5974a12 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpc_isoprobability.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpc_isoprobability.rst.txt
@@ -142,7 +142,7 @@ the predicted probabilities.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.170 seconds)
+ **Total running time of the script:** (0 minutes 0.153 seconds)
.. _sphx_glr_download_auto_examples_gaussian_process_plot_gpc_isoprobability.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpc_xor.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpc_xor.rst.txt
index 1e85c86b68f10..a2a6480dfdcae 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpc_xor.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpc_xor.rst.txt
@@ -108,7 +108,7 @@ stationary kernels often obtain better results.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.508 seconds)
+ **Total running time of the script:** (0 minutes 0.539 seconds)
.. _sphx_glr_download_auto_examples_gaussian_process_plot_gpc_xor.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpr_co2.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpr_co2.rst.txt
index db73f1903a9cc..c53f87a87480c 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpr_co2.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpr_co2.rst.txt
@@ -1617,7 +1617,7 @@ indicating that the data can be very well explained by the model.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 4.477 seconds)
+ **Total running time of the script:** (0 minutes 4.586 seconds)
.. _sphx_glr_download_auto_examples_gaussian_process_plot_gpr_co2.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpr_noisy.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpr_noisy.rst.txt
index e2351b8c9d2e0..eb3bb60ed0349 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpr_noisy.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpr_noisy.rst.txt
@@ -448,7 +448,7 @@ of hyperparameters despite the bad initial values.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 5.848 seconds)
+ **Total running time of the script:** (0 minutes 6.214 seconds)
.. _sphx_glr_download_auto_examples_gaussian_process_plot_gpr_noisy.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpr_noisy_targets.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpr_noisy_targets.rst.txt
index 12965bc67bd89..f01e413b871ab 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpr_noisy_targets.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpr_noisy_targets.rst.txt
@@ -305,7 +305,7 @@ variable.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.513 seconds)
+ **Total running time of the script:** (0 minutes 0.539 seconds)
.. _sphx_glr_download_auto_examples_gaussian_process_plot_gpr_noisy_targets.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpr_on_structured_data.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpr_on_structured_data.rst.txt
index 0f27832f2cd88..cc904a5d10484 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpr_on_structured_data.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpr_on_structured_data.rst.txt
@@ -293,7 +293,7 @@ Classification
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.246 seconds)
+ **Total running time of the script:** (0 minutes 0.216 seconds)
.. _sphx_glr_download_auto_examples_gaussian_process_plot_gpr_on_structured_data.py:
diff --git a/dev/_sources/auto_examples/gaussian_process/plot_gpr_prior_posterior.rst.txt b/dev/_sources/auto_examples/gaussian_process/plot_gpr_prior_posterior.rst.txt
index 2bd0d4cbb039f..ec68dafd13797 100644
--- a/dev/_sources/auto_examples/gaussian_process/plot_gpr_prior_posterior.rst.txt
+++ b/dev/_sources/auto_examples/gaussian_process/plot_gpr_prior_posterior.rst.txt
@@ -503,7 +503,7 @@ Matérn kernel
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.631 seconds)
+ **Total running time of the script:** (0 minutes 1.578 seconds)
.. _sphx_glr_download_auto_examples_gaussian_process_plot_gpr_prior_posterior.py:
diff --git a/dev/_sources/auto_examples/impute/plot_iterative_imputer_variants_comparison.rst.txt b/dev/_sources/auto_examples/impute/plot_iterative_imputer_variants_comparison.rst.txt
index 29bf0484fdf80..b7d8e0835b9f9 100644
--- a/dev/_sources/auto_examples/impute/plot_iterative_imputer_variants_comparison.rst.txt
+++ b/dev/_sources/auto_examples/impute/plot_iterative_imputer_variants_comparison.rst.txt
@@ -206,7 +206,7 @@ complex and costly missing values imputation strategies.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 6.331 seconds)
+ **Total running time of the script:** (0 minutes 6.600 seconds)
.. _sphx_glr_download_auto_examples_impute_plot_iterative_imputer_variants_comparison.py:
diff --git a/dev/_sources/auto_examples/impute/plot_missing_values.rst.txt b/dev/_sources/auto_examples/impute/plot_missing_values.rst.txt
index fb84651b9e3f5..8f36ed9d5879c 100644
--- a/dev/_sources/auto_examples/impute/plot_missing_values.rst.txt
+++ b/dev/_sources/auto_examples/impute/plot_missing_values.rst.txt
@@ -413,7 +413,7 @@ results (otherwise known as a 'long tail').
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 8.831 seconds)
+ **Total running time of the script:** (0 minutes 9.147 seconds)
.. _sphx_glr_download_auto_examples_impute_plot_missing_values.py:
diff --git a/dev/_sources/auto_examples/inspection/plot_causal_interpretation.rst.txt b/dev/_sources/auto_examples/inspection/plot_causal_interpretation.rst.txt
index be9c25adcd9ad..94ed1b71f9cb7 100644
--- a/dev/_sources/auto_examples/inspection/plot_causal_interpretation.rst.txt
+++ b/dev/_sources/auto_examples/inspection/plot_causal_interpretation.rst.txt
@@ -340,7 +340,7 @@ estimations.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 2.134 seconds)
+ **Total running time of the script:** (0 minutes 2.079 seconds)
.. _sphx_glr_download_auto_examples_inspection_plot_causal_interpretation.py:
diff --git a/dev/_sources/auto_examples/inspection/plot_linear_model_coefficient_interpretation.rst.txt b/dev/_sources/auto_examples/inspection/plot_linear_model_coefficient_interpretation.rst.txt
index a3361314c4535..82d9d328a3d1a 100644
--- a/dev/_sources/auto_examples/inspection/plot_linear_model_coefficient_interpretation.rst.txt
+++ b/dev/_sources/auto_examples/inspection/plot_linear_model_coefficient_interpretation.rst.txt
@@ -4657,7 +4657,7 @@ Lessons learned
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 12.481 seconds)
+ **Total running time of the script:** (0 minutes 13.482 seconds)
.. _sphx_glr_download_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py:
diff --git a/dev/_sources/auto_examples/inspection/plot_partial_dependence.rst.txt b/dev/_sources/auto_examples/inspection/plot_partial_dependence.rst.txt
index 7c5f72227c6ee..673a680b47d34 100644
--- a/dev/_sources/auto_examples/inspection/plot_partial_dependence.rst.txt
+++ b/dev/_sources/auto_examples/inspection/plot_partial_dependence.rst.txt
@@ -1941,7 +1941,7 @@ single-variable partial dependence plots.
.. code-block:: none
Training MLPRegressor...
- done in 0.572s
+ done in 0.601s
Test R2 score: 0.61
@@ -2025,7 +2025,7 @@ We will plot the averaged partial dependence.
.. code-block:: none
Computing partial dependence plots...
- done in 0.529s
+ done in 0.583s
@@ -2068,7 +2068,7 @@ specific preprocessor we created for this model.
.. code-block:: none
Training HistGradientBoostingRegressor...
- done in 0.128s
+ done in 0.170s
Test R2 score: 0.62
@@ -2125,7 +2125,7 @@ features.
.. code-block:: none
Computing partial dependence plots...
- done in 1.004s
+ done in 1.242s
@@ -2203,7 +2203,7 @@ selected ICEs for the temperature and humidity features.
.. code-block:: none
Computing partial dependence plots and individual conditional expectation...
- done in 0.436s
+ done in 0.544s
@@ -2324,7 +2324,7 @@ heatmap.
.. code-block:: none
Computing partial dependence plots...
- done in 6.799s
+ done in 7.795s
@@ -2385,7 +2385,7 @@ non-linear feature interactions.
.. code-block:: none
Computing partial dependence plots...
- done in 6.213s
+ done in 7.217s
@@ -2453,7 +2453,7 @@ the season, the weather, and the target would be as follow:
.. code-block:: none
Computing partial dependence plots...
- done in 0.329s
+ done in 0.357s
@@ -2576,7 +2576,7 @@ but with custom values
.. code-block:: none
Computing partial dependence plots with custom evaluation values...
- done in 0.493s
+ done in 0.458s
@@ -2584,7 +2584,7 @@ but with custom values
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 21.299 seconds)
+ **Total running time of the script:** (0 minutes 24.172 seconds)
.. _sphx_glr_download_auto_examples_inspection_plot_partial_dependence.py:
diff --git a/dev/_sources/auto_examples/inspection/plot_permutation_importance.rst.txt b/dev/_sources/auto_examples/inspection/plot_permutation_importance.rst.txt
index 8d3c2f2aef67d..02b0ecccc379e 100644
--- a/dev/_sources/auto_examples/inspection/plot_permutation_importance.rst.txt
+++ b/dev/_sources/auto_examples/inspection/plot_permutation_importance.rst.txt
@@ -2522,7 +2522,7 @@ still valid.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 4.412 seconds)
+ **Total running time of the script:** (0 minutes 4.739 seconds)
.. _sphx_glr_download_auto_examples_inspection_plot_permutation_importance.py:
diff --git a/dev/_sources/auto_examples/inspection/plot_permutation_importance_multicollinear.rst.txt b/dev/_sources/auto_examples/inspection/plot_permutation_importance_multicollinear.rst.txt
index 7803eeab940e9..cf7e99e36f69e 100644
--- a/dev/_sources/auto_examples/inspection/plot_permutation_importance_multicollinear.rst.txt
+++ b/dev/_sources/auto_examples/inspection/plot_permutation_importance_multicollinear.rst.txt
@@ -345,7 +345,7 @@ features:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 4.395 seconds)
+ **Total running time of the script:** (0 minutes 4.603 seconds)
.. _sphx_glr_download_auto_examples_inspection_plot_permutation_importance_multicollinear.py:
diff --git a/dev/_sources/auto_examples/kernel_approximation/plot_scalable_poly_kernels.rst.txt b/dev/_sources/auto_examples/kernel_approximation/plot_scalable_poly_kernels.rst.txt
index a6b2acdafd33e..dd435aedf8848 100644
--- a/dev/_sources/auto_examples/kernel_approximation/plot_scalable_poly_kernels.rst.txt
+++ b/dev/_sources/auto_examples/kernel_approximation/plot_scalable_poly_kernels.rst.txt
@@ -400,7 +400,7 @@ https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 23.927 seconds)
+ **Total running time of the script:** (0 minutes 39.138 seconds)
.. _sphx_glr_download_auto_examples_kernel_approximation_plot_scalable_poly_kernels.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_ard.rst.txt b/dev/_sources/auto_examples/linear_model/plot_ard.rst.txt
index 58700b6efc2cd..507e825ec52c3 100644
--- a/dev/_sources/auto_examples/linear_model/plot_ard.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_ard.rst.txt
@@ -351,7 +351,7 @@ models fail when extrapolating.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.839 seconds)
+ **Total running time of the script:** (0 minutes 0.661 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_ard.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_bayesian_ridge_curvefit.rst.txt b/dev/_sources/auto_examples/linear_model/plot_bayesian_ridge_curvefit.rst.txt
index 2fbc4439f8cc0..8e24b2c11a780 100644
--- a/dev/_sources/auto_examples/linear_model/plot_bayesian_ridge_curvefit.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_bayesian_ridge_curvefit.rst.txt
@@ -167,7 +167,7 @@ Plot the true and predicted curves with log marginal likelihood (L)
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.291 seconds)
+ **Total running time of the script:** (0 minutes 0.311 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_bayesian_ridge_curvefit.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.rst.txt b/dev/_sources/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.rst.txt
index fc9107fe24a2c..cf3fd8ded83b8 100644
--- a/dev/_sources/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.rst.txt
@@ -783,7 +783,7 @@ matrix, the preprocessing code will incorrectly rescale it a second time.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.422 seconds)
+ **Total running time of the script:** (0 minutes 2.279 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_huber_vs_ridge.rst.txt b/dev/_sources/auto_examples/linear_model/plot_huber_vs_ridge.rst.txt
index a5f67681296fe..35de26e1a2416 100644
--- a/dev/_sources/auto_examples/linear_model/plot_huber_vs_ridge.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_huber_vs_ridge.rst.txt
@@ -99,7 +99,7 @@ function approaches that of the ridge.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.102 seconds)
+ **Total running time of the script:** (0 minutes 0.104 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_huber_vs_ridge.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_lasso_and_elasticnet.rst.txt b/dev/_sources/auto_examples/linear_model/plot_lasso_and_elasticnet.rst.txt
index 3a89fb45921cf..ca12c654ae359 100644
--- a/dev/_sources/auto_examples/linear_model/plot_lasso_and_elasticnet.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_lasso_and_elasticnet.rst.txt
@@ -288,7 +288,7 @@ comparison of :class:`~sklearn.linear_model.ARDRegression` and
.. code-block:: none
- ARD fit done in 0.045s
+ ARD fit done in 0.044s
ARD r^2 on test data : 0.543
@@ -437,7 +437,7 @@ References
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.455 seconds)
+ **Total running time of the script:** (0 minutes 0.469 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_lasso_and_elasticnet.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.rst.txt b/dev/_sources/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.rst.txt
index ebb36995217f0..da74a9bdaa055 100644
--- a/dev/_sources/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.rst.txt
@@ -94,8 +94,8 @@ runtime with a dense data format.
.. code-block:: none
- Sparse Lasso done in 0.109s
- Dense Lasso done in 0.042s
+ Sparse Lasso done in 0.108s
+ Dense Lasso done in 0.032s
Distance between coefficients : 1.01e-13
@@ -151,8 +151,8 @@ expect the implementation that uses the sparse data format to be faster.
.. code-block:: none
Matrix density : 0.626%
- Sparse Lasso done in 0.180s
- Dense Lasso done in 0.916s
+ Sparse Lasso done in 0.186s
+ Dense Lasso done in 0.748s
Distance between coefficients : 1.10e-11
@@ -161,7 +161,7 @@ expect the implementation that uses the sparse data format to be faster.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.319 seconds)
+ **Total running time of the script:** (0 minutes 1.148 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_lasso_dense_vs_sparse_data.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_lasso_lars_ic.rst.txt b/dev/_sources/auto_examples/linear_model/plot_lasso_lars_ic.rst.txt
index f850ef7c37ded..6d82d5479639d 100644
--- a/dev/_sources/auto_examples/linear_model/plot_lasso_lars_ic.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_lasso_lars_ic.rst.txt
@@ -346,7 +346,7 @@ regularization parameter.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.112 seconds)
+ **Total running time of the script:** (0 minutes 0.106 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_lasso_lars_ic.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.rst.txt b/dev/_sources/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.rst.txt
index 8af499a80253d..eb50edad5d8c2 100644
--- a/dev/_sources/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.rst.txt
@@ -220,7 +220,7 @@ under different constraints.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.854 seconds)
+ **Total running time of the script:** (0 minutes 0.897 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_lasso_lasso_lars_elasticnet_path.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_lasso_model_selection.rst.txt b/dev/_sources/auto_examples/linear_model/plot_lasso_model_selection.rst.txt
index 0673464a183f8..1e8fba9f9210e 100644
--- a/dev/_sources/auto_examples/linear_model/plot_lasso_model_selection.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_lasso_model_selection.rst.txt
@@ -397,16 +397,16 @@ We can check which value of `alpha` leads to the minimum AIC and BIC.
-
+
- AIC criterion
- BIC criterion
+ AIC criterion
+ BIC criterion
alphas
@@ -416,139 +416,139 @@ We can check which value of `alpha` leads to the minimum AIC and BIC.
- 45.160030
- 5244.764779
- 5244.764779
+ 45.160030
+ 5244.764779
+ 5244.764779
- 42.300343
- 5208.250639
- 5212.341949
+ 42.300343
+ 5208.250639
+ 5212.341949
- 21.542052
- 4928.018900
- 4936.201520
+ 21.542052
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+ 4936.201520
- 15.034077
- 4869.678359
- 4881.952289
+ 15.034077
+ 4869.678359
+ 4881.952289
- 6.189631
- 4815.437362
- 4831.802601
+ 6.189631
+ 4815.437362
+ 4831.802601
- 5.329616
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+ 5.329616
+ 4810.423641
+ 4830.880191
- 4.306012
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+ 4.306012
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- 4.124225
- 4804.126502
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+ 4.124225
+ 4804.126502
+ 4832.765671
- 3.820705
- 4803.621645
- 4836.352124
+ 3.820705
+ 4803.621645
+ 4836.352124
- 3.750389
- 4805.012521
- 4841.834310
+ 3.750389
+ 4805.012521
+ 4841.834310
- 3.570655
- 4805.290075
- 4846.203174
+ 3.570655
+ 4805.290075
+ 4846.203174
- 3.550213
- 4807.075887
- 4852.080295
+ 3.550213
+ 4807.075887
+ 4852.080295
- 3.358295
- 4806.878051
- 4855.973770
+ 3.358295
+ 4806.878051
+ 4855.973770
- 3.259297
- 4807.706026
- 4860.893055
+ 3.259297
+ 4807.706026
+ 4860.893055
- 3.237703
- 4809.440409
- 4866.718747
+ 3.237703
+ 4809.440409
+ 4866.718747
- 2.850031
- 4805.989341
- 4867.358990
+ 2.850031
+ 4805.989341
+ 4867.358990
- 2.384338
- 4801.702266
- 4867.163224
+ 2.384338
+ 4801.702266
+ 4867.163224
- 2.296575
- 4802.594754
- 4872.147022
+ 2.296575
+ 4802.594754
+ 4872.147022
- 2.031555
- 4801.236720
- 4874.880298
+ 2.031555
+ 4801.236720
+ 4874.880298
- 1.618263
- 4798.484109
- 4876.218997
+ 1.618263
+ 4798.484109
+ 4876.218997
- 1.526599
- 4799.543841
- 4881.370039
+ 1.526599
+ 4799.543841
+ 4881.370039
- 0.586798
- 4794.238744
- 4880.156252
+ 0.586798
+ 4794.238744
+ 4880.156252
- 0.445978
- 4795.589715
- 4885.598533
+ 0.445978
+ 4795.589715
+ 4885.598533
- 0.259031
- 4796.966981
- 4891.067109
+ 0.259031
+ 4796.966981
+ 4891.067109
- 0.032179
- 4796.662409
- 4894.853846
+ 0.032179
+ 4796.662409
+ 4894.853846
- 0.019069
- 4794.652739
- 4888.752867
+ 0.019069
+ 4794.652739
+ 4888.752867
- 0.000000
- 4796.626286
- 4894.817724
+ 0.000000
+ 4796.626286
+ 4894.817724
@@ -685,7 +685,7 @@ Let's start by making the hyperparameter tuning using
.. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_lasso_model_selection_002.png
- :alt: Mean square error on each fold: coordinate descent (train time: 0.24s)
+ :alt: Mean square error on each fold: coordinate descent (train time: 0.26s)
:srcset: /auto_examples/linear_model/images/sphx_glr_plot_lasso_model_selection_002.png
:class: sphx-glr-single-img
@@ -792,7 +792,7 @@ strategy: it works in different settings.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.895 seconds)
+ **Total running time of the script:** (0 minutes 0.958 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_lasso_model_selection.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_logistic.rst.txt b/dev/_sources/auto_examples/linear_model/plot_logistic.rst.txt
index 3e691cbaaf734..9d389027d3972 100644
--- a/dev/_sources/auto_examples/linear_model/plot_logistic.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_logistic.rst.txt
@@ -101,7 +101,7 @@ i.e. class one or two, using the logistic curve.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.113 seconds)
+ **Total running time of the script:** (0 minutes 0.116 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_logistic.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.rst.txt b/dev/_sources/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.rst.txt
index 7de5ed771cb06..3e6a3b824ad4a 100644
--- a/dev/_sources/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.rst.txt
@@ -153,7 +153,7 @@ The visualization shows coefficients of the models for varying C.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.411 seconds)
+ **Total running time of the script:** (0 minutes 0.509 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_logistic_l1_l2_sparsity.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_logistic_multinomial.rst.txt b/dev/_sources/auto_examples/linear_model/plot_logistic_multinomial.rst.txt
index 8dc351e146ae3..963c1e21bd3c2 100644
--- a/dev/_sources/auto_examples/linear_model/plot_logistic_multinomial.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_logistic_multinomial.rst.txt
@@ -303,7 +303,7 @@ a utility function would.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.597 seconds)
+ **Total running time of the script:** (0 minutes 0.594 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_logistic_multinomial.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_logistic_path.rst.txt b/dev/_sources/auto_examples/linear_model/plot_logistic_path.rst.txt
index 6320c2858c3b1..020b2b3f48366 100644
--- a/dev/_sources/auto_examples/linear_model/plot_logistic_path.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_logistic_path.rst.txt
@@ -203,7 +203,7 @@ Plot regularization path
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.165 seconds)
+ **Total running time of the script:** (0 minutes 0.204 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_logistic_path.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_multi_task_lasso_support.rst.txt b/dev/_sources/auto_examples/linear_model/plot_multi_task_lasso_support.rst.txt
index aabde670d97d6..1b8a4bae09472 100644
--- a/dev/_sources/auto_examples/linear_model/plot_multi_task_lasso_support.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_multi_task_lasso_support.rst.txt
@@ -168,7 +168,7 @@ Plot support and time series
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.221 seconds)
+ **Total running time of the script:** (0 minutes 0.251 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_multi_task_lasso_support.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_nnls.rst.txt b/dev/_sources/auto_examples/linear_model/plot_nnls.rst.txt
index 61dea96535b03..f7195a82c3738 100644
--- a/dev/_sources/auto_examples/linear_model/plot_nnls.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_nnls.rst.txt
@@ -190,7 +190,7 @@ The Non-Negative Least squares inherently yield sparse results.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.068 seconds)
+ **Total running time of the script:** (0 minutes 0.080 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_nnls.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_ols_ridge.rst.txt b/dev/_sources/auto_examples/linear_model/plot_ols_ridge.rst.txt
index 110efd92589d1..f8cd94817131b 100644
--- a/dev/_sources/auto_examples/linear_model/plot_ols_ridge.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_ols_ridge.rst.txt
@@ -293,7 +293,7 @@ is noisy, or sample size is small.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.441 seconds)
+ **Total running time of the script:** (0 minutes 0.463 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_ols_ridge.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_omp.rst.txt b/dev/_sources/auto_examples/linear_model/plot_omp.rst.txt
index 18cc86cff3748..1dd781c5cd054 100644
--- a/dev/_sources/auto_examples/linear_model/plot_omp.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_omp.rst.txt
@@ -115,7 +115,7 @@ measurement encoded with a dictionary
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.210 seconds)
+ **Total running time of the script:** (0 minutes 0.230 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_omp.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.rst.txt b/dev/_sources/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.rst.txt
index 69ee0799826e1..789ef9e443299 100644
--- a/dev/_sources/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.rst.txt
@@ -1000,7 +1000,7 @@ This plot is called a Lorenz curve and can be summarized by the Gini index:
.. code-block:: none
-
+
@@ -1077,7 +1077,7 @@ Main takeaways
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 19.290 seconds)
+ **Total running time of the script:** (0 minutes 21.918 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_poisson_regression_non_normal_loss.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_polynomial_interpolation.rst.txt b/dev/_sources/auto_examples/linear_model/plot_polynomial_interpolation.rst.txt
index 2dde47b611fd2..bc641eaaad4ea 100644
--- a/dev/_sources/auto_examples/linear_model/plot_polynomial_interpolation.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_polynomial_interpolation.rst.txt
@@ -335,7 +335,7 @@ setting the knots manually.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.420 seconds)
+ **Total running time of the script:** (0 minutes 0.469 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_polynomial_interpolation.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_quantile_regression.rst.txt b/dev/_sources/auto_examples/linear_model/plot_quantile_regression.rst.txt
index 9b16641b873c6..838535c4946f6 100644
--- a/dev/_sources/auto_examples/linear_model/plot_quantile_regression.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_quantile_regression.rst.txt
@@ -491,7 +491,7 @@ We reach similar conclusions on the out-of-sample evaluation.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.837 seconds)
+ **Total running time of the script:** (0 minutes 0.938 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_quantile_regression.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_ransac.rst.txt b/dev/_sources/auto_examples/linear_model/plot_ransac.rst.txt
index 5a7daf3d104ab..36883083a9b90 100644
--- a/dev/_sources/auto_examples/linear_model/plot_ransac.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_ransac.rst.txt
@@ -126,7 +126,7 @@ and the fitted line is determined only by the identified inliers.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.092 seconds)
+ **Total running time of the script:** (0 minutes 0.116 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_ransac.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_ridge_coeffs.rst.txt b/dev/_sources/auto_examples/linear_model/plot_ridge_coeffs.rst.txt
index 595451bd8de66..5543608311d78 100644
--- a/dev/_sources/auto_examples/linear_model/plot_ridge_coeffs.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_ridge_coeffs.rst.txt
@@ -266,7 +266,7 @@ the :ref:`sphx_glr_auto_examples_linear_model_plot_huber_vs_ridge.py` example.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.648 seconds)
+ **Total running time of the script:** (0 minutes 0.735 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_ridge_coeffs.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_ridge_path.rst.txt b/dev/_sources/auto_examples/linear_model/plot_ridge_path.rst.txt
index e084d8995e6db..d31fd574b865c 100644
--- a/dev/_sources/auto_examples/linear_model/plot_ridge_path.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_ridge_path.rst.txt
@@ -132,7 +132,7 @@ Display results
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.377 seconds)
+ **Total running time of the script:** (0 minutes 0.401 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_ridge_path.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_robust_fit.rst.txt b/dev/_sources/auto_examples/linear_model/plot_robust_fit.rst.txt
index 02efa3e691f19..ebd5c3be5954b 100644
--- a/dev/_sources/auto_examples/linear_model/plot_robust_fit.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_robust_fit.rst.txt
@@ -187,7 +187,7 @@ What we can see that:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.814 seconds)
+ **Total running time of the script:** (0 minutes 1.984 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_robust_fit.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_sgd_early_stopping.rst.txt b/dev/_sources/auto_examples/linear_model/plot_sgd_early_stopping.rst.txt
index 45b7bc88c3ab7..b5049acaa8185 100644
--- a/dev/_sources/auto_examples/linear_model/plot_sgd_early_stopping.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_sgd_early_stopping.rst.txt
@@ -213,7 +213,7 @@ is held out with the validation stopping criterion.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 30.042 seconds)
+ **Total running time of the script:** (0 minutes 30.474 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_sgd_early_stopping.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_sgd_penalties.rst.txt b/dev/_sources/auto_examples/linear_model/plot_sgd_penalties.rst.txt
index cc7d059368f5a..a027321eb7086 100644
--- a/dev/_sources/auto_examples/linear_model/plot_sgd_penalties.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_sgd_penalties.rst.txt
@@ -92,7 +92,7 @@ and :class:`~sklearn.linear_model.SGDRegressor`.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.262 seconds)
+ **Total running time of the script:** (0 minutes 0.312 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_sgd_penalties.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_sgd_separating_hyperplane.rst.txt b/dev/_sources/auto_examples/linear_model/plot_sgd_separating_hyperplane.rst.txt
index 19dae448693f9..8f87d15fbb9cc 100644
--- a/dev/_sources/auto_examples/linear_model/plot_sgd_separating_hyperplane.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_sgd_separating_hyperplane.rst.txt
@@ -82,7 +82,7 @@ trained using SGD.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.070 seconds)
+ **Total running time of the script:** (0 minutes 0.075 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_sgd_separating_hyperplane.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_sgd_weighted_samples.rst.txt b/dev/_sources/auto_examples/linear_model/plot_sgd_weighted_samples.rst.txt
index f5d429789d8c6..c43279cddc213 100644
--- a/dev/_sources/auto_examples/linear_model/plot_sgd_weighted_samples.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_sgd_weighted_samples.rst.txt
@@ -98,7 +98,7 @@ is proportional to its weight.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.076 seconds)
+ **Total running time of the script:** (0 minutes 0.103 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_sgd_weighted_samples.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.rst.txt b/dev/_sources/auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.rst.txt
index c71e4c6ca7d79..3bbfb9f48eb34 100644
--- a/dev/_sources/auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.rst.txt
@@ -270,7 +270,7 @@ show that we obtain similar results on a toy dataset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.423 seconds)
+ **Total running time of the script:** (0 minutes 0.466 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_sgdocsvm_vs_ocsvm.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.rst.txt b/dev/_sources/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.rst.txt
index 27b540a612743..753b615cfabf9 100644
--- a/dev/_sources/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.rst.txt
@@ -61,7 +61,7 @@ traditional (l2-penalised) logistic regression model.
0.27054655 0.62717609 0.19522393 0.30897646 0.34586917 0.28207552
0.34125758 0.29898468 0.34279478 0.59489497 0.38353048 0.35278655
0.19829832 0.14603365]
- Run time (3 epochs) for model ovr:1.13
+ Run time (3 epochs) for model ovr:1.15
[model=Multinomial, solver=saga] Number of epochs: 1
[model=Multinomial, solver=saga] Number of epochs: 2
[model=Multinomial, solver=saga] Number of epochs: 5
@@ -71,8 +71,8 @@ traditional (l2-penalised) logistic regression model.
0.06686804 0.21443888 0.11528972 0.2075215 0.10914094 0.11144673
0.13988486 0.09684337 0.26286057 0.11682692 0.55800226 0.17370318
0.11452112 0.14603365]
- Run time (5 epochs) for model multinomial:1.01
- Example run in 5.035 s
+ Run time (5 epochs) for model multinomial:0.95
+ Example run in 5.046 s
@@ -198,7 +198,7 @@ traditional (l2-penalised) logistic regression model.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 5.118 seconds)
+ **Total running time of the script:** (0 minutes 5.114 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_sparse_logistic_regression_20newsgroups.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.rst.txt b/dev/_sources/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.rst.txt
index 633da83276265..010c165d05132 100644
--- a/dev/_sources/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.rst.txt
@@ -50,7 +50,7 @@ multi-layer perceptron model on this dataset.
Sparsity with L1 penalty: 74.57%
Test score with L1 penalty: 0.8253
- Example run in 9.795 s
+ Example run in 16.111 s
@@ -130,7 +130,7 @@ multi-layer perceptron model on this dataset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 9.863 seconds)
+ **Total running time of the script:** (0 minutes 16.183 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_sparse_logistic_regression_mnist.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_theilsen.rst.txt b/dev/_sources/auto_examples/linear_model/plot_theilsen.rst.txt
index 37a582a23a6da..e941731324ef5 100644
--- a/dev/_sources/auto_examples/linear_model/plot_theilsen.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_theilsen.rst.txt
@@ -191,7 +191,7 @@ Outliers in the X direction
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.531 seconds)
+ **Total running time of the script:** (0 minutes 0.581 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_theilsen.py:
diff --git a/dev/_sources/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.rst.txt b/dev/_sources/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.rst.txt
index f39733fe5c9f1..bb05e7221c9ed 100644
--- a/dev/_sources/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.rst.txt
+++ b/dev/_sources/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.rst.txt
@@ -1016,7 +1016,7 @@ of models, each with its own set of hyperparameters.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 7.283 seconds)
+ **Total running time of the script:** (0 minutes 10.308 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_tweedie_regression_insurance_claims.py:
diff --git a/dev/_sources/auto_examples/manifold/plot_compare_methods.rst.txt b/dev/_sources/auto_examples/manifold/plot_compare_methods.rst.txt
index 5e5c547b74e25..cf25d6208b539 100644
--- a/dev/_sources/auto_examples/manifold/plot_compare_methods.rst.txt
+++ b/dev/_sources/auto_examples/manifold/plot_compare_methods.rst.txt
@@ -386,7 +386,7 @@ different results. Read more in the :ref:`User Guide `.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 11.747 seconds)
+ **Total running time of the script:** (0 minutes 13.848 seconds)
.. _sphx_glr_download_auto_examples_manifold_plot_compare_methods.py:
diff --git a/dev/_sources/auto_examples/manifold/plot_lle_digits.rst.txt b/dev/_sources/auto_examples/manifold/plot_lle_digits.rst.txt
index e3bdd8a5cb39a..e2974ae3683ef 100644
--- a/dev/_sources/auto_examples/manifold/plot_lle_digits.rst.txt
+++ b/dev/_sources/auto_examples/manifold/plot_lle_digits.rst.txt
@@ -308,91 +308,91 @@ Finally, we can plot the resulting projection given by each method.
*
.. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_002.png
- :alt: Random projection embedding (time 0.001s)
+ :alt: Random projection embedding (time 0.002s)
:srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_002.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_003.png
- :alt: Truncated SVD embedding (time 0.003s)
+ :alt: Truncated SVD embedding (time 0.004s)
:srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_003.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_004.png
- :alt: Linear Discriminant Analysis embedding (time 0.008s)
+ :alt: Linear Discriminant Analysis embedding (time 0.010s)
:srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_004.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_005.png
- :alt: Isomap embedding (time 0.782s)
+ :alt: Isomap embedding (time 0.829s)
:srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_005.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_006.png
- :alt: Standard LLE embedding (time 0.156s)
+ :alt: Standard LLE embedding (time 0.172s)
:srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_006.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_007.png
- :alt: Modified LLE embedding (time 2.564s)
+ :alt: Modified LLE embedding (time 3.127s)
:srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_007.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_008.png
- :alt: Hessian LLE embedding (time 1.720s)
+ :alt: Hessian LLE embedding (time 2.047s)
:srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_008.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_009.png
- :alt: LTSA LLE embedding (time 2.339s)
+ :alt: LTSA LLE embedding (time 2.763s)
:srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_009.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_010.png
- :alt: MDS embedding (time 2.438s)
+ :alt: MDS embedding (time 2.776s)
:srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_010.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_011.png
- :alt: Random Trees embedding (time 0.204s)
+ :alt: Random Trees embedding (time 0.227s)
:srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_011.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_012.png
- :alt: Spectral embedding (time 0.153s)
+ :alt: Spectral embedding (time 0.163s)
:srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_012.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_013.png
- :alt: t-SNE embedding (time 2.759s)
+ :alt: t-SNE embedding (time 2.768s)
:srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_013.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_014.png
- :alt: NCA embedding (time 2.884s)
+ :alt: NCA embedding (time 2.959s)
:srcset: /auto_examples/manifold/images/sphx_glr_plot_lle_digits_014.png
:class: sphx-glr-multi-img
@@ -403,7 +403,7 @@ Finally, we can plot the resulting projection given by each method.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 20.739 seconds)
+ **Total running time of the script:** (0 minutes 23.116 seconds)
.. _sphx_glr_download_auto_examples_manifold_plot_lle_digits.py:
diff --git a/dev/_sources/auto_examples/manifold/plot_manifold_sphere.rst.txt b/dev/_sources/auto_examples/manifold/plot_manifold_sphere.rst.txt
index 9d02f8e2a5eeb..8b5d54da9b64c 100644
--- a/dev/_sources/auto_examples/manifold/plot_manifold_sphere.rst.txt
+++ b/dev/_sources/auto_examples/manifold/plot_manifold_sphere.rst.txt
@@ -47,7 +47,7 @@ that of representing a flat map of the Earth, as with
.. image-sg:: /auto_examples/manifold/images/sphx_glr_plot_manifold_sphere_001.png
- :alt: Manifold Learning with 1000 points, 10 neighbors, LLE (0.053 sec), LTSA (0.85 sec), Hessian LLE (0.65 sec), Modified LLE (1.2 sec), Isomap (0.19 sec), MDS (0.65 sec), Spectral Embedding (0.038 sec), t-SNE (3.6 sec)
+ :alt: Manifold Learning with 1000 points, 10 neighbors, LLE (0.059 sec), LTSA (0.95 sec), Hessian LLE (0.77 sec), Modified LLE (1.4 sec), Isomap (0.2 sec), MDS (0.87 sec), Spectral Embedding (0.047 sec), t-SNE (4 sec)
:srcset: /auto_examples/manifold/images/sphx_glr_plot_manifold_sphere_001.png
:class: sphx-glr-single-img
@@ -56,14 +56,14 @@ that of representing a flat map of the Earth, as with
.. code-block:: none
- standard: 0.053 sec
- ltsa: 0.85 sec
- hessian: 0.65 sec
- modified: 1.2 sec
- ISO: 0.19 sec
- MDS: 0.65 sec
- Spectral Embedding: 0.038 sec
- t-SNE: 3.6 sec
+ standard: 0.059 sec
+ ltsa: 0.95 sec
+ hessian: 0.77 sec
+ modified: 1.4 sec
+ ISO: 0.2 sec
+ MDS: 0.87 sec
+ Spectral Embedding: 0.047 sec
+ t-SNE: 4 sec
@@ -209,7 +209,7 @@ that of representing a flat map of the Earth, as with
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 7.742 seconds)
+ **Total running time of the script:** (0 minutes 8.970 seconds)
.. _sphx_glr_download_auto_examples_manifold_plot_manifold_sphere.py:
diff --git a/dev/_sources/auto_examples/manifold/plot_mds.rst.txt b/dev/_sources/auto_examples/manifold/plot_mds.rst.txt
index fefa296a9af7c..a33467f3c8957 100644
--- a/dev/_sources/auto_examples/manifold/plot_mds.rst.txt
+++ b/dev/_sources/auto_examples/manifold/plot_mds.rst.txt
@@ -242,7 +242,7 @@ Finally, we plot the original data and both MDS reconstructions.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.250 seconds)
+ **Total running time of the script:** (0 minutes 0.284 seconds)
.. _sphx_glr_download_auto_examples_manifold_plot_mds.py:
diff --git a/dev/_sources/auto_examples/manifold/plot_swissroll.rst.txt b/dev/_sources/auto_examples/manifold/plot_swissroll.rst.txt
index aabac7515b6de..309f75e1f2c80 100644
--- a/dev/_sources/auto_examples/manifold/plot_swissroll.rst.txt
+++ b/dev/_sources/auto_examples/manifold/plot_swissroll.rst.txt
@@ -241,7 +241,7 @@ on real world data.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 16.590 seconds)
+ **Total running time of the script:** (0 minutes 18.914 seconds)
.. _sphx_glr_download_auto_examples_manifold_plot_swissroll.py:
diff --git a/dev/_sources/auto_examples/manifold/plot_t_sne_perplexity.rst.txt b/dev/_sources/auto_examples/manifold/plot_t_sne_perplexity.rst.txt
index ede6e833c7c09..016b65d3c018e 100644
--- a/dev/_sources/auto_examples/manifold/plot_t_sne_perplexity.rst.txt
+++ b/dev/_sources/auto_examples/manifold/plot_t_sne_perplexity.rst.txt
@@ -55,18 +55,18 @@ those effects.
.. code-block:: none
- circles, perplexity=5 in 0.13 sec
- circles, perplexity=30 in 0.21 sec
- circles, perplexity=50 in 0.22 sec
- circles, perplexity=100 in 0.22 sec
- S-curve, perplexity=5 in 0.13 sec
- S-curve, perplexity=30 in 0.18 sec
- S-curve, perplexity=50 in 0.22 sec
- S-curve, perplexity=100 in 0.22 sec
- uniform grid, perplexity=5 in 0.16 sec
- uniform grid, perplexity=30 in 0.23 sec
- uniform grid, perplexity=50 in 0.26 sec
- uniform grid, perplexity=100 in 0.26 sec
+ circles, perplexity=5 in 0.17 sec
+ circles, perplexity=30 in 0.23 sec
+ circles, perplexity=50 in 0.29 sec
+ circles, perplexity=100 in 0.25 sec
+ S-curve, perplexity=5 in 0.14 sec
+ S-curve, perplexity=30 in 0.22 sec
+ S-curve, perplexity=50 in 0.25 sec
+ S-curve, perplexity=100 in 0.26 sec
+ uniform grid, perplexity=5 in 0.19 sec
+ uniform grid, perplexity=30 in 0.29 sec
+ uniform grid, perplexity=50 in 0.3 sec
+ uniform grid, perplexity=100 in 0.31 sec
@@ -202,7 +202,7 @@ those effects.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 2.953 seconds)
+ **Total running time of the script:** (0 minutes 3.467 seconds)
.. _sphx_glr_download_auto_examples_manifold_plot_t_sne_perplexity.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_anomaly_comparison.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_anomaly_comparison.rst.txt
index 08eb3ab12fc9b..2ff8a7fb53a1b 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_anomaly_comparison.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_anomaly_comparison.rst.txt
@@ -224,7 +224,7 @@ the problem is completely unsupervised so model selection can be a challenge.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 3.036 seconds)
+ **Total running time of the script:** (0 minutes 3.345 seconds)
.. _sphx_glr_download_auto_examples_miscellaneous_plot_anomaly_comparison.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_display_object_visualization.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_display_object_visualization.rst.txt
index 2694330c7a131..0ccb1060c7db0 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_display_object_visualization.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_display_object_visualization.rst.txt
@@ -983,7 +983,7 @@ row.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.302 seconds)
+ **Total running time of the script:** (0 minutes 0.342 seconds)
.. _sphx_glr_download_auto_examples_miscellaneous_plot_display_object_visualization.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_estimator_representation.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_estimator_representation.rst.txt
index b77d2510eae36..7db071fdc24b3 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_estimator_representation.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_estimator_representation.rst.txt
@@ -1224,7 +1224,7 @@ this feature.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.030 seconds)
+ **Total running time of the script:** (0 minutes 0.038 seconds)
.. _sphx_glr_download_auto_examples_miscellaneous_plot_estimator_representation.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_isotonic_regression.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_isotonic_regression.rst.txt
index 0ddf3b825481c..ed328f6797d3c 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_isotonic_regression.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_isotonic_regression.rst.txt
@@ -723,7 +723,7 @@ be seen on the plot of the decision function on the right-hand.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.136 seconds)
+ **Total running time of the script:** (0 minutes 0.145 seconds)
.. _sphx_glr_download_auto_examples_miscellaneous_plot_isotonic_regression.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.rst.txt
index 04002f18e7a2e..2b2b63eb4d43b 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.rst.txt
@@ -326,13 +326,13 @@ For each value of ``n_components``, we plot:
.. code-block:: none
Embedding 300 samples with dim 130107 using various random projections
- Projected 300 samples from 130107 to 300 in 0.215s
+ Projected 300 samples from 130107 to 300 in 0.212s
Random matrix with size: 1.301 MB
Mean distances rate: 1.02 (0.17)
- Projected 300 samples from 130107 to 1000 in 0.709s
+ Projected 300 samples from 130107 to 1000 in 0.704s
Random matrix with size: 4.324 MB
Mean distances rate: 1.01 (0.09)
- Projected 300 samples from 130107 to 10000 in 7.091s
+ Projected 300 samples from 130107 to 10000 in 7.220s
Random matrix with size: 43.239 MB
Mean distances rate: 1.01 (0.03)
@@ -365,7 +365,7 @@ pairwise distances.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 10.267 seconds)
+ **Total running time of the script:** (0 minutes 10.490 seconds)
.. _sphx_glr_download_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_kernel_approximation.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_kernel_approximation.rst.txt
index f87d0caee05d1..10ac53b2e2388 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_kernel_approximation.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_kernel_approximation.rst.txt
@@ -326,7 +326,7 @@ in :ref:`kernel_approximation`.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.578 seconds)
+ **Total running time of the script:** (0 minutes 1.694 seconds)
.. _sphx_glr_download_auto_examples_miscellaneous_plot_kernel_approximation.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_kernel_ridge_regression.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_kernel_ridge_regression.rst.txt
index ede7fbc8e3efc..7be4c35820a12 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_kernel_ridge_regression.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_kernel_ridge_regression.rst.txt
@@ -147,12 +147,12 @@ Compare times of SVR and Kernel Ridge Regression
.. code-block:: none
Best SVR with params: {'C': 1.0, 'gamma': np.float64(0.1)} and R2 score: 0.737
- SVR complexity and bandwidth selected and model fitted in 0.491 s
+ SVR complexity and bandwidth selected and model fitted in 0.502 s
Best KRR with params: {'alpha': 0.1, 'gamma': np.float64(0.1)} and R2 score: 0.723
- KRR complexity and bandwidth selected and model fitted in 0.184 s
+ KRR complexity and bandwidth selected and model fitted in 0.204 s
Support vector ratio: 0.340
- SVR prediction for 100000 inputs in 0.114 s
- KRR prediction for 100000 inputs in 0.089 s
+ SVR prediction for 100000 inputs in 0.120 s
+ KRR prediction for 100000 inputs in 0.086 s
@@ -343,7 +343,7 @@ Visualize the learning curves
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 8.287 seconds)
+ **Total running time of the script:** (0 minutes 13.128 seconds)
.. _sphx_glr_download_auto_examples_miscellaneous_plot_kernel_ridge_regression.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_metadata_routing.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_metadata_routing.rst.txt
index fa705f03c8d0f..0aee2bf2735cc 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_metadata_routing.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_metadata_routing.rst.txt
@@ -3601,7 +3601,7 @@ need to modify your code at all:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.044 seconds)
+ **Total running time of the script:** (0 minutes 0.045 seconds)
.. _sphx_glr_download_auto_examples_miscellaneous_plot_metadata_routing.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_multilabel.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_multilabel.rst.txt
index 8a979b8517337..ebc4c7c86e311 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_multilabel.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_multilabel.rst.txt
@@ -165,7 +165,7 @@ have a label.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.169 seconds)
+ **Total running time of the script:** (0 minutes 0.182 seconds)
.. _sphx_glr_download_auto_examples_miscellaneous_plot_multilabel.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_multioutput_face_completion.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_multioutput_face_completion.rst.txt
index 4ac886e798a3f..93b6ef9b77c69 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_multioutput_face_completion.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_multioutput_face_completion.rst.txt
@@ -133,7 +133,7 @@ regression and ridge regression complete the lower half of those faces.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.613 seconds)
+ **Total running time of the script:** (0 minutes 1.716 seconds)
.. _sphx_glr_download_auto_examples_miscellaneous_plot_multioutput_face_completion.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_outlier_detection_bench.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_outlier_detection_bench.rst.txt
index 606edb84d5a03..6ea10d2d8279f 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_outlier_detection_bench.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_outlier_detection_bench.rst.txt
@@ -237,8 +237,8 @@ The SA dataset contains 41 features out of which 3 are categorical:
.. code-block:: none
- Duration for LOF: 1.71 s
- Duration for IForest: 0.27 s
+ Duration for LOF: 1.79 s
+ Duration for IForest: 0.28 s
@@ -306,8 +306,8 @@ samples encoded with label 2 and outliers as those with label 4.
.. code-block:: none
- Duration for LOF: 1.67 s
- Duration for IForest: 0.21 s
+ Duration for LOF: 1.77 s
+ Duration for IForest: 0.22 s
@@ -413,7 +413,7 @@ a list made by hand.
.. code-block:: none
- Duration for LOF: 0.81 s
+ Duration for LOF: 0.83 s
Duration for IForest: 0.22 s
@@ -476,7 +476,7 @@ which are binary encoded and some are continuous.
.. code-block:: none
- Duration for LOF: 0.05 s
+ Duration for LOF: 0.06 s
Duration for IForest: 0.13 s
@@ -727,7 +727,7 @@ Note that the optimal preprocessing depends on the dataset, as shown below:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 43.628 seconds)
+ **Total running time of the script:** (0 minutes 57.192 seconds)
.. _sphx_glr_download_auto_examples_miscellaneous_plot_outlier_detection_bench.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.rst.txt
index 27a297fb1c12d..d0f5fad0ae29a 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.rst.txt
@@ -1099,7 +1099,7 @@ The length of the axes list must be equal to the number of plots drawn.
.. code-block:: none
-
+
@@ -1170,7 +1170,7 @@ plot function.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 2.386 seconds)
+ **Total running time of the script:** (0 minutes 2.860 seconds)
.. _sphx_glr_download_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_roc_curve_visualization_api.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_roc_curve_visualization_api.rst.txt
index 875842928f519..a07b4e683b0f5 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_roc_curve_visualization_api.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_roc_curve_visualization_api.rst.txt
@@ -825,7 +825,7 @@ curves.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.146 seconds)
+ **Total running time of the script:** (0 minutes 0.155 seconds)
.. _sphx_glr_download_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py:
diff --git a/dev/_sources/auto_examples/miscellaneous/plot_set_output.rst.txt b/dev/_sources/auto_examples/miscellaneous/plot_set_output.rst.txt
index 1535d45404890..4a1eaccadd843 100644
--- a/dev/_sources/auto_examples/miscellaneous/plot_set_output.rst.txt
+++ b/dev/_sources/auto_examples/miscellaneous/plot_set_output.rst.txt
@@ -854,7 +854,7 @@ DataFrames.
this.parentElement.nextElementSibling)"
>
score_func
- <function f_c...x7fb81b1ab640>
+ <function f_c...x7f7a8437b640>
@@ -1961,7 +1961,7 @@ outside of the context manager, the output will be a NumPy array
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.139 seconds)
+ **Total running time of the script:** (0 minutes 0.154 seconds)
.. _sphx_glr_download_auto_examples_miscellaneous_plot_set_output.py:
diff --git a/dev/_sources/auto_examples/mixture/plot_concentration_prior.rst.txt b/dev/_sources/auto_examples/mixture/plot_concentration_prior.rst.txt
index 4453dc22a382f..04fda1173c88d 100644
--- a/dev/_sources/auto_examples/mixture/plot_concentration_prior.rst.txt
+++ b/dev/_sources/auto_examples/mixture/plot_concentration_prior.rst.txt
@@ -211,7 +211,7 @@ tends to divide natural clusters into unnecessary sub-components.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 6.539 seconds)
+ **Total running time of the script:** (0 minutes 6.782 seconds)
.. _sphx_glr_download_auto_examples_mixture_plot_concentration_prior.py:
diff --git a/dev/_sources/auto_examples/mixture/plot_gmm.rst.txt b/dev/_sources/auto_examples/mixture/plot_gmm.rst.txt
index 1f6cca92a4b58..bb52ce5cc3bf9 100644
--- a/dev/_sources/auto_examples/mixture/plot_gmm.rst.txt
+++ b/dev/_sources/auto_examples/mixture/plot_gmm.rst.txt
@@ -144,7 +144,7 @@ regularization properties of the inference algorithm.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.217 seconds)
+ **Total running time of the script:** (0 minutes 0.262 seconds)
.. _sphx_glr_download_auto_examples_mixture_plot_gmm.py:
diff --git a/dev/_sources/auto_examples/mixture/plot_gmm_covariances.rst.txt b/dev/_sources/auto_examples/mixture/plot_gmm_covariances.rst.txt
index eb210f769f64d..273be69e8a769 100644
--- a/dev/_sources/auto_examples/mixture/plot_gmm_covariances.rst.txt
+++ b/dev/_sources/auto_examples/mixture/plot_gmm_covariances.rst.txt
@@ -173,7 +173,7 @@ dimensions.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.193 seconds)
+ **Total running time of the script:** (0 minutes 0.261 seconds)
.. _sphx_glr_download_auto_examples_mixture_plot_gmm_covariances.py:
diff --git a/dev/_sources/auto_examples/mixture/plot_gmm_init.rst.txt b/dev/_sources/auto_examples/mixture/plot_gmm_init.rst.txt
index 57c35f96c12b8..a26a87e872039 100644
--- a/dev/_sources/auto_examples/mixture/plot_gmm_init.rst.txt
+++ b/dev/_sources/auto_examples/mixture/plot_gmm_init.rst.txt
@@ -56,7 +56,7 @@ time to initialize and low number of GaussianMixture iterations to converge.
.. image-sg:: /auto_examples/mixture/images/sphx_glr_plot_gmm_init_001.png
- :alt: GMM iterations and relative time taken to initialize, kmeans, Iter 8 | Init Time 1.00x, random_from_data, Iter 137 | Init Time 0.54x, k-means++, Iter 11 | Init Time 0.75x, random, Iter 47 | Init Time 0.57x
+ :alt: GMM iterations and relative time taken to initialize, kmeans, Iter 8 | Init Time 1.00x, random_from_data, Iter 137 | Init Time 0.56x, k-means++, Iter 11 | Init Time 1.09x, random, Iter 47 | Init Time 0.93x
:srcset: /auto_examples/mixture/images/sphx_glr_plot_gmm_init_001.png
:class: sphx-glr-single-img
@@ -146,7 +146,7 @@ time to initialize and low number of GaussianMixture iterations to converge.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.619 seconds)
+ **Total running time of the script:** (0 minutes 0.839 seconds)
.. _sphx_glr_download_auto_examples_mixture_plot_gmm_init.py:
diff --git a/dev/_sources/auto_examples/mixture/plot_gmm_pdf.rst.txt b/dev/_sources/auto_examples/mixture/plot_gmm_pdf.rst.txt
index a03f8616dca81..248fb34a42e3a 100644
--- a/dev/_sources/auto_examples/mixture/plot_gmm_pdf.rst.txt
+++ b/dev/_sources/auto_examples/mixture/plot_gmm_pdf.rst.txt
@@ -91,7 +91,7 @@ matrices.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.118 seconds)
+ **Total running time of the script:** (0 minutes 0.143 seconds)
.. _sphx_glr_download_auto_examples_mixture_plot_gmm_pdf.py:
diff --git a/dev/_sources/auto_examples/mixture/plot_gmm_selection.rst.txt b/dev/_sources/auto_examples/mixture/plot_gmm_selection.rst.txt
index 0deec9a59d9b2..ac05db90bb69f 100644
--- a/dev/_sources/auto_examples/mixture/plot_gmm_selection.rst.txt
+++ b/dev/_sources/auto_examples/mixture/plot_gmm_selection.rst.txt
@@ -642,7 +642,7 @@ The best set of parameters and estimator are stored in `best_parameters_` and
param_grid={'covariance_type': ['spherical', 'tied', 'diag',
'full'],
'n_components': range(1, 7)},
- scoring=<function gmm_bic_score at 0x7fb7fb8b0280>)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
+ scoring=<function gmm_bic_score at 0x7f7a67b4e290>)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Parameters
@@ -675,7 +675,7 @@ The best set of parameters and estimator are stored in `best_parameters_` and
this.parentElement.nextElementSibling)"
>
scoring
- <function gmm...x7fb7fb8b0280>
+ <function gmm...x7f7a67b4e290>
@@ -1147,7 +1147,7 @@ on the `covariance_type`:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.478 seconds)
+ **Total running time of the script:** (0 minutes 1.695 seconds)
.. _sphx_glr_download_auto_examples_mixture_plot_gmm_selection.py:
diff --git a/dev/_sources/auto_examples/mixture/plot_gmm_sin.rst.txt b/dev/_sources/auto_examples/mixture/plot_gmm_sin.rst.txt
index 4fe2fb63bed3f..2af0028c59176 100644
--- a/dev/_sources/auto_examples/mixture/plot_gmm_sin.rst.txt
+++ b/dev/_sources/auto_examples/mixture/plot_gmm_sin.rst.txt
@@ -225,7 +225,7 @@ number of Gaussian components instead of a continuous noisy sine curve.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.435 seconds)
+ **Total running time of the script:** (0 minutes 0.512 seconds)
.. _sphx_glr_download_auto_examples_mixture_plot_gmm_sin.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_confusion_matrix.rst.txt b/dev/_sources/auto_examples/model_selection/plot_confusion_matrix.rst.txt
index 6194d6a551fe8..b7b7372434317 100644
--- a/dev/_sources/auto_examples/model_selection/plot_confusion_matrix.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_confusion_matrix.rst.txt
@@ -135,7 +135,7 @@ using :ref:`grid_search`.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.165 seconds)
+ **Total running time of the script:** (0 minutes 0.177 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_confusion_matrix.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_cost_sensitive_learning.rst.txt b/dev/_sources/auto_examples/model_selection/plot_cost_sensitive_learning.rst.txt
index 609554978a779..286e98c222456 100644
--- a/dev/_sources/auto_examples/model_selection/plot_cost_sensitive_learning.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_cost_sensitive_learning.rst.txt
@@ -4663,7 +4663,7 @@ beyond the scope of the scikit-learn library itself.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 27.918 seconds)
+ **Total running time of the script:** (0 minutes 36.317 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_cost_sensitive_learning.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_cv_indices.rst.txt b/dev/_sources/auto_examples/model_selection/plot_cv_indices.rst.txt
index ab0cf949bbcb1..e164e00d8c084 100644
--- a/dev/_sources/auto_examples/model_selection/plot_cv_indices.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_cv_indices.rst.txt
@@ -412,7 +412,7 @@ Note how some use the group/class information while others do not.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.298 seconds)
+ **Total running time of the script:** (0 minutes 1.297 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_cv_indices.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_cv_predict.rst.txt b/dev/_sources/auto_examples/model_selection/plot_cv_predict.rst.txt
index d37f49aa6feae..82ca6f48f3b77 100644
--- a/dev/_sources/auto_examples/model_selection/plot_cv_predict.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_cv_predict.rst.txt
@@ -159,7 +159,7 @@ It is recommended to compute per-fold performance metrics using:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.204 seconds)
+ **Total running time of the script:** (0 minutes 0.198 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_cv_predict.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_det.rst.txt b/dev/_sources/auto_examples/model_selection/plot_det.rst.txt
index ad4a5f3e559b7..322c7daa9fe07 100644
--- a/dev/_sources/auto_examples/model_selection/plot_det.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_det.rst.txt
@@ -239,7 +239,7 @@ and :math:`\text{FNR} = 1`, i.e.:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.204 seconds)
+ **Total running time of the script:** (0 minutes 0.247 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_det.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_grid_search_digits.rst.txt b/dev/_sources/auto_examples/model_selection/plot_grid_search_digits.rst.txt
index 95a223fbc9716..fe43cf04ebc5b 100644
--- a/dev/_sources/auto_examples/model_selection/plot_grid_search_digits.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_grid_search_digits.rst.txt
@@ -334,7 +334,7 @@ of the hyper-parameters and create the grid-search instance:
selected subset of best models based on precision and recall.
Its scoring time is:
- mean_score_time 0.00506
+ mean_score_time 0.005702
mean_test_recall 0.877206
std_test_recall 0.069196
mean_test_precision 1.0
@@ -829,7 +829,7 @@ of the hyper-parameters and create the grid-search instance:
param_grid=[{'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001],
'kernel': ['rbf']},
{'C': [1, 10, 100, 1000], 'kernel': ['linear']}],
- refit=<function refit_strategy at 0x7fb7fb9200d0>,
+ refit=<function refit_strategy at 0x7f7a5fe66a70>,
scoring=['precision', 'recall'])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
@@ -883,7 +883,7 @@ of the hyper-parameters and create the grid-search instance:
this.parentElement.nextElementSibling)"
>
refit
- <function ref...x7fb7fb9200d0>
+ <function ref...x7f7a5fe66a70>
@@ -1218,7 +1218,7 @@ metrics on the left-out set:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 9.438 seconds)
+ **Total running time of the script:** (0 minutes 10.712 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_grid_search_digits.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_grid_search_refit_callable.rst.txt b/dev/_sources/auto_examples/model_selection/plot_grid_search_refit_callable.rst.txt
index 1a4308534fc21..070c16b3b4d15 100644
--- a/dev/_sources/auto_examples/model_selection/plot_grid_search_refit_callable.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_grid_search_refit_callable.rst.txt
@@ -717,7 +717,7 @@ Load the digits dataset and fit the model
n_jobs=1,
param_grid={'reduce_dim__n_components': [6, 8, 10, 15, 20, 25, 35,
45, 55]},
- refit=<function best_low_complexity at 0x7fb7fb84d870>,
+ refit=<function best_low_complexity at 0x7f7a67b4c0d0>,
return_train_score=True, scoring='accuracy')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
@@ -771,7 +771,7 @@ Load the digits dataset and fit the model
this.parentElement.nextElementSibling)"
>
refit
- <function bes...x7fb7fb84d870>
+ <function bes...x7f7a67b4c0d0>
@@ -1387,15 +1387,15 @@ performance. We also show a summary table of all models using polars.
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ i32 ┆ str │
╞══════════════╪════════════╪══════════╪═════════════╪═══════════╪══════════╪══════╪══════════╡
- │ 6 ┆ 0.8631 ┆ 0.0241 ┆ 0.8697 ┆ 0.0048 ┆ 0.089 ┆ 9 ┆ │
- │ 8 ┆ 0.9037 ┆ 0.0192 ┆ 0.9146 ┆ 0.0028 ┆ 0.081 ┆ 8 ┆ │
- │ 10 ┆ 0.9341 ┆ 0.0148 ┆ 0.9493 ┆ 0.0023 ┆ 0.057 ┆ 7 ┆ │
- │ 15 ┆ 0.95 ┆ 0.0162 ┆ 0.9662 ┆ 0.0022 ┆ 0.056 ┆ 6 ┆ │
- │ 20 ┆ 0.9563 ┆ 0.0144 ┆ 0.9759 ┆ 0.0019 ┆ 0.056 ┆ 5 ┆ │
- │ 25 ┆ 0.9643 ┆ 0.0126 ┆ 0.9836 ┆ 0.0014 ┆ 0.052 ┆ 4 ┆ * │
- │ 35 ┆ 0.9685 ┆ 0.0115 ┆ 0.9903 ┆ 0.0013 ┆ 0.055 ┆ 3 ┆ │
- │ 45 ┆ 0.9711 ┆ 0.0093 ┆ 0.9926 ┆ 0.001 ┆ 0.058 ┆ 2 ┆ │
- │ 55 ┆ 0.9717 ┆ 0.0093 ┆ 0.993 ┆ 0.001 ┆ 0.064 ┆ 1 ┆ │
+ │ 6 ┆ 0.8631 ┆ 0.0241 ┆ 0.8697 ┆ 0.0048 ┆ 0.099 ┆ 9 ┆ │
+ │ 8 ┆ 0.9037 ┆ 0.0192 ┆ 0.9146 ┆ 0.0028 ┆ 0.092 ┆ 8 ┆ │
+ │ 10 ┆ 0.9341 ┆ 0.0148 ┆ 0.9493 ┆ 0.0023 ┆ 0.065 ┆ 7 ┆ │
+ │ 15 ┆ 0.95 ┆ 0.0162 ┆ 0.9662 ┆ 0.0022 ┆ 0.063 ┆ 6 ┆ │
+ │ 20 ┆ 0.9563 ┆ 0.0144 ┆ 0.9759 ┆ 0.0019 ┆ 0.063 ┆ 5 ┆ │
+ │ 25 ┆ 0.9643 ┆ 0.0126 ┆ 0.9836 ┆ 0.0014 ┆ 0.058 ┆ 4 ┆ * │
+ │ 35 ┆ 0.9685 ┆ 0.0115 ┆ 0.9903 ┆ 0.0013 ┆ 0.061 ┆ 3 ┆ │
+ │ 45 ┆ 0.9711 ┆ 0.0093 ┆ 0.9926 ┆ 0.001 ┆ 0.066 ┆ 2 ┆ │
+ │ 55 ┆ 0.9717 ┆ 0.0093 ┆ 0.993 ┆ 0.001 ┆ 0.07 ┆ 1 ┆ │
└──────────────┴────────────┴──────────┴─────────────┴───────────┴──────────┴──────┴──────────┘
@@ -1441,7 +1441,7 @@ selection of the "best" model is desired.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 18.474 seconds)
+ **Total running time of the script:** (0 minutes 20.645 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_grid_search_refit_callable.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_grid_search_stats.rst.txt b/dev/_sources/auto_examples/model_selection/plot_grid_search_stats.rst.txt
index adb865120e3e4..2bfdfc0c976c4 100644
--- a/dev/_sources/auto_examples/model_selection/plot_grid_search_stats.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_grid_search_stats.rst.txt
@@ -1950,7 +1950,7 @@ Take-home messages
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.898 seconds)
+ **Total running time of the script:** (0 minutes 1.698 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_grid_search_stats.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_grid_search_text_feature_extraction.rst.txt b/dev/_sources/auto_examples/model_selection/plot_grid_search_text_feature_extraction.rst.txt
index 5d23cb269ae1d..3b6f648f499b6 100644
--- a/dev/_sources/auto_examples/model_selection/plot_grid_search_text_feature_extraction.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_grid_search_text_feature_extraction.rst.txt
@@ -1095,7 +1095,7 @@ via the parameter `n_jobs`.
.. code-block:: none
Fitting 5 folds for each of 40 candidates, totalling 200 fits
- Done in 28.332s
+ Done in 34.306s
@@ -1234,7 +1234,7 @@ different folds of the cross-validation.
@@ -1304,7 +1304,7 @@ said axis is to be understood as :math:`10^x`.
@@ -1332,7 +1332,7 @@ and :math:`10^0`, regardless of the hyperparameter `norm`.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 30.517 seconds)
+ **Total running time of the script:** (0 minutes 36.763 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_learning_curve.rst.txt b/dev/_sources/auto_examples/model_selection/plot_learning_curve.rst.txt
index 23e8b8a9c933d..a9187b4cc7e3f 100644
--- a/dev/_sources/auto_examples/model_selection/plot_learning_curve.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_learning_curve.rst.txt
@@ -293,7 +293,7 @@ increases.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 27.833 seconds)
+ **Total running time of the script:** (0 minutes 29.093 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_learning_curve.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_likelihood_ratios.rst.txt b/dev/_sources/auto_examples/model_selection/plot_likelihood_ratios.rst.txt
index 28640fb9cc697..6335e138e22cc 100644
--- a/dev/_sources/auto_examples/model_selection/plot_likelihood_ratios.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_likelihood_ratios.rst.txt
@@ -872,7 +872,7 @@ of those computed with on balanced classes.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.962 seconds)
+ **Total running time of the script:** (0 minutes 2.002 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_likelihood_ratios.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt b/dev/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt
index 2ee82c9ebcb2d..5c8f4f62727c7 100644
--- a/dev/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt
@@ -177,7 +177,7 @@ Plotting the result
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 8.243 seconds)
+ **Total running time of the script:** (0 minutes 8.690 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_multi_metric_evaluation.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_nested_cross_validation_iris.rst.txt b/dev/_sources/auto_examples/model_selection/plot_nested_cross_validation_iris.rst.txt
index a86165771d13b..17143344f2e12 100644
--- a/dev/_sources/auto_examples/model_selection/plot_nested_cross_validation_iris.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_nested_cross_validation_iris.rst.txt
@@ -175,7 +175,7 @@ between their scores.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 6.757 seconds)
+ **Total running time of the script:** (0 minutes 6.917 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_nested_cross_validation_iris.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_permutation_tests_for_classification.rst.txt b/dev/_sources/auto_examples/model_selection/plot_permutation_tests_for_classification.rst.txt
index e833a1f22ceac..84c176bbc816e 100644
--- a/dev/_sources/auto_examples/model_selection/plot_permutation_tests_for_classification.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_permutation_tests_for_classification.rst.txt
@@ -251,7 +251,7 @@ if there is only weak structure in the data [1]_.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 11.913 seconds)
+ **Total running time of the script:** (0 minutes 12.651 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_permutation_tests_for_classification.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_precision_recall.rst.txt b/dev/_sources/auto_examples/model_selection/plot_precision_recall.rst.txt
index 6a0940a283754..e5df9725a3f37 100644
--- a/dev/_sources/auto_examples/model_selection/plot_precision_recall.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_precision_recall.rst.txt
@@ -667,7 +667,7 @@ we will first scale the data using a
}
Pipeline(steps=[('standardscaler', StandardScaler()),
('linearsvc',
- LinearSVC(random_state=RandomState(MT19937) at 0x7FB80B1CF440))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
+ LinearSVC(random_state=RandomState(MT19937) at 0x7F7A7C366C40))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Parameters
@@ -870,7 +870,7 @@ we will first scale the data using a
this.parentElement.nextElementSibling)"
>
random_state
- RandomState(M...0x7FB80B1CF440
+ RandomState(M...0x7F7A7C366C40
@@ -1194,7 +1194,7 @@ Plot Precision-Recall curve for each class and iso-f1 curves
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.427 seconds)
+ **Total running time of the script:** (0 minutes 0.546 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_precision_recall.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_randomized_search.rst.txt b/dev/_sources/auto_examples/model_selection/plot_randomized_search.rst.txt
index 6f494e71be3f6..b1b248ac9a604 100644
--- a/dev/_sources/auto_examples/model_selection/plot_randomized_search.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_randomized_search.rst.txt
@@ -46,7 +46,7 @@ simultaneously using grid search, but pick only the ones deemed most important.
.. code-block:: none
- RandomizedSearchCV took 0.60 seconds for 15 candidates parameter settings.
+ RandomizedSearchCV took 0.68 seconds for 15 candidates parameter settings.
Model with rank: 1
Mean validation score: 0.987 (std: 0.011)
Parameters: {'alpha': np.float64(0.01001911984591966), 'average': False, 'l1_ratio': np.float64(0.7665012035905148)}
@@ -59,7 +59,7 @@ simultaneously using grid search, but pick only the ones deemed most important.
Mean validation score: 0.983 (std: 0.011)
Parameters: {'alpha': np.float64(0.1352374671440465), 'average': False, 'l1_ratio': np.float64(0.6719936995475292)}
- GridSearchCV took 3.46 seconds for 60 candidate parameter settings.
+ GridSearchCV took 3.93 seconds for 60 candidate parameter settings.
Model with rank: 1
Mean validation score: 0.994 (std: 0.005)
Parameters: {'alpha': np.float64(0.1), 'average': False, 'l1_ratio': np.float64(0.1111111111111111)}
@@ -160,7 +160,7 @@ simultaneously using grid search, but pick only the ones deemed most important.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 4.074 seconds)
+ **Total running time of the script:** (0 minutes 4.624 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_randomized_search.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_roc.rst.txt b/dev/_sources/auto_examples/model_selection/plot_roc.rst.txt
index b5e1c3b0865c0..a68d272bcf820 100644
--- a/dev/_sources/auto_examples/model_selection/plot_roc.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_roc.rst.txt
@@ -832,7 +832,7 @@ depending on the desired outcome:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.651 seconds)
+ **Total running time of the script:** (0 minutes 0.723 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_roc.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_roc_crossval.rst.txt b/dev/_sources/auto_examples/model_selection/plot_roc_crossval.rst.txt
index d645b5051056a..bfa52f8a21277 100644
--- a/dev/_sources/auto_examples/model_selection/plot_roc_crossval.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_roc_crossval.rst.txt
@@ -217,7 +217,7 @@ frequent class.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.204 seconds)
+ **Total running time of the script:** (0 minutes 0.213 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_roc_crossval.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_successive_halving_heatmap.rst.txt b/dev/_sources/auto_examples/model_selection/plot_successive_halving_heatmap.rst.txt
index 1d9b37202b854..1b9af33f505c1 100644
--- a/dev/_sources/auto_examples/model_selection/plot_successive_halving_heatmap.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_successive_halving_heatmap.rst.txt
@@ -171,7 +171,7 @@ We now plot heatmaps for both search estimators.
.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_successive_halving_heatmap_001.png
- :alt: Successive Halving time = 1.442s, GridSearch time = 5.505s
+ :alt: Successive Halving time = 1.649s, GridSearch time = 5.974s
:srcset: /auto_examples/model_selection/images/sphx_glr_plot_successive_halving_heatmap_001.png
:class: sphx-glr-single-img
@@ -195,7 +195,7 @@ class is able to find parameter combinations that are just as accurate as
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 7.105 seconds)
+ **Total running time of the script:** (0 minutes 7.792 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_successive_halving_heatmap.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_successive_halving_iterations.rst.txt b/dev/_sources/auto_examples/model_selection/plot_successive_halving_iterations.rst.txt
index 7ec1feb9ba445..6bf173d393251 100644
--- a/dev/_sources/auto_examples/model_selection/plot_successive_halving_iterations.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_successive_halving_iterations.rst.txt
@@ -567,14 +567,14 @@ We first define the parameter space and train a
cursor: pointer;
}
HalvingRandomSearchCV(estimator=RandomForestClassifier(n_estimators=20,
- random_state=RandomState(MT19937) at 0x7FB80B1CF440),
+ random_state=RandomState(MT19937) at 0x7F7A7C366C40),
factor=2,
param_distributions={'bootstrap': [True, False],
'criterion': ['gini', 'entropy'],
'max_depth': [3, None],
- 'max_features': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fb818befa00>,
- 'min_samples_split': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fb818bedba0>},
- random_state=RandomState(MT19937) at 0x7FB80B1CF440)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
+ 'max_features': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7f7a5fd819c0>,
+ 'min_samples_split': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7f7a5fd83190>},
+ random_state=RandomState(MT19937) at 0x7F7A7C366C40)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Parameters
@@ -587,7 +587,7 @@ We first define the parameter space and train a
this.parentElement.nextElementSibling)"
>
estimator
- RandomForestC...x7FB80B1CF440)
+ RandomForestC...x7F7A7C366C40)
@@ -597,7 +597,7 @@ We first define the parameter space and train a
this.parentElement.nextElementSibling)"
>
param_distributions
- {'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'max_depth': [3, None], 'max_features': <scipy.stats....x7fb818befa00>, ...}
+ {'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'max_depth': [3, None], 'max_features': <scipy.stats....x7f7a5fd819c0>, ...}
@@ -717,7 +717,7 @@ We first define the parameter space and train a
this.parentElement.nextElementSibling)"
>
random_state
- RandomState(M...0x7FB80B1CF440
+ RandomState(M...0x7F7A7C366C40
@@ -746,7 +746,7 @@ We first define the parameter space and train a
RandomForestClassifier(bootstrap=False, criterion='entropy', max_features=4,
min_samples_split=5, n_estimators=20,
- random_state=RandomState(MT19937) at 0x7FB7FAFBEB40)
+ random_state=RandomState(MT19937) at 0x7F7A64BC6B40)
Parameters
@@ -879,7 +879,7 @@ We first define the parameter space and train a
this.parentElement.nextElementSibling)"
>
random_state
- RandomState(M...0x7FB7FAFBEB40
+ RandomState(M...0x7F7A64BC6B40
@@ -1056,7 +1056,7 @@ last iteration.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 5.323 seconds)
+ **Total running time of the script:** (0 minutes 6.126 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_successive_halving_iterations.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_train_error_vs_test_error.rst.txt b/dev/_sources/auto_examples/model_selection/plot_train_error_vs_test_error.rst.txt
index 54f5184d8e7aa..dc4409eeabf53 100644
--- a/dev/_sources/auto_examples/model_selection/plot_train_error_vs_test_error.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_train_error_vs_test_error.rst.txt
@@ -280,7 +280,7 @@ obtained on the test set.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 4.751 seconds)
+ **Total running time of the script:** (0 minutes 5.705 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_train_error_vs_test_error.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_tuned_decision_threshold.rst.txt b/dev/_sources/auto_examples/model_selection/plot_tuned_decision_threshold.rst.txt
index 7f0ec470f2c16..68fd8039038e3 100644
--- a/dev/_sources/auto_examples/model_selection/plot_tuned_decision_threshold.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_tuned_decision_threshold.rst.txt
@@ -1221,7 +1221,7 @@ for more details.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 35.871 seconds)
+ **Total running time of the script:** (0 minutes 36.388 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_tuned_decision_threshold.py:
diff --git a/dev/_sources/auto_examples/model_selection/plot_underfitting_overfitting.rst.txt b/dev/_sources/auto_examples/model_selection/plot_underfitting_overfitting.rst.txt
index d1e4e9117c491..dda2ccb00b719 100644
--- a/dev/_sources/auto_examples/model_selection/plot_underfitting_overfitting.rst.txt
+++ b/dev/_sources/auto_examples/model_selection/plot_underfitting_overfitting.rst.txt
@@ -117,7 +117,7 @@ training data.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.214 seconds)
+ **Total running time of the script:** (0 minutes 0.211 seconds)
.. _sphx_glr_download_auto_examples_model_selection_plot_underfitting_overfitting.py:
diff --git a/dev/_sources/auto_examples/multiclass/plot_multiclass_overview.rst.txt b/dev/_sources/auto_examples/multiclass/plot_multiclass_overview.rst.txt
index 1e202b4223453..030409c60f81d 100644
--- a/dev/_sources/auto_examples/multiclass/plot_multiclass_overview.rst.txt
+++ b/dev/_sources/auto_examples/multiclass/plot_multiclass_overview.rst.txt
@@ -327,7 +327,7 @@ References
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 22.513 seconds)
+ **Total running time of the script:** (0 minutes 20.184 seconds)
.. _sphx_glr_download_auto_examples_multiclass_plot_multiclass_overview.py:
diff --git a/dev/_sources/auto_examples/multioutput/plot_classifier_chain_yeast.rst.txt b/dev/_sources/auto_examples/multioutput/plot_classifier_chain_yeast.rst.txt
index 456f385768d75..eb6814aa42d3c 100644
--- a/dev/_sources/auto_examples/multioutput/plot_classifier_chain_yeast.rst.txt
+++ b/dev/_sources/auto_examples/multioutput/plot_classifier_chain_yeast.rst.txt
@@ -243,7 +243,7 @@ There are three main takeaways from this plot:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 2.481 seconds)
+ **Total running time of the script:** (0 minutes 2.049 seconds)
.. _sphx_glr_download_auto_examples_multioutput_plot_classifier_chain_yeast.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_caching_nearest_neighbors.rst.txt b/dev/_sources/auto_examples/neighbors/plot_caching_nearest_neighbors.rst.txt
index ac0bf9a59ff9b..93efd5b2dea67 100644
--- a/dev/_sources/auto_examples/neighbors/plot_caching_nearest_neighbors.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_caching_nearest_neighbors.rst.txt
@@ -105,7 +105,7 @@ larger, or when the grid of parameter to search is large.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.620 seconds)
+ **Total running time of the script:** (0 minutes 1.364 seconds)
.. _sphx_glr_download_auto_examples_neighbors_plot_caching_nearest_neighbors.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_classification.rst.txt b/dev/_sources/auto_examples/neighbors/plot_classification.rst.txt
index 69cade9891410..340d68a4e4a90 100644
--- a/dev/_sources/auto_examples/neighbors/plot_classification.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_classification.rst.txt
@@ -174,7 +174,7 @@ In some cases, taking the distance into account might improve the model.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.573 seconds)
+ **Total running time of the script:** (0 minutes 0.530 seconds)
.. _sphx_glr_download_auto_examples_neighbors_plot_classification.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_digits_kde_sampling.rst.txt b/dev/_sources/auto_examples/neighbors/plot_digits_kde_sampling.rst.txt
index e0c7c153f9879..6cddd4831bd76 100644
--- a/dev/_sources/auto_examples/neighbors/plot_digits_kde_sampling.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_digits_kde_sampling.rst.txt
@@ -112,7 +112,7 @@ of the data.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 4.695 seconds)
+ **Total running time of the script:** (0 minutes 4.414 seconds)
.. _sphx_glr_download_auto_examples_neighbors_plot_digits_kde_sampling.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_kde_1d.rst.txt b/dev/_sources/auto_examples/neighbors/plot_kde_1d.rst.txt
index a3e883fc9cd02..28c282878ce39 100644
--- a/dev/_sources/auto_examples/neighbors/plot_kde_1d.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_kde_1d.rst.txt
@@ -212,7 +212,7 @@ as well.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.608 seconds)
+ **Total running time of the script:** (0 minutes 0.577 seconds)
.. _sphx_glr_download_auto_examples_neighbors_plot_kde_1d.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_lof_novelty_detection.rst.txt b/dev/_sources/auto_examples/neighbors/plot_lof_novelty_detection.rst.txt
index 70f6cefe834c8..3cbba0f7c0e6c 100644
--- a/dev/_sources/auto_examples/neighbors/plot_lof_novelty_detection.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_lof_novelty_detection.rst.txt
@@ -128,7 +128,7 @@ n_neighbors=20 appears to work well in general.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.743 seconds)
+ **Total running time of the script:** (0 minutes 0.711 seconds)
.. _sphx_glr_download_auto_examples_neighbors_plot_lof_novelty_detection.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_lof_outlier_detection.rst.txt b/dev/_sources/auto_examples/neighbors/plot_lof_outlier_detection.rst.txt
index f449f10662d61..067f9f27d36e5 100644
--- a/dev/_sources/auto_examples/neighbors/plot_lof_outlier_detection.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_lof_outlier_detection.rst.txt
@@ -165,7 +165,7 @@ Plot results
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.092 seconds)
+ **Total running time of the script:** (0 minutes 0.101 seconds)
.. _sphx_glr_download_auto_examples_neighbors_plot_lof_outlier_detection.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_nca_classification.rst.txt b/dev/_sources/auto_examples/neighbors/plot_nca_classification.rst.txt
index cca1f9b8250d5..55ebbadef57b8 100644
--- a/dev/_sources/auto_examples/neighbors/plot_nca_classification.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_nca_classification.rst.txt
@@ -144,7 +144,7 @@ training set.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.801 seconds)
+ **Total running time of the script:** (0 minutes 0.775 seconds)
.. _sphx_glr_download_auto_examples_neighbors_plot_nca_classification.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_nca_dim_reduction.rst.txt b/dev/_sources/auto_examples/neighbors/plot_nca_dim_reduction.rst.txt
index eaffa5c1bee87..af08933e74fa8 100644
--- a/dev/_sources/auto_examples/neighbors/plot_nca_dim_reduction.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_nca_dim_reduction.rst.txt
@@ -153,7 +153,7 @@ meaningful despite the large reduction in dimension.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 2.266 seconds)
+ **Total running time of the script:** (0 minutes 2.012 seconds)
.. _sphx_glr_download_auto_examples_neighbors_plot_nca_dim_reduction.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_nca_illustration.rst.txt b/dev/_sources/auto_examples/neighbors/plot_nca_illustration.rst.txt
index 63b6bd98a0bb6..9ff6180fc5fbd 100644
--- a/dev/_sources/auto_examples/neighbors/plot_nca_illustration.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_nca_illustration.rst.txt
@@ -168,7 +168,7 @@ embedding and find the nearest neighbors.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.184 seconds)
+ **Total running time of the script:** (0 minutes 0.159 seconds)
.. _sphx_glr_download_auto_examples_neighbors_plot_nca_illustration.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_nearest_centroid.rst.txt b/dev/_sources/auto_examples/neighbors/plot_nearest_centroid.rst.txt
index c180452d800c6..fa9df67c1893d 100644
--- a/dev/_sources/auto_examples/neighbors/plot_nearest_centroid.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_nearest_centroid.rst.txt
@@ -108,7 +108,7 @@ It will plot the decision boundaries for each class.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.184 seconds)
+ **Total running time of the script:** (0 minutes 0.169 seconds)
.. _sphx_glr_download_auto_examples_neighbors_plot_nearest_centroid.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_regression.rst.txt b/dev/_sources/auto_examples/neighbors/plot_regression.rst.txt
index 7c1e4f22db582..9fd6eff448656 100644
--- a/dev/_sources/auto_examples/neighbors/plot_regression.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_regression.rst.txt
@@ -114,7 +114,7 @@ weights in prediction effect predicted values.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.255 seconds)
+ **Total running time of the script:** (0 minutes 0.228 seconds)
.. _sphx_glr_download_auto_examples_neighbors_plot_regression.py:
diff --git a/dev/_sources/auto_examples/neighbors/plot_species_kde.rst.txt b/dev/_sources/auto_examples/neighbors/plot_species_kde.rst.txt
index fe9b37730b13d..4a08fa4820a13 100644
--- a/dev/_sources/auto_examples/neighbors/plot_species_kde.rst.txt
+++ b/dev/_sources/auto_examples/neighbors/plot_species_kde.rst.txt
@@ -199,7 +199,7 @@ References
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 3.943 seconds)
+ **Total running time of the script:** (0 minutes 3.468 seconds)
.. _sphx_glr_download_auto_examples_neighbors_plot_species_kde.py:
diff --git a/dev/_sources/auto_examples/neural_networks/plot_mlp_alpha.rst.txt b/dev/_sources/auto_examples/neural_networks/plot_mlp_alpha.rst.txt
index eb3f772e31d04..baadbe1969c8a 100644
--- a/dev/_sources/auto_examples/neural_networks/plot_mlp_alpha.rst.txt
+++ b/dev/_sources/auto_examples/neural_networks/plot_mlp_alpha.rst.txt
@@ -182,7 +182,7 @@ decision boundary.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 2.278 seconds)
+ **Total running time of the script:** (0 minutes 2.037 seconds)
.. _sphx_glr_download_auto_examples_neural_networks_plot_mlp_alpha.py:
diff --git a/dev/_sources/auto_examples/neural_networks/plot_mlp_training_curves.rst.txt b/dev/_sources/auto_examples/neural_networks/plot_mlp_training_curves.rst.txt
index 101aee1cb54ad..01060125913ab 100644
--- a/dev/_sources/auto_examples/neural_networks/plot_mlp_training_curves.rst.txt
+++ b/dev/_sources/auto_examples/neural_networks/plot_mlp_training_curves.rst.txt
@@ -280,7 +280,7 @@ Note that those results can be highly dependent on the value of
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 3.122 seconds)
+ **Total running time of the script:** (0 minutes 2.757 seconds)
.. _sphx_glr_download_auto_examples_neural_networks_plot_mlp_training_curves.py:
diff --git a/dev/_sources/auto_examples/neural_networks/plot_mnist_filters.rst.txt b/dev/_sources/auto_examples/neural_networks/plot_mnist_filters.rst.txt
index 95b108c487155..db6b7e5325577 100644
--- a/dev/_sources/auto_examples/neural_networks/plot_mnist_filters.rst.txt
+++ b/dev/_sources/auto_examples/neural_networks/plot_mnist_filters.rst.txt
@@ -129,7 +129,7 @@ to build this documentation on a regular basis.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 17.934 seconds)
+ **Total running time of the script:** (0 minutes 11.166 seconds)
.. _sphx_glr_download_auto_examples_neural_networks_plot_mnist_filters.py:
diff --git a/dev/_sources/auto_examples/neural_networks/plot_rbm_logistic_classification.rst.txt b/dev/_sources/auto_examples/neural_networks/plot_rbm_logistic_classification.rst.txt
index bb7178b477858..2efd3f44e7c0d 100644
--- a/dev/_sources/auto_examples/neural_networks/plot_rbm_logistic_classification.rst.txt
+++ b/dev/_sources/auto_examples/neural_networks/plot_rbm_logistic_classification.rst.txt
@@ -174,16 +174,16 @@ reproduced here because of runtime constraints.
.. code-block:: none
- [BernoulliRBM] Iteration 1, pseudo-likelihood = -25.57, time = 0.11s
- [BernoulliRBM] Iteration 2, pseudo-likelihood = -23.68, time = 0.16s
- [BernoulliRBM] Iteration 3, pseudo-likelihood = -22.88, time = 0.16s
- [BernoulliRBM] Iteration 4, pseudo-likelihood = -21.91, time = 0.15s
- [BernoulliRBM] Iteration 5, pseudo-likelihood = -21.79, time = 0.15s
- [BernoulliRBM] Iteration 6, pseudo-likelihood = -20.96, time = 0.14s
- [BernoulliRBM] Iteration 7, pseudo-likelihood = -20.88, time = 0.15s
- [BernoulliRBM] Iteration 8, pseudo-likelihood = -20.50, time = 0.14s
- [BernoulliRBM] Iteration 9, pseudo-likelihood = -20.34, time = 0.15s
- [BernoulliRBM] Iteration 10, pseudo-likelihood = -20.21, time = 0.14s
+ [BernoulliRBM] Iteration 1, pseudo-likelihood = -25.57, time = 0.09s
+ [BernoulliRBM] Iteration 2, pseudo-likelihood = -23.68, time = 0.13s
+ [BernoulliRBM] Iteration 3, pseudo-likelihood = -22.88, time = 0.13s
+ [BernoulliRBM] Iteration 4, pseudo-likelihood = -21.91, time = 0.13s
+ [BernoulliRBM] Iteration 5, pseudo-likelihood = -21.79, time = 0.12s
+ [BernoulliRBM] Iteration 6, pseudo-likelihood = -20.96, time = 0.12s
+ [BernoulliRBM] Iteration 7, pseudo-likelihood = -20.88, time = 0.12s
+ [BernoulliRBM] Iteration 8, pseudo-likelihood = -20.50, time = 0.12s
+ [BernoulliRBM] Iteration 9, pseudo-likelihood = -20.34, time = 0.12s
+ [BernoulliRBM] Iteration 10, pseudo-likelihood = -20.21, time = 0.12s
.. raw:: html
@@ -1029,7 +1029,7 @@ Plotting
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 3.177 seconds)
+ **Total running time of the script:** (0 minutes 2.764 seconds)
.. _sphx_glr_download_auto_examples_neural_networks_plot_rbm_logistic_classification.py:
diff --git a/dev/_sources/auto_examples/preprocessing/plot_all_scaling.rst.txt b/dev/_sources/auto_examples/preprocessing/plot_all_scaling.rst.txt
index d83195868b258..74559796a45f6 100644
--- a/dev/_sources/auto_examples/preprocessing/plot_all_scaling.rst.txt
+++ b/dev/_sources/auto_examples/preprocessing/plot_all_scaling.rst.txt
@@ -633,7 +633,7 @@ case if some original features had a mix of positive and negative values.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 10.968 seconds)
+ **Total running time of the script:** (0 minutes 9.276 seconds)
.. _sphx_glr_download_auto_examples_preprocessing_plot_all_scaling.py:
diff --git a/dev/_sources/auto_examples/preprocessing/plot_discretization.rst.txt b/dev/_sources/auto_examples/preprocessing/plot_discretization.rst.txt
index 0cdcb730ece5f..7338663c04931 100644
--- a/dev/_sources/auto_examples/preprocessing/plot_discretization.rst.txt
+++ b/dev/_sources/auto_examples/preprocessing/plot_discretization.rst.txt
@@ -127,7 +127,7 @@ up the data anywhere.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.230 seconds)
+ **Total running time of the script:** (0 minutes 0.227 seconds)
.. _sphx_glr_download_auto_examples_preprocessing_plot_discretization.py:
diff --git a/dev/_sources/auto_examples/preprocessing/plot_discretization_classification.rst.txt b/dev/_sources/auto_examples/preprocessing/plot_discretization_classification.rst.txt
index b9479abcfdf6a..70ebfa8797dbd 100644
--- a/dev/_sources/auto_examples/preprocessing/plot_discretization_classification.rst.txt
+++ b/dev/_sources/auto_examples/preprocessing/plot_discretization_classification.rst.txt
@@ -304,7 +304,7 @@ set.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 3.793 seconds)
+ **Total running time of the script:** (0 minutes 3.876 seconds)
.. _sphx_glr_download_auto_examples_preprocessing_plot_discretization_classification.py:
diff --git a/dev/_sources/auto_examples/preprocessing/plot_discretization_strategies.rst.txt b/dev/_sources/auto_examples/preprocessing/plot_discretization_strategies.rst.txt
index 48fed89c68f9e..146ac1edb285a 100644
--- a/dev/_sources/auto_examples/preprocessing/plot_discretization_strategies.rst.txt
+++ b/dev/_sources/auto_examples/preprocessing/plot_discretization_strategies.rst.txt
@@ -143,7 +143,7 @@ The plot shows the regions where the discretized encoding is constant.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.667 seconds)
+ **Total running time of the script:** (0 minutes 0.677 seconds)
.. _sphx_glr_download_auto_examples_preprocessing_plot_discretization_strategies.py:
diff --git a/dev/_sources/auto_examples/preprocessing/plot_map_data_to_normal.rst.txt b/dev/_sources/auto_examples/preprocessing/plot_map_data_to_normal.rst.txt
index 336f31dff4969..feb95aba2723e 100644
--- a/dev/_sources/auto_examples/preprocessing/plot_map_data_to_normal.rst.txt
+++ b/dev/_sources/auto_examples/preprocessing/plot_map_data_to_normal.rst.txt
@@ -179,7 +179,7 @@ is prone to overfitting. The use of the power transform is then recommended.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 2.026 seconds)
+ **Total running time of the script:** (0 minutes 2.135 seconds)
.. _sphx_glr_download_auto_examples_preprocessing_plot_map_data_to_normal.py:
diff --git a/dev/_sources/auto_examples/preprocessing/plot_scaling_importance.rst.txt b/dev/_sources/auto_examples/preprocessing/plot_scaling_importance.rst.txt
index 0c1cc0534335a..31a401ceafeba 100644
--- a/dev/_sources/auto_examples/preprocessing/plot_scaling_importance.rst.txt
+++ b/dev/_sources/auto_examples/preprocessing/plot_scaling_importance.rst.txt
@@ -405,7 +405,7 @@ the scaling step.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.767 seconds)
+ **Total running time of the script:** (0 minutes 1.794 seconds)
.. _sphx_glr_download_auto_examples_preprocessing_plot_scaling_importance.py:
diff --git a/dev/_sources/auto_examples/preprocessing/plot_target_encoder.rst.txt b/dev/_sources/auto_examples/preprocessing/plot_target_encoder.rst.txt
index 43c47d1059a3a..4df75b1e97d93 100644
--- a/dev/_sources/auto_examples/preprocessing/plot_target_encoder.rst.txt
+++ b/dev/_sources/auto_examples/preprocessing/plot_target_encoder.rst.txt
@@ -1539,7 +1539,7 @@ can be explained as follows:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 30.518 seconds)
+ **Total running time of the script:** (0 minutes 28.279 seconds)
.. _sphx_glr_download_auto_examples_preprocessing_plot_target_encoder.py:
diff --git a/dev/_sources/auto_examples/preprocessing/plot_target_encoder_cross_val.rst.txt b/dev/_sources/auto_examples/preprocessing/plot_target_encoder_cross_val.rst.txt
index 6fd4aacb51528..65d11a612b8be 100644
--- a/dev/_sources/auto_examples/preprocessing/plot_target_encoder_cross_val.rst.txt
+++ b/dev/_sources/auto_examples/preprocessing/plot_target_encoder_cross_val.rst.txt
@@ -388,7 +388,7 @@ will correctly call :meth:`TargetEncoder.fit_transform` and use
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.328 seconds)
+ **Total running time of the script:** (0 minutes 0.329 seconds)
.. _sphx_glr_download_auto_examples_preprocessing_plot_target_encoder_cross_val.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_22_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_22_0.rst.txt
index 9fe3557fca669..53d396b1a4db0 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_22_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_22_0.rst.txt
@@ -503,7 +503,7 @@ Read more in the :ref:`User Guide `.
.. code-block:: none
- 0.9946720085470085
+ 0.9961333333333333
@@ -511,7 +511,7 @@ Read more in the :ref:`User Guide `.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.340 seconds)
+ **Total running time of the script:** (0 minutes 1.682 seconds)
.. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_0_22_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_23_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_23_0.rst.txt
index 222c4c0a8d24c..5504da689deb5 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_23_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_23_0.rst.txt
@@ -1282,7 +1282,7 @@ please refer to our :ref:`parallelism` notes.
.. code-block:: none
- 0.6765813507338866
+ 0.7885988040212891
@@ -1409,7 +1409,7 @@ The two linear regressors :class:`~sklearn.linear_model.Lasso` and
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.606 seconds)
+ **Total running time of the script:** (0 minutes 0.758 seconds)
.. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_0_23_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_24_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_24_0.rst.txt
index 6077e7eca9e44..31089f8366323 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_24_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_0_24_0.rst.txt
@@ -1031,7 +1031,7 @@ models, but uses much less memory than
.. code-block:: none
- 0.7332
+ 0.7297
@@ -1828,7 +1828,7 @@ to improve the understanding of machine learning practices:
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 14.327 seconds)
+ **Total running time of the script:** (0 minutes 17.681 seconds)
.. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_0_24_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_0_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_0_0.rst.txt
index dd23e296a9b34..b528c9d77588a 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_0_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_0_0.rst.txt
@@ -350,7 +350,7 @@ documentation.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.015 seconds)
+ **Total running time of the script:** (0 minutes 0.018 seconds)
.. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_0_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_1_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_1_0.rst.txt
index cdace5bea37de..68ee79e988f87 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_1_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_1_0.rst.txt
@@ -988,7 +988,7 @@ more complex pipelines:
this.parentElement.nextElementSibling)"
>
score_func
- <function f_c...x7fb81b1ab640>
+ <function f_c...x7f7a8437b640>
@@ -1501,7 +1501,7 @@ structure to the clustering.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.905 seconds)
+ **Total running time of the script:** (0 minutes 1.038 seconds)
.. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_1_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_2_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_2_0.rst.txt
index 54124fdd8c62a..4d6eb51c95c11 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_2_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_2_0.rst.txt
@@ -1242,7 +1242,7 @@ A detailed list of the impacted estimators can be found in the
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 5.867 seconds)
+ **Total running time of the script:** (0 minutes 7.081 seconds)
.. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_2_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_3_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_3_0.rst.txt
index 68ade3e1e2f12..ffaed3220dc26 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_3_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_3_0.rst.txt
@@ -296,7 +296,7 @@ See the :ref:`User Guide ` for more details.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.406 seconds)
+ **Total running time of the script:** (0 minutes 1.469 seconds)
.. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_3_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_4_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_4_0.rst.txt
index 05866ab83c8fa..1099687de7f1a 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_4_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_4_0.rst.txt
@@ -112,7 +112,7 @@ algorithm:
.. code-block:: none
- ROC AUC score is 0.928124185832119
+ ROC AUC score is 0.9280424983429423
@@ -1845,7 +1845,7 @@ cross-validation with sample weights and :class:`~model_selection.GroupKFold`:
.. code-block:: none
- cv error on test sets: [-0.21116819 -0.29376831 -0.68230524 -0.16457611 -0.29025105]
+ cv error on test sets: [-0.49476638 -0.2710103 -0.28060396 -0.55197451 -0.35894459]
@@ -1890,7 +1890,7 @@ eigenvalue decomposition of the data set covariance matrix.
.. code-block:: none
- Speedup: 3.5x
+ Speedup: 2.9x
@@ -1898,7 +1898,7 @@ eigenvalue decomposition of the data set covariance matrix.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.953 seconds)
+ **Total running time of the script:** (0 minutes 2.173 seconds)
.. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_4_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_5_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_5_0.rst.txt
index c0c8e20e027ba..2694d879869be 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_5_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_5_0.rst.txt
@@ -427,7 +427,7 @@ non-numeric arrays using a callable metric.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.862 seconds)
+ **Total running time of the script:** (0 minutes 1.605 seconds)
.. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_5_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_6_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_6_0.rst.txt
index 9be76fa522bb1..b78043f6ea01c 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_6_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_6_0.rst.txt
@@ -84,8 +84,8 @@ or to pass a pre-fitted model to some of the meta-estimators. Here's a short exa
.. code-block:: none
- Fitting the classifier took 3.01 milliseconds
- Fitting the threshold classifier took 0.50 milliseconds
+ Fitting the classifier took 4.43 milliseconds
+ Fitting the threshold classifier took 0.75 milliseconds
@@ -279,7 +279,7 @@ check out.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.088 seconds)
+ **Total running time of the script:** (0 minutes 0.116 seconds)
.. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_6_0.py:
diff --git a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_7_0.rst.txt b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_7_0.rst.txt
index ab877c495d819..7ec83e14c5063 100644
--- a/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_7_0.rst.txt
+++ b/dev/_sources/auto_examples/release_highlights/plot_release_highlights_1_7_0.rst.txt
@@ -1793,7 +1793,7 @@ sparse arrays.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.157 seconds)
+ **Total running time of the script:** (0 minutes 0.194 seconds)
.. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_7_0.py:
diff --git a/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_digits.rst.txt b/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_digits.rst.txt
index 387f6f83299f9..78fceaa95107a 100644
--- a/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_digits.rst.txt
+++ b/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_digits.rst.txt
@@ -226,7 +226,7 @@ Confusion matrix
.. code-block:: none
-
+
@@ -308,7 +308,7 @@ Plot
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.325 seconds)
+ **Total running time of the script:** (0 minutes 0.369 seconds)
.. _sphx_glr_download_auto_examples_semi_supervised_plot_label_propagation_digits.py:
diff --git a/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.rst.txt b/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.rst.txt
index b975539a59ee6..0f8e2c3ef1f7f 100644
--- a/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.rst.txt
+++ b/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.rst.txt
@@ -319,7 +319,7 @@ model with their true labels.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.544 seconds)
+ **Total running time of the script:** (0 minutes 0.514 seconds)
.. _sphx_glr_download_auto_examples_semi_supervised_plot_label_propagation_digits_active_learning.py:
diff --git a/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_structure.rst.txt b/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_structure.rst.txt
index fed82c9de9878..9086f7559c6bd 100644
--- a/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_structure.rst.txt
+++ b/dev/_sources/auto_examples/semi_supervised/plot_label_propagation_structure.rst.txt
@@ -801,7 +801,7 @@ when the label was unknown.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.176 seconds)
+ **Total running time of the script:** (0 minutes 0.163 seconds)
.. _sphx_glr_download_auto_examples_semi_supervised_plot_label_propagation_structure.py:
diff --git a/dev/_sources/auto_examples/semi_supervised/plot_self_training_varying_threshold.rst.txt b/dev/_sources/auto_examples/semi_supervised/plot_self_training_varying_threshold.rst.txt
index 7012cf63aa0c6..5c39ab894344d 100644
--- a/dev/_sources/auto_examples/semi_supervised/plot_self_training_varying_threshold.rst.txt
+++ b/dev/_sources/auto_examples/semi_supervised/plot_self_training_varying_threshold.rst.txt
@@ -154,7 +154,7 @@ around 0.7.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 6.105 seconds)
+ **Total running time of the script:** (0 minutes 5.633 seconds)
.. _sphx_glr_download_auto_examples_semi_supervised_plot_self_training_varying_threshold.py:
diff --git a/dev/_sources/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.rst.txt b/dev/_sources/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.rst.txt
index e8a81e9cd5066..b796a28de2d00 100644
--- a/dev/_sources/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.rst.txt
+++ b/dev/_sources/auto_examples/semi_supervised/plot_semi_supervised_newsgroups.rst.txt
@@ -187,7 +187,7 @@ loader or setting them to `None` to get all 20 of them.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 7.148 seconds)
+ **Total running time of the script:** (0 minutes 7.517 seconds)
.. _sphx_glr_download_auto_examples_semi_supervised_plot_semi_supervised_newsgroups.py:
diff --git a/dev/_sources/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.rst.txt b/dev/_sources/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.rst.txt
index 55dee7261b6c9..119b413b40fe8 100644
--- a/dev/_sources/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.rst.txt
+++ b/dev/_sources/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.rst.txt
@@ -123,7 +123,7 @@ identical to training the SVC on 100% of the data.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.933 seconds)
+ **Total running time of the script:** (0 minutes 0.918 seconds)
.. _sphx_glr_download_auto_examples_semi_supervised_plot_semi_supervised_versus_svm_iris.py:
diff --git a/dev/_sources/auto_examples/svm/plot_custom_kernel.rst.txt b/dev/_sources/auto_examples/svm/plot_custom_kernel.rst.txt
index 771cf33fa78ff..686001adcbd00 100644
--- a/dev/_sources/auto_examples/svm/plot_custom_kernel.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_custom_kernel.rst.txt
@@ -95,7 +95,7 @@ plot the decision surface and the support vectors.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.093 seconds)
+ **Total running time of the script:** (0 minutes 0.094 seconds)
.. _sphx_glr_download_auto_examples_svm_plot_custom_kernel.py:
diff --git a/dev/_sources/auto_examples/svm/plot_iris_svc.rst.txt b/dev/_sources/auto_examples/svm/plot_iris_svc.rst.txt
index 9c901cf90d4ee..272e499443e36 100644
--- a/dev/_sources/auto_examples/svm/plot_iris_svc.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_iris_svc.rst.txt
@@ -127,7 +127,7 @@ kernel and its parameters.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.224 seconds)
+ **Total running time of the script:** (0 minutes 0.221 seconds)
.. _sphx_glr_download_auto_examples_svm_plot_iris_svc.py:
diff --git a/dev/_sources/auto_examples/svm/plot_linearsvc_support_vectors.rst.txt b/dev/_sources/auto_examples/svm/plot_linearsvc_support_vectors.rst.txt
index c7f7e05b946ee..2e2c97c40480c 100644
--- a/dev/_sources/auto_examples/svm/plot_linearsvc_support_vectors.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_linearsvc_support_vectors.rst.txt
@@ -96,7 +96,7 @@ vectors in LinearSVC.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.221 seconds)
+ **Total running time of the script:** (0 minutes 0.208 seconds)
.. _sphx_glr_download_auto_examples_svm_plot_linearsvc_support_vectors.py:
diff --git a/dev/_sources/auto_examples/svm/plot_oneclass.rst.txt b/dev/_sources/auto_examples/svm/plot_oneclass.rst.txt
index 2561c46372278..7250298428255 100644
--- a/dev/_sources/auto_examples/svm/plot_oneclass.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_oneclass.rst.txt
@@ -160,7 +160,7 @@ classifying new data as similar or different to the training set.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.191 seconds)
+ **Total running time of the script:** (0 minutes 0.146 seconds)
.. _sphx_glr_download_auto_examples_svm_plot_oneclass.py:
diff --git a/dev/_sources/auto_examples/svm/plot_rbf_parameters.rst.txt b/dev/_sources/auto_examples/svm/plot_rbf_parameters.rst.txt
index 86c0e03480775..a0e1ace385e48 100644
--- a/dev/_sources/auto_examples/svm/plot_rbf_parameters.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_rbf_parameters.rst.txt
@@ -371,7 +371,7 @@ the same color.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 5.220 seconds)
+ **Total running time of the script:** (0 minutes 5.070 seconds)
.. _sphx_glr_download_auto_examples_svm_plot_rbf_parameters.py:
diff --git a/dev/_sources/auto_examples/svm/plot_separating_hyperplane.rst.txt b/dev/_sources/auto_examples/svm/plot_separating_hyperplane.rst.txt
index 64187e53954fa..5bd317e4e26ca 100644
--- a/dev/_sources/auto_examples/svm/plot_separating_hyperplane.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_separating_hyperplane.rst.txt
@@ -86,7 +86,7 @@ linear kernel.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.067 seconds)
+ **Total running time of the script:** (0 minutes 0.070 seconds)
.. _sphx_glr_download_auto_examples_svm_plot_separating_hyperplane.py:
diff --git a/dev/_sources/auto_examples/svm/plot_svm_anova.rst.txt b/dev/_sources/auto_examples/svm/plot_svm_anova.rst.txt
index 72cb34d56c84a..c4b7b6584cb93 100644
--- a/dev/_sources/auto_examples/svm/plot_svm_anova.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_svm_anova.rst.txt
@@ -145,7 +145,7 @@ Plot the cross-validation score as a function of percentile of features
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.328 seconds)
+ **Total running time of the script:** (0 minutes 0.335 seconds)
.. _sphx_glr_download_auto_examples_svm_plot_svm_anova.py:
diff --git a/dev/_sources/auto_examples/svm/plot_svm_kernels.rst.txt b/dev/_sources/auto_examples/svm/plot_svm_kernels.rst.txt
index a634da47de460..c623316239dc6 100644
--- a/dev/_sources/auto_examples/svm/plot_svm_kernels.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_svm_kernels.rst.txt
@@ -459,7 +459,7 @@ reasonable decision boundary for the above dataset.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 1.413 seconds)
+ **Total running time of the script:** (0 minutes 1.383 seconds)
.. _sphx_glr_download_auto_examples_svm_plot_svm_kernels.py:
diff --git a/dev/_sources/auto_examples/svm/plot_svm_margin.rst.txt b/dev/_sources/auto_examples/svm/plot_svm_margin.rst.txt
index 8007b9fbe9df8..f211ed7e25b4c 100644
--- a/dev/_sources/auto_examples/svm/plot_svm_margin.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_svm_margin.rst.txt
@@ -137,7 +137,7 @@ the margins to be calculated using all the data in the area.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.066 seconds)
+ **Total running time of the script:** (0 minutes 0.065 seconds)
.. _sphx_glr_download_auto_examples_svm_plot_svm_margin.py:
diff --git a/dev/_sources/auto_examples/svm/plot_svm_regression.rst.txt b/dev/_sources/auto_examples/svm/plot_svm_regression.rst.txt
index 9a03bb8dd3550..f780d8794f772 100644
--- a/dev/_sources/auto_examples/svm/plot_svm_regression.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_svm_regression.rst.txt
@@ -153,7 +153,7 @@ Look at the results
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 6.100 seconds)
+ **Total running time of the script:** (0 minutes 6.304 seconds)
.. _sphx_glr_download_auto_examples_svm_plot_svm_regression.py:
diff --git a/dev/_sources/auto_examples/svm/plot_svm_scale_c.rst.txt b/dev/_sources/auto_examples/svm/plot_svm_scale_c.rst.txt
index c354bda2183f5..50ef3166a8eec 100644
--- a/dev/_sources/auto_examples/svm/plot_svm_scale_c.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_svm_scale_c.rst.txt
@@ -323,7 +323,7 @@ builder.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 18.798 seconds)
+ **Total running time of the script:** (0 minutes 19.914 seconds)
.. _sphx_glr_download_auto_examples_svm_plot_svm_scale_c.py:
diff --git a/dev/_sources/auto_examples/svm/plot_svm_tie_breaking.rst.txt b/dev/_sources/auto_examples/svm/plot_svm_tie_breaking.rst.txt
index 964a14aff1c68..5fcf739b02d99 100644
--- a/dev/_sources/auto_examples/svm/plot_svm_tie_breaking.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_svm_tie_breaking.rst.txt
@@ -101,7 +101,7 @@ create a non-convex decision boundary in that area.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.974 seconds)
+ **Total running time of the script:** (0 minutes 0.996 seconds)
.. _sphx_glr_download_auto_examples_svm_plot_svm_tie_breaking.py:
diff --git a/dev/_sources/auto_examples/svm/plot_weighted_samples.rst.txt b/dev/_sources/auto_examples/svm/plot_weighted_samples.rst.txt
index 1c2bba57c4475..b0ea1eb5ababd 100644
--- a/dev/_sources/auto_examples/svm/plot_weighted_samples.rst.txt
+++ b/dev/_sources/auto_examples/svm/plot_weighted_samples.rst.txt
@@ -124,7 +124,7 @@ class, making the deformation of the decision boundary more visible.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.284 seconds)
+ **Total running time of the script:** (0 minutes 0.285 seconds)
.. _sphx_glr_download_auto_examples_svm_plot_weighted_samples.py:
diff --git a/dev/_sources/auto_examples/text/plot_document_classification_20newsgroups.rst.txt b/dev/_sources/auto_examples/text/plot_document_classification_20newsgroups.rst.txt
index 155613f2ed007..e5b4aa4db4364 100644
--- a/dev/_sources/auto_examples/text/plot_document_classification_20newsgroups.rst.txt
+++ b/dev/_sources/auto_examples/text/plot_document_classification_20newsgroups.rst.txt
@@ -193,9 +193,9 @@ metadata stripping.
2034 documents - 3.98MB (training set)
1353 documents - 2.87MB (test set)
4 categories
- vectorize training done in 0.374s at 10.638MB/s
+ vectorize training done in 0.563s at 7.063MB/s
n_samples: 2034, n_features: 7831
- vectorize testing done in 0.244s at 11.764MB/s
+ vectorize testing done in 0.414s at 6.928MB/s
n_samples: 1353, n_features: 7831
@@ -717,8 +717,8 @@ for a demo on how such tuning can be done.
________________________________________________________________________________
Training:
LogisticRegression(C=5, max_iter=1000)
- train time: 0.163s
- test time: 0.000643s
+ train time: 0.247s
+ test time: 0.000694s
accuracy: 0.772
dimensionality: 5316
density: 1.0
@@ -729,8 +729,8 @@ for a demo on how such tuning can be done.
________________________________________________________________________________
Training:
RidgeClassifier(solver='sparse_cg')
- train time: 0.0312s
- test time: 0.00056s
+ train time: 0.0418s
+ test time: 0.000688s
accuracy: 0.76
dimensionality: 5316
density: 1.0
@@ -741,8 +741,8 @@ for a demo on how such tuning can be done.
________________________________________________________________________________
Training:
KNeighborsClassifier(n_neighbors=100)
- train time: 0.0007s
- test time: 0.0641s
+ train time: 0.00103s
+ test time: 0.078s
accuracy: 0.752
================================================================================
@@ -750,8 +750,8 @@ for a demo on how such tuning can be done.
________________________________________________________________________________
Training:
RandomForestClassifier()
- train time: 1.63s
- test time: 0.0535s
+ train time: 1.8s
+ test time: 0.0549s
accuracy: 0.7
================================================================================
@@ -759,8 +759,8 @@ for a demo on how such tuning can be done.
________________________________________________________________________________
Training:
LinearSVC(C=0.1, dual=False)
- train time: 0.0286s
- test time: 0.00054s
+ train time: 0.0304s
+ test time: 0.000684s
accuracy: 0.752
dimensionality: 5316
density: 1.0
@@ -771,8 +771,8 @@ for a demo on how such tuning can be done.
________________________________________________________________________________
Training:
SGDClassifier(early_stopping=True, loss='log_loss', n_iter_no_change=3)
- train time: 0.0311s
- test time: 0.000534s
+ train time: 0.0336s
+ test time: 0.000648s
accuracy: 0.764
dimensionality: 5316
density: 1.0
@@ -783,8 +783,8 @@ for a demo on how such tuning can be done.
________________________________________________________________________________
Training:
NearestCentroid()
- train time: 2.06s
- test time: 0.00175s
+ train time: 1.14s
+ test time: 0.00203s
accuracy: 0.748
================================================================================
@@ -792,8 +792,8 @@ for a demo on how such tuning can be done.
________________________________________________________________________________
Training:
ComplementNB(alpha=0.1)
- train time: 0.00194s
- test time: 0.00053s
+ train time: 0.00231s
+ test time: 0.000585s
accuracy: 0.779
@@ -894,7 +894,7 @@ classification problems.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 8.542 seconds)
+ **Total running time of the script:** (0 minutes 8.828 seconds)
.. _sphx_glr_download_auto_examples_text_plot_document_classification_20newsgroups.py:
diff --git a/dev/_sources/auto_examples/text/plot_document_clustering.rst.txt b/dev/_sources/auto_examples/text/plot_document_clustering.rst.txt
index 0daed1c36c202..8d29c3ed8eede 100644
--- a/dev/_sources/auto_examples/text/plot_document_clustering.rst.txt
+++ b/dev/_sources/auto_examples/text/plot_document_clustering.rst.txt
@@ -267,7 +267,7 @@ IDF normalization as provided by
.. code-block:: none
- vectorization done in 0.402 s
+ vectorization done in 0.475 s
n_samples: 3387, n_features: 7929
@@ -389,7 +389,7 @@ best inertia (objective function of k-means) is chosen.
.. code-block:: none
- clustering done in 0.19 ± 0.05 s
+ clustering done in 0.25 ± 0.07 s
Homogeneity: 0.349 ± 0.010
Completeness: 0.398 ± 0.009
V-measure: 0.372 ± 0.009
@@ -448,7 +448,7 @@ the information retrieval and text mining literature.
.. code-block:: none
- LSA done in 0.365 s
+ LSA done in 0.408 s
Explained variance of the SVD step: 18.4%
@@ -527,7 +527,7 @@ clustering evaluation metrics have improved. We repeat the experiment with
.. code-block:: none
- clustering done in 0.02 ± 0.00 s
+ clustering done in 0.02 ± 0.01 s
Homogeneity: 0.333 ± 0.084
Completeness: 0.351 ± 0.066
V-measure: 0.341 ± 0.076
@@ -618,7 +618,7 @@ the hashed vector space.
.. code-block:: none
- vectorization done in 3.063 s
+ vectorization done in 1.871 s
@@ -772,7 +772,7 @@ quality compared to the traditional k-means algorithm.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 8.783 seconds)
+ **Total running time of the script:** (0 minutes 8.349 seconds)
.. _sphx_glr_download_auto_examples_text_plot_document_clustering.py:
diff --git a/dev/_sources/auto_examples/text/plot_hashing_vs_dict_vectorizer.rst.txt b/dev/_sources/auto_examples/text/plot_hashing_vs_dict_vectorizer.rst.txt
index 5c6783f769ce1..51afd959096f3 100644
--- a/dev/_sources/auto_examples/text/plot_hashing_vs_dict_vectorizer.rst.txt
+++ b/dev/_sources/auto_examples/text/plot_hashing_vs_dict_vectorizer.rst.txt
@@ -234,7 +234,7 @@ both of them receive dictionaries as input.
.. code-block:: none
- done in 1.095 s at 5.7 MB/s
+ done in 1.242 s at 5.0 MB/s
Found 47928 unique terms
@@ -374,7 +374,7 @@ The default number of features for the
.. code-block:: none
- done in 0.616 s at 10.1 MB/s
+ done in 0.676 s at 9.2 MB/s
Found 43873 unique tokens
@@ -414,7 +414,7 @@ majority of them is inactive.
.. code-block:: none
- done in 0.660 s at 9.5 MB/s
+ done in 0.630 s at 9.9 MB/s
Found 47668 unique tokens
@@ -456,7 +456,7 @@ passing a dictionary with an implied frequency of 1 for each feature name.
.. code-block:: none
- done in 0.676 s at 9.2 MB/s
+ done in 0.673 s at 9.3 MB/s
Found 43873 unique tokens
@@ -544,7 +544,7 @@ is more flexible. In particular it accepts various regex patterns through the
.. code-block:: none
- done in 0.760 s at 8.2 MB/s
+ done in 0.780 s at 8.0 MB/s
Found 47885 unique terms
@@ -590,7 +590,7 @@ preprocessing and tokenization of the
.. code-block:: none
- done in 0.623 s at 10.0 MB/s
+ done in 0.572 s at 10.9 MB/s
@@ -645,7 +645,7 @@ normalizing and weighting from a
.. code-block:: none
- done in 0.811 s at 7.7 MB/s
+ done in 0.748 s at 8.3 MB/s
Found 47885 unique terms
@@ -707,7 +707,7 @@ compiles a regular expression once and then reuses it for all the documents.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 5.718 seconds)
+ **Total running time of the script:** (0 minutes 5.812 seconds)
.. _sphx_glr_download_auto_examples_text_plot_hashing_vs_dict_vectorizer.py:
diff --git a/dev/_sources/auto_examples/tree/plot_cost_complexity_pruning.rst.txt b/dev/_sources/auto_examples/tree/plot_cost_complexity_pruning.rst.txt
index 33dd2cc318ce7..211b0d452e2bf 100644
--- a/dev/_sources/auto_examples/tree/plot_cost_complexity_pruning.rst.txt
+++ b/dev/_sources/auto_examples/tree/plot_cost_complexity_pruning.rst.txt
@@ -233,7 +233,7 @@ In this example, setting ``ccp_alpha=0.015`` maximizes the testing accuracy.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.459 seconds)
+ **Total running time of the script:** (0 minutes 0.530 seconds)
.. _sphx_glr_download_auto_examples_tree_plot_cost_complexity_pruning.py:
diff --git a/dev/_sources/auto_examples/tree/plot_iris_dtc.rst.txt b/dev/_sources/auto_examples/tree/plot_iris_dtc.rst.txt
index f9f08cdb1980d..fd34d2a9e38f9 100644
--- a/dev/_sources/auto_examples/tree/plot_iris_dtc.rst.txt
+++ b/dev/_sources/auto_examples/tree/plot_iris_dtc.rst.txt
@@ -169,7 +169,7 @@ together.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.834 seconds)
+ **Total running time of the script:** (0 minutes 1.038 seconds)
.. _sphx_glr_download_auto_examples_tree_plot_iris_dtc.py:
diff --git a/dev/_sources/auto_examples/tree/plot_tree_regression.rst.txt b/dev/_sources/auto_examples/tree/plot_tree_regression.rst.txt
index 110a9692930e3..f99016d20ad94 100644
--- a/dev/_sources/auto_examples/tree/plot_tree_regression.rst.txt
+++ b/dev/_sources/auto_examples/tree/plot_tree_regression.rst.txt
@@ -1603,7 +1603,7 @@ influenced by the noise.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.318 seconds)
+ **Total running time of the script:** (0 minutes 0.365 seconds)
.. _sphx_glr_download_auto_examples_tree_plot_tree_regression.py:
diff --git a/dev/_sources/auto_examples/tree/plot_unveil_tree_structure.rst.txt b/dev/_sources/auto_examples/tree/plot_unveil_tree_structure.rst.txt
index 469f41f4e901f..386030c31a7a1 100644
--- a/dev/_sources/auto_examples/tree/plot_unveil_tree_structure.rst.txt
+++ b/dev/_sources/auto_examples/tree/plot_unveil_tree_structure.rst.txt
@@ -1032,7 +1032,7 @@ through.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** (0 minutes 0.080 seconds)
+ **Total running time of the script:** (0 minutes 0.089 seconds)
.. _sphx_glr_download_auto_examples_tree_plot_unveil_tree_structure.py:
diff --git a/dev/_sources/developers/contributing.rst.txt b/dev/_sources/developers/contributing.rst.txt
index 1f11008748de1..de3074839ad7d 100644
--- a/dev/_sources/developers/contributing.rst.txt
+++ b/dev/_sources/developers/contributing.rst.txt
@@ -99,6 +99,8 @@ and follows the decision-making process outlined in :ref:`governance`.
Look for issues marked "help wanted" or similar. Helping these projects may help
scikit-learn too. See also :ref:`related_projects`.
+.. _automated_contributions_policy:
+
Automated Contributions Policy
==============================
@@ -107,7 +109,17 @@ fully-automated tools. Maintainers reserve the right, at their sole discretion,
to close such submissions and to block any account responsible for them.
Ideally, contributions should follow from a human-to-human discussion in the
-form of an issue.
+form of an issue. In particular, please do not paste AI generated text in the
+description of issues, PRs or in comments as it makes it significantly harder for
+reviewers to assess the relevance of your contribution and the potential value it
+brings to future end-users of the library. Note that it's fine to use AI tools
+to proofread or improve your draft text if you are not a native English speaker,
+but reviewers are not interested in unknowingly interacting back and forth with
+automated chatbots that fundamentally do not care about the value of our open
+source project.
+
+Please self review all code or documentation changes made by AI tools before
+submitting them under your name.
Submitting a bug report or a feature request
============================================
diff --git a/dev/_sources/faq.rst.txt b/dev/_sources/faq.rst.txt
index 99cb13c5be4d6..74026abc1ef32 100644
--- a/dev/_sources/faq.rst.txt
+++ b/dev/_sources/faq.rst.txt
@@ -300,6 +300,33 @@ reviewers are busy. We ask for your understanding and request that you
not close your pull request or discontinue your work solely because of
this reason.
+What does the "spam" label for issues or pull requests mean?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The "spam" label is an indication for reviewers that the issue or
+pull request may not have received sufficient effort or preparation
+from the author for a productive review. The maintainers are using this label
+as a way to deal with the increase of low value PRs and issues.
+
+If an issue or PR was labeled as spam and simultaneously closed, the decision
+is final. A common reason for this happening is when people open a PR for an
+issue that is still under discussion. Please wait for the discussion to
+converge before opening a PR.
+
+If your issue or PR was labeled as spam and not closed the following steps
+can increase the chances of the label being removed:
+
+- follow the :ref:`contribution guidelines ` and use the provided
+ issue and pull request templates
+- improve the formatting and grammar of the text of the title and description of the issue/PR
+- improve the diff to remove noise and unrelated changes
+- improve the issue or pull request title to be more descriptive
+- self review your code, especially if :ref:`you used AI tools to generate it `
+- refrain from opening PRs that paraphrase existing code or documentation
+ without actually improving the correctness, clarity or educational
+ value of the existing code or documentation.
+
+
.. _new_algorithms_inclusion_criteria:
What are the inclusion criteria for new algorithms?
diff --git a/dev/_sources/sg_execution_times.rst.txt b/dev/_sources/sg_execution_times.rst.txt
index 4d7736f545667..65ffad46bbd1a 100644
--- a/dev/_sources/sg_execution_times.rst.txt
+++ b/dev/_sources/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
Computation times
=================
-**21:01.920** total execution time for 279 files **from all galleries**:
+**22:20.860** total execution time for 279 files **from all galleries**:
.. container::
@@ -32,830 +32,830 @@ Computation times
* - Example
- Time
- Mem (MB)
+ * - :ref:`sphx_glr_auto_examples_miscellaneous_plot_outlier_detection_bench.py` (``../examples/miscellaneous/plot_outlier_detection_bench.py``)
+ - 00:57.192
+ - 0.0
* - :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py` (``../examples/feature_selection/plot_select_from_model_diabetes.py``)
- - 00:58.378
+ - 00:56.967
- 0.0
* - :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py` (``../examples/ensemble/plot_forest_hist_grad_boosting_comparison.py``)
- - 00:52.578
+ - 00:53.366
- 0.0
* - :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py` (``../examples/compose/plot_compare_reduction.py``)
- - 00:48.015
- - 0.0
- * - :ref:`sphx_glr_auto_examples_miscellaneous_plot_outlier_detection_bench.py` (``../examples/miscellaneous/plot_outlier_detection_bench.py``)
- - 00:43.628
+ - 00:45.156
- 0.0
- * - :ref:`sphx_glr_auto_examples_model_selection_plot_tuned_decision_threshold.py` (``../examples/model_selection/plot_tuned_decision_threshold.py``)
- - 00:35.871
- - 0.0
- * - :ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder.py` (``../examples/preprocessing/plot_target_encoder.py``)
- - 00:30.518
+ * - :ref:`sphx_glr_auto_examples_kernel_approximation_plot_scalable_poly_kernels.py` (``../examples/kernel_approximation/plot_scalable_poly_kernels.py``)
+ - 00:39.138
- 0.0
* - :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` (``../examples/model_selection/plot_grid_search_text_feature_extraction.py``)
- - 00:30.517
+ - 00:36.763
- 0.0
- * - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_early_stopping.py` (``../examples/linear_model/plot_sgd_early_stopping.py``)
- - 00:30.042
+ * - :ref:`sphx_glr_auto_examples_model_selection_plot_tuned_decision_threshold.py` (``../examples/model_selection/plot_tuned_decision_threshold.py``)
+ - 00:36.388
- 0.0
* - :ref:`sphx_glr_auto_examples_model_selection_plot_cost_sensitive_learning.py` (``../examples/model_selection/plot_cost_sensitive_learning.py``)
- - 00:27.918
+ - 00:36.317
+ - 0.0
+ * - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_early_stopping.py` (``../examples/linear_model/plot_sgd_early_stopping.py``)
+ - 00:30.474
- 0.0
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- * - :ref:`sphx_glr_auto_examples_linear_model_plot_poisson_regression_non_normal_loss.py` (``../examples/linear_model/plot_poisson_regression_non_normal_loss.py``)
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- * - :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py` (``../examples/model_selection/plot_grid_search_refit_callable.py``)
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- * - :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_0_24_0.py` (``../examples/release_highlights/plot_release_highlights_0_24_0.py``)
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* - :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py` (``../examples/miscellaneous/plot_johnson_lindenstrauss_bound.py``)
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* - :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_oob.py` (``../examples/ensemble/plot_gradient_boosting_oob.py``)
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- * - :ref:`sphx_glr_auto_examples_applications_plot_time_series_lagged_features.py` (``../examples/applications/plot_time_series_lagged_features.py``)
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* - :ref:`sphx_glr_auto_examples_model_selection_plot_multi_metric_evaluation.py` (``../examples/model_selection/plot_multi_metric_evaluation.py``)
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- * - :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py` (``../examples/ensemble/plot_gradient_boosting_regularization.py``)
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- * - :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py` (``../examples/decomposition/plot_faces_decomposition.py``)
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- * - :ref:`sphx_glr_auto_examples_manifold_plot_manifold_sphere.py` (``../examples/manifold/plot_manifold_sphere.py``)
- - 00:07.742
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* - :ref:`sphx_glr_auto_examples_classification_plot_lda.py` (``../examples/classification/plot_lda.py``)
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- * - :ref:`sphx_glr_auto_examples_linear_model_plot_tweedie_regression_insurance_claims.py` (``../examples/linear_model/plot_tweedie_regression_insurance_claims.py``)
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* - :ref:`sphx_glr_auto_examples_semi_supervised_plot_semi_supervised_newsgroups.py` (``../examples/semi_supervised/plot_semi_supervised_newsgroups.py``)
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- * - :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_heatmap.py` (``../examples/model_selection/plot_successive_halving_heatmap.py``)
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* - :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py` (``../examples/model_selection/plot_nested_cross_validation_iris.py``)
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- * - :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py` (``../examples/applications/plot_species_distribution_modeling.py``)
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* - :ref:`sphx_glr_auto_examples_mixture_plot_concentration_prior.py` (``../examples/mixture/plot_concentration_prior.py``)
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- * - :ref:`sphx_glr_auto_examples_impute_plot_iterative_imputer_variants_comparison.py` (``../examples/impute/plot_iterative_imputer_variants_comparison.py``)
- - 00:06.331
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* - :ref:`sphx_glr_auto_examples_applications_plot_digits_denoising.py` (``../examples/applications/plot_digits_denoising.py``)
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- * - :ref:`sphx_glr_auto_examples_semi_supervised_plot_self_training_varying_threshold.py` (``../examples/semi_supervised/plot_self_training_varying_threshold.py``)
- - 00:06.105
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* - :ref:`sphx_glr_auto_examples_svm_plot_svm_regression.py` (``../examples/svm/plot_svm_regression.py``)
- - 00:06.100
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- * - :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_2_0.py` (``../examples/release_highlights/plot_release_highlights_1_2_0.py``)
- - 00:05.867
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- * - :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_noisy.py` (``../examples/gaussian_process/plot_gpr_noisy.py``)
- - 00:05.848
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* - :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py` (``../examples/text/plot_hashing_vs_dict_vectorizer.py``)
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* - :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py` (``../examples/applications/plot_face_recognition.py``)
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- * - :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py` (``../examples/cluster/plot_kmeans_digits.py``)
- - 00:05.421
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- * - :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_iterations.py` (``../examples/model_selection/plot_successive_halving_iterations.py``)
- - 00:05.323
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- * - :ref:`sphx_glr_auto_examples_svm_plot_rbf_parameters.py` (``../examples/svm/plot_rbf_parameters.py``)
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* - :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_20newsgroups.py` (``../examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py``)
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- * - :ref:`sphx_glr_auto_examples_cluster_plot_coin_segmentation.py` (``../examples/cluster/plot_coin_segmentation.py``)
- - 00:04.916
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- * - :ref:`sphx_glr_auto_examples_applications_plot_stock_market.py` (``../examples/applications/plot_stock_market.py``)
- - 00:04.877
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- * - :ref:`sphx_glr_auto_examples_model_selection_plot_train_error_vs_test_error.py` (``../examples/model_selection/plot_train_error_vs_test_error.py``)
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- * - :ref:`sphx_glr_auto_examples_neighbors_plot_digits_kde_sampling.py` (``../examples/neighbors/plot_digits_kde_sampling.py``)
- - 00:04.695
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* - :ref:`sphx_glr_auto_examples_applications_plot_model_complexity_influence.py` (``../examples/applications/plot_model_complexity_influence.py``)
- - 00:04.643
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- * - :ref:`sphx_glr_auto_examples_gaussian_process_plot_compare_gpr_krr.py` (``../examples/gaussian_process/plot_compare_gpr_krr.py``)
- - 00:04.538
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- * - :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_co2.py` (``../examples/gaussian_process/plot_gpr_co2.py``)
- - 00:04.477
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- * - :ref:`sphx_glr_auto_examples_inspection_plot_permutation_importance.py` (``../examples/inspection/plot_permutation_importance.py``)
- - 00:04.412
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- * - :ref:`sphx_glr_auto_examples_inspection_plot_permutation_importance_multicollinear.py` (``../examples/inspection/plot_permutation_importance_multicollinear.py``)
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* - :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_multiclass.py` (``../examples/ensemble/plot_adaboost_multiclass.py``)
- - 00:04.214
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* - :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py` (``../examples/ensemble/plot_gradient_boosting_categorical.py``)
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- * - :ref:`sphx_glr_auto_examples_covariance_plot_robust_vs_empirical_covariance.py` (``../examples/covariance/plot_robust_vs_empirical_covariance.py``)
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- * - :ref:`sphx_glr_auto_examples_cluster_plot_face_compress.py` (``../examples/cluster/plot_face_compress.py``)
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* - :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpc.py` (``../examples/gaussian_process/plot_gpc.py``)
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- * - :ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_alpha.py` (``../examples/neural_networks/plot_mlp_alpha.py``)
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- * - :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_4_0.py` (``../examples/release_highlights/plot_release_highlights_1_4_0.py``)
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* - :ref:`sphx_glr_auto_examples_linear_model_plot_robust_fit.py` (``../examples/linear_model/plot_robust_fit.py``)
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* - :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py` (``../examples/miscellaneous/plot_kernel_approximation.py``)
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- - 00:01.422
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- - 00:01.416
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- * - :ref:`sphx_glr_auto_examples_svm_plot_svm_kernels.py` (``../examples/svm/plot_svm_kernels.py``)
- - 00:01.413
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- * - :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_3_0.py` (``../examples/release_highlights/plot_release_highlights_1_3_0.py``)
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- * - :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_0_22_0.py` (``../examples/release_highlights/plot_release_highlights_0_22_0.py``)
- - 00:01.340
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* - :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py` (``../examples/ensemble/plot_gradient_boosting_regression.py``)
- - 00:01.339
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- * - :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_dense_vs_sparse_data.py` (``../examples/linear_model/plot_lasso_dense_vs_sparse_data.py``)
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* - :ref:`sphx_glr_auto_examples_model_selection_plot_cv_indices.py` (``../examples/model_selection/plot_cv_indices.py``)
- - 00:01.298
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* - :ref:`sphx_glr_auto_examples_compose_plot_digits_pipe.py` (``../examples/compose/plot_digits_pipe.py``)
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- * - :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_stability_low_dim_dense.py` (``../examples/cluster/plot_kmeans_stability_low_dim_dense.py``)
- - 00:01.279
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* - :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_assumptions.py` (``../examples/cluster/plot_kmeans_assumptions.py``)
- - 00:01.104
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- - 00:01.082
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- * - :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py` (``../examples/cluster/plot_kmeans_silhouette_analysis.py``)
- - 00:01.073
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* - :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py` (``../examples/cluster/plot_adjusted_for_chance_measures.py``)
- - 00:01.050
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* - :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_clustering_metrics.py` (``../examples/cluster/plot_agglomerative_clustering_metrics.py``)
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* - :ref:`sphx_glr_auto_examples_cluster_plot_bisect_kmeans.py` (``../examples/cluster/plot_bisect_kmeans.py``)
- - 00:01.039
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* - :ref:`sphx_glr_auto_examples_svm_plot_svm_tie_breaking.py` (``../examples/svm/plot_svm_tie_breaking.py``)
- - 00:00.974
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- * - :ref:`sphx_glr_auto_examples_semi_supervised_plot_semi_supervised_versus_svm_iris.py` (``../examples/semi_supervised/plot_semi_supervised_versus_svm_iris.py``)
- - 00:00.933
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* - :ref:`sphx_glr_auto_examples_ensemble_plot_voting_regressor.py` (``../examples/ensemble/plot_voting_regressor.py``)
- - 00:00.928
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* - :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py` (``../examples/linear_model/plot_lasso_and_elasticnet.py``)
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* - :ref:`sphx_glr_auto_examples_linear_model_plot_ols_ridge.py` (``../examples/linear_model/plot_ols_ridge.py``)
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* - :ref:`sphx_glr_auto_examples_decomposition_plot_varimax_fa.py` (``../examples/decomposition/plot_varimax_fa.py``)
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* - :ref:`sphx_glr_auto_examples_applications_plot_outlier_detection_wine.py` (``../examples/applications/plot_outlier_detection_wine.py``)
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* - :ref:`sphx_glr_auto_examples_svm_plot_svm_anova.py` (``../examples/svm/plot_svm_anova.py``)
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* - :ref:`sphx_glr_auto_examples_linear_model_plot_bayesian_ridge_curvefit.py` (``../examples/linear_model/plot_bayesian_ridge_curvefit.py``)
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- * - :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization.py` (``../examples/preprocessing/plot_discretization.py``)
+ * - :ref:`sphx_glr_auto_examples_linear_model_plot_multi_task_lasso_support.py` (``../examples/linear_model/plot_multi_task_lasso_support.py``)
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+ * - :ref:`sphx_glr_auto_examples_model_selection_plot_det.py` (``../examples/model_selection/plot_det.py``)
+ - 00:00.247
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+ * - :ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py` (``../examples/cross_decomposition/plot_compare_cross_decomposition.py``)
+ - 00:00.245
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+ * - :ref:`sphx_glr_auto_examples_linear_model_plot_omp.py` (``../examples/linear_model/plot_omp.py``)
- 00:00.230
- 0.0
- * - :ref:`sphx_glr_auto_examples_decomposition_plot_incremental_pca.py` (``../examples/decomposition/plot_incremental_pca.py``)
+ * - :ref:`sphx_glr_auto_examples_neighbors_plot_regression.py` (``../examples/neighbors/plot_regression.py``)
- 00:00.228
- 0.0
- * - :ref:`sphx_glr_auto_examples_svm_plot_iris_svc.py` (``../examples/svm/plot_iris_svc.py``)
- - 00:00.224
+ * - :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization.py` (``../examples/preprocessing/plot_discretization.py``)
+ - 00:00.227
- 0.0
- * - :ref:`sphx_glr_auto_examples_feature_selection_plot_f_test_vs_mi.py` (``../examples/feature_selection/plot_f_test_vs_mi.py``)
- - 00:00.222
+ * - :ref:`sphx_glr_auto_examples_decomposition_plot_incremental_pca.py` (``../examples/decomposition/plot_incremental_pca.py``)
+ - 00:00.224
- 0.0
- * - :ref:`sphx_glr_auto_examples_linear_model_plot_multi_task_lasso_support.py` (``../examples/linear_model/plot_multi_task_lasso_support.py``)
+ * - :ref:`sphx_glr_auto_examples_svm_plot_iris_svc.py` (``../examples/svm/plot_iris_svc.py``)
- 00:00.221
- 0.0
- * - :ref:`sphx_glr_auto_examples_svm_plot_linearsvc_support_vectors.py` (``../examples/svm/plot_linearsvc_support_vectors.py``)
- - 00:00.221
+ * - :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_lda.py` (``../examples/decomposition/plot_pca_vs_lda.py``)
+ - 00:00.218
- 0.0
- * - :ref:`sphx_glr_auto_examples_mixture_plot_gmm.py` (``../examples/mixture/plot_gmm.py``)
- - 00:00.217
+ * - :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_on_structured_data.py` (``../examples/gaussian_process/plot_gpr_on_structured_data.py``)
+ - 00:00.216
- 0.0
- * - :ref:`sphx_glr_auto_examples_model_selection_plot_underfitting_overfitting.py` (``../examples/model_selection/plot_underfitting_overfitting.py``)
- - 00:00.214
+ * - :ref:`sphx_glr_auto_examples_model_selection_plot_roc_crossval.py` (``../examples/model_selection/plot_roc_crossval.py``)
+ - 00:00.213
- 0.0
- * - :ref:`sphx_glr_auto_examples_linear_model_plot_omp.py` (``../examples/linear_model/plot_omp.py``)
- - 00:00.210
+ * - :ref:`sphx_glr_auto_examples_model_selection_plot_underfitting_overfitting.py` (``../examples/model_selection/plot_underfitting_overfitting.py``)
+ - 00:00.211
- 0.0
- * - :ref:`sphx_glr_auto_examples_model_selection_plot_roc_crossval.py` (``../examples/model_selection/plot_roc_crossval.py``)
- - 00:00.204
+ * - :ref:`sphx_glr_auto_examples_svm_plot_linearsvc_support_vectors.py` (``../examples/svm/plot_linearsvc_support_vectors.py``)
+ - 00:00.208
- 0.0
- * - :ref:`sphx_glr_auto_examples_model_selection_plot_det.py` (``../examples/model_selection/plot_det.py``)
+ * - :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py` (``../examples/linear_model/plot_logistic_path.py``)
- 00:00.204
- 0.0
* - :ref:`sphx_glr_auto_examples_model_selection_plot_cv_predict.py` (``../examples/model_selection/plot_cv_predict.py``)
- - 00:00.204
+ - 00:00.198
- 0.0
- * - :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_lda.py` (``../examples/decomposition/plot_pca_vs_lda.py``)
- - 00:00.197
- - 0.0
- * - :ref:`sphx_glr_auto_examples_mixture_plot_gmm_covariances.py` (``../examples/mixture/plot_gmm_covariances.py``)
- - 00:00.193
- - 0.0
- * - :ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py` (``../examples/cross_decomposition/plot_compare_cross_decomposition.py``)
- - 00:00.192
- - 0.0
- * - :ref:`sphx_glr_auto_examples_svm_plot_oneclass.py` (``../examples/svm/plot_oneclass.py``)
- - 00:00.191
+ * - :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_7_0.py` (``../examples/release_highlights/plot_release_highlights_1_7_0.py``)
+ - 00:00.194
- 0.0
* - :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection.py` (``../examples/feature_selection/plot_feature_selection.py``)
- - 00:00.187
- - 0.0
- * - :ref:`sphx_glr_auto_examples_neighbors_plot_nca_illustration.py` (``../examples/neighbors/plot_nca_illustration.py``)
- - 00:00.184
+ - 00:00.189
- 0.0
- * - :ref:`sphx_glr_auto_examples_neighbors_plot_nearest_centroid.py` (``../examples/neighbors/plot_nearest_centroid.py``)
- - 00:00.184
+ * - :ref:`sphx_glr_auto_examples_miscellaneous_plot_multilabel.py` (``../examples/miscellaneous/plot_multilabel.py``)
+ - 00:00.182
- 0.0
- * - :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_structure.py` (``../examples/semi_supervised/plot_label_propagation_structure.py``)
- - 00:00.176
+ * - :ref:`sphx_glr_auto_examples_model_selection_plot_confusion_matrix.py` (``../examples/model_selection/plot_confusion_matrix.py``)
+ - 00:00.177
- 0.0
* - :ref:`sphx_glr_auto_examples_cluster_plot_mini_batch_kmeans.py` (``../examples/cluster/plot_mini_batch_kmeans.py``)
- - 00:00.171
+ - 00:00.174
- 0.0
- * - :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpc_isoprobability.py` (``../examples/gaussian_process/plot_gpc_isoprobability.py``)
- - 00:00.170
+ * - :ref:`sphx_glr_auto_examples_cluster_plot_dbscan.py` (``../examples/cluster/plot_dbscan.py``)
+ - 00:00.172
- 0.0
- * - :ref:`sphx_glr_auto_examples_miscellaneous_plot_multilabel.py` (``../examples/miscellaneous/plot_multilabel.py``)
+ * - :ref:`sphx_glr_auto_examples_neighbors_plot_nearest_centroid.py` (``../examples/neighbors/plot_nearest_centroid.py``)
- 00:00.169
- 0.0
* - :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane_unbalanced.py` (``../examples/svm/plot_separating_hyperplane_unbalanced.py``)
- 00:00.167
- 0.0
- * - :ref:`sphx_glr_auto_examples_cluster_plot_dbscan.py` (``../examples/cluster/plot_dbscan.py``)
- - 00:00.166
- - 0.0
- * - :ref:`sphx_glr_auto_examples_model_selection_plot_confusion_matrix.py` (``../examples/model_selection/plot_confusion_matrix.py``)
- - 00:00.165
- - 0.0
- * - :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py` (``../examples/linear_model/plot_logistic_path.py``)
- - 00:00.165
+ * - :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_structure.py` (``../examples/semi_supervised/plot_label_propagation_structure.py``)
+ - 00:00.163
- 0.0
- * - :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_7_0.py` (``../examples/release_highlights/plot_release_highlights_1_7_0.py``)
- - 00:00.157
+ * - :ref:`sphx_glr_auto_examples_neighbors_plot_nca_illustration.py` (``../examples/neighbors/plot_nca_illustration.py``)
+ - 00:00.159
- 0.0
* - :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py` (``../examples/miscellaneous/plot_roc_curve_visualization_api.py``)
- - 00:00.146
+ - 00:00.155
- 0.0
* - :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py` (``../examples/miscellaneous/plot_set_output.py``)
- - 00:00.139
+ - 00:00.154
- 0.0
- * - :ref:`sphx_glr_auto_examples_miscellaneous_plot_isotonic_regression.py` (``../examples/miscellaneous/plot_isotonic_regression.py``)
- - 00:00.136
+ * - :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpc_isoprobability.py` (``../examples/gaussian_process/plot_gpc_isoprobability.py``)
+ - 00:00.153
- 0.0
* - :ref:`sphx_glr_auto_examples_datasets_plot_random_multilabel_dataset.py` (``../examples/datasets/plot_random_multilabel_dataset.py``)
- - 00:00.134
+ - 00:00.148
- 0.0
- * - :ref:`sphx_glr_auto_examples_miscellaneous_plot_pipeline_display.py` (``../examples/miscellaneous/plot_pipeline_display.py``)
- - 00:00.128
+ * - :ref:`sphx_glr_auto_examples_svm_plot_oneclass.py` (``../examples/svm/plot_oneclass.py``)
+ - 00:00.146
- 0.0
- * - :ref:`sphx_glr_auto_examples_cluster_plot_digits_agglomeration.py` (``../examples/cluster/plot_digits_agglomeration.py``)
- - 00:00.119
+ * - :ref:`sphx_glr_auto_examples_miscellaneous_plot_isotonic_regression.py` (``../examples/miscellaneous/plot_isotonic_regression.py``)
+ - 00:00.145
- 0.0
* - :ref:`sphx_glr_auto_examples_mixture_plot_gmm_pdf.py` (``../examples/mixture/plot_gmm_pdf.py``)
- - 00:00.118
+ - 00:00.143
+ - 0.0
+ * - :ref:`sphx_glr_auto_examples_cluster_plot_digits_agglomeration.py` (``../examples/cluster/plot_digits_agglomeration.py``)
+ - 00:00.130
+ - 0.0
+ * - :ref:`sphx_glr_auto_examples_miscellaneous_plot_pipeline_display.py` (``../examples/miscellaneous/plot_pipeline_display.py``)
+ - 00:00.128
+ - 0.0
+ * - :ref:`sphx_glr_auto_examples_linear_model_plot_ransac.py` (``../examples/linear_model/plot_ransac.py``)
+ - 00:00.116
- 0.0
* - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_iris.py` (``../examples/linear_model/plot_sgd_iris.py``)
- 00:00.116
- 0.0
+ * - :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_6_0.py` (``../examples/release_highlights/plot_release_highlights_1_6_0.py``)
+ - 00:00.116
+ - 0.0
* - :ref:`sphx_glr_auto_examples_linear_model_plot_logistic.py` (``../examples/linear_model/plot_logistic.py``)
- - 00:00.113
+ - 00:00.116
- 0.0
* - :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_lars_ic.py` (``../examples/linear_model/plot_lasso_lars_ic.py``)
- - 00:00.112
+ - 00:00.106
- 0.0
* - :ref:`sphx_glr_auto_examples_linear_model_plot_huber_vs_ridge.py` (``../examples/linear_model/plot_huber_vs_ridge.py``)
- - 00:00.102
- - 0.0
- * - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_loss_functions.py` (``../examples/linear_model/plot_sgd_loss_functions.py``)
- - 00:00.100
+ - 00:00.104
- 0.0
- * - :ref:`sphx_glr_auto_examples_svm_plot_custom_kernel.py` (``../examples/svm/plot_custom_kernel.py``)
- - 00:00.093
+ * - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_weighted_samples.py` (``../examples/linear_model/plot_sgd_weighted_samples.py``)
+ - 00:00.103
- 0.0
* - :ref:`sphx_glr_auto_examples_neighbors_plot_lof_outlier_detection.py` (``../examples/neighbors/plot_lof_outlier_detection.py``)
- - 00:00.092
- - 0.0
- * - :ref:`sphx_glr_auto_examples_linear_model_plot_ransac.py` (``../examples/linear_model/plot_ransac.py``)
- - 00:00.092
+ - 00:00.101
- 0.0
- * - :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_6_0.py` (``../examples/release_highlights/plot_release_highlights_1_6_0.py``)
- - 00:00.088
+ * - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_loss_functions.py` (``../examples/linear_model/plot_sgd_loss_functions.py``)
+ - 00:00.100
- 0.0
* - :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_dendrogram.py` (``../examples/cluster/plot_agglomerative_dendrogram.py``)
- - 00:00.085
+ - 00:00.098
+ - 0.0
+ * - :ref:`sphx_glr_auto_examples_svm_plot_custom_kernel.py` (``../examples/svm/plot_custom_kernel.py``)
+ - 00:00.094
- 0.0
* - :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py` (``../examples/tree/plot_unveil_tree_structure.py``)
- - 00:00.080
+ - 00:00.089
- 0.0
- * - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_weighted_samples.py` (``../examples/linear_model/plot_sgd_weighted_samples.py``)
- - 00:00.076
+ * - :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py` (``../examples/linear_model/plot_nnls.py``)
+ - 00:00.080
- 0.0
* - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_separating_hyperplane.py` (``../examples/linear_model/plot_sgd_separating_hyperplane.py``)
- - 00:00.070
- - 0.0
- * - :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py` (``../examples/linear_model/plot_nnls.py``)
- - 00:00.068
+ - 00:00.075
- 0.0
* - :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane.py` (``../examples/svm/plot_separating_hyperplane.py``)
- - 00:00.067
+ - 00:00.070
- 0.0
* - :ref:`sphx_glr_auto_examples_svm_plot_svm_margin.py` (``../examples/svm/plot_svm_margin.py``)
- - 00:00.066
+ - 00:00.065
- 0.0
* - :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_plusplus.py` (``../examples/cluster/plot_kmeans_plusplus.py``)
- - 00:00.059
+ - 00:00.058
- 0.0
* - :ref:`sphx_glr_auto_examples_miscellaneous_plot_metadata_routing.py` (``../examples/miscellaneous/plot_metadata_routing.py``)
- - 00:00.044
+ - 00:00.045
- 0.0
* - :ref:`sphx_glr_auto_examples_miscellaneous_plot_estimator_representation.py` (``../examples/miscellaneous/plot_estimator_representation.py``)
- - 00:00.030
- - 0.0
- * - :ref:`sphx_glr_auto_examples_frozen_plot_frozen_examples.py` (``../examples/frozen/plot_frozen_examples.py``)
- - 00:00.017
+ - 00:00.038
- 0.0
* - :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_0_0.py` (``../examples/release_highlights/plot_release_highlights_1_0_0.py``)
- - 00:00.015
+ - 00:00.018
+ - 0.0
+ * - :ref:`sphx_glr_auto_examples_frozen_plot_frozen_examples.py` (``../examples/frozen/plot_frozen_examples.py``)
+ - 00:00.018
- 0.0
* - :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection_pipeline.py` (``../examples/feature_selection/plot_feature_selection_pipeline.py``)
- 00:00.014
diff --git a/dev/auto_examples/applications/plot_cyclical_feature_engineering.html b/dev/auto_examples/applications/plot_cyclical_feature_engineering.html
index bec91b619f064..5333c03120140 100644
--- a/dev/auto_examples/applications/plot_cyclical_feature_engineering.html
+++ b/dev/auto_examples/applications/plot_cyclical_feature_engineering.html
@@ -2852,7 +2852,7 @@ Concluding remarksGridSearchCV(TweedieRegressor(power=2), param_grid({"alpha": alphas}))
instead of RidgeCV.
-Total running time of the script: (0 minutes 11.910 seconds)
+Total running time of the script: (0 minutes 12.705 seconds)
diff --git a/dev/auto_examples/applications/plot_digits_denoising.html b/dev/auto_examples/applications/plot_digits_denoising.html
index cd1d3e11bea72..c783435768461 100644
--- a/dev/auto_examples/applications/plot_digits_denoising.html
+++ b/dev/auto_examples/applications/plot_digits_denoising.html
@@ -1140,7 +1140,7 @@ Reconstruct and denoise test imagesn_components, gamma, and alpha.
-Total running time of the script: (0 minutes 6.328 seconds)
+Total running time of the script: (0 minutes 6.615 seconds)
Extracting the top 150 eigenfaces from 966 faces
-done in 0.072s
+done in 0.074s
Projecting the input data on the eigenfaces orthonormal basis
done in 0.005s
@@ -1095,7 +1095,7 @@
Fitting the classifier to the training set
-done in 4.839s
+done in 4.992s
Best estimator found by grid search:
SVC(C=np.float64(76823.03433306457), class_weight='balanced',
gamma=np.float64(0.0034189458230957995))
@@ -1116,7 +1116,7 @@
Predicting people's names on the test set
-done in 0.039s
+done in 0.042s
precision recall f1-score support
Ariel Sharon 0.75 0.69 0.72 13
@@ -1170,7 +1170,7 @@
convolutional neural networks but this family of models is outside of the scope of
the scikit-learn library. Interested readers should instead try to use pytorch or
tensorflow to implement such models.
-Total running time of the script: (0 minutes 5.567 seconds)
+Total running time of the script: (0 minutes 5.748 seconds)
diff --git a/dev/auto_examples/applications/plot_model_complexity_influence.html b/dev/auto_examples/applications/plot_model_complexity_influence.html
index 198290b55b1aa..1c045da3ce43b 100644
--- a/dev/auto_examples/applications/plot_model_complexity_influence.html
+++ b/dev/auto_examples/applications/plot_model_complexity_influence.html
@@ -1263,49 +1263,49 @@ Run the code and plot the resultsBenchmarking SGDClassifier(alpha=0.001, l1_ratio=0.25, loss='modified_huber',
n_iter_no_change=2, penalty='elasticnet', tol=0.1)
-Complexity: 4948 | Hamming Loss (Misclassification Ratio): 0.2675 | Pred. Time: 0.055522s
+Complexity: 4948 | Hamming Loss (Misclassification Ratio): 0.2675 | Pred. Time: 0.058043s
Benchmarking SGDClassifier(alpha=0.001, l1_ratio=0.5, loss='modified_huber',
n_iter_no_change=2, penalty='elasticnet', tol=0.1)
-Complexity: 1847 | Hamming Loss (Misclassification Ratio): 0.3264 | Pred. Time: 0.041783s
+Complexity: 1847 | Hamming Loss (Misclassification Ratio): 0.3264 | Pred. Time: 0.043822s
Benchmarking SGDClassifier(alpha=0.001, l1_ratio=0.75, loss='modified_huber',
n_iter_no_change=2, penalty='elasticnet', tol=0.1)
-Complexity: 997 | Hamming Loss (Misclassification Ratio): 0.3383 | Pred. Time: 0.034388s
+Complexity: 997 | Hamming Loss (Misclassification Ratio): 0.3383 | Pred. Time: 0.036736s
Benchmarking SGDClassifier(alpha=0.001, l1_ratio=0.9, loss='modified_huber',
n_iter_no_change=2, penalty='elasticnet', tol=0.1)
-Complexity: 802 | Hamming Loss (Misclassification Ratio): 0.3582 | Pred. Time: 0.031939s
+Complexity: 802 | Hamming Loss (Misclassification Ratio): 0.3582 | Pred. Time: 0.033267s
Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.05)
-Complexity: 18 | MSE: 5558.7313 | Pred. Time: 0.000191s
+Complexity: 18 | MSE: 5558.7313 | Pred. Time: 0.000184s
Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.1)
-Complexity: 36 | MSE: 5289.8022 | Pred. Time: 0.000262s
+Complexity: 36 | MSE: 5289.8022 | Pred. Time: 0.000261s
Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.2)
-Complexity: 72 | MSE: 5193.8353 | Pred. Time: 0.000419s
+Complexity: 72 | MSE: 5193.8353 | Pred. Time: 0.000406s
Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.35)
-Complexity: 124 | MSE: 5131.3279 | Pred. Time: 0.000635s
+Complexity: 124 | MSE: 5131.3279 | Pred. Time: 0.000622s
Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05)
-Complexity: 178 | MSE: 5149.0779 | Pred. Time: 0.000862s
+Complexity: 178 | MSE: 5149.0779 | Pred. Time: 0.000846s
Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=10)
-Complexity: 10 | MSE: 4066.4812 | Pred. Time: 0.000176s
+Complexity: 10 | MSE: 4066.4812 | Pred. Time: 0.000174s
Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=25)
-Complexity: 25 | MSE: 3551.1723 | Pred. Time: 0.000195s
+Complexity: 25 | MSE: 3551.1723 | Pred. Time: 0.000193s
Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=50)
-Complexity: 50 | MSE: 3445.2171 | Pred. Time: 0.000239s
+Complexity: 50 | MSE: 3445.2171 | Pred. Time: 0.000235s
Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=75)
-Complexity: 75 | MSE: 3433.0358 | Pred. Time: 0.000275s
+Complexity: 75 | MSE: 3433.0358 | Pred. Time: 0.000267s
Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2)
-Complexity: 100 | MSE: 3456.0602 | Pred. Time: 0.000309s
+Complexity: 100 | MSE: 3456.0602 | Pred. Time: 0.000303s
@@ -1319,7 +1319,7 @@ ConclusionTotal running time of the script: (0 minutes 4.643 seconds)
+Total running time of the script: (0 minutes 4.675 seconds)
diff --git a/dev/auto_examples/applications/plot_out_of_core_classification.html b/dev/auto_examples/applications/plot_out_of_core_classification.html
index dc6171c0dac41..e764f2e9fb89b 100644
--- a/dev/auto_examples/applications/plot_out_of_core_classification.html
+++ b/dev/auto_examples/applications/plot_out_of_core_classification.html
@@ -1306,46 +1306,46 @@ Main#
Test set is 878 documents (108 positive)
- SGD classifier : 962 train docs ( 132 positive) 878 test docs ( 108 positive) accuracy: 0.915 in 0.60s ( 1590 docs/s)
- Perceptron classifier : 962 train docs ( 132 positive) 878 test docs ( 108 positive) accuracy: 0.855 in 0.61s ( 1584 docs/s)
- NB Multinomial classifier : 962 train docs ( 132 positive) 878 test docs ( 108 positive) accuracy: 0.877 in 0.62s ( 1563 docs/s)
- Passive-Aggressive classifier : 962 train docs ( 132 positive) 878 test docs ( 108 positive) accuracy: 0.933 in 0.62s ( 1557 docs/s)
+ SGD classifier : 962 train docs ( 132 positive) 878 test docs ( 108 positive) accuracy: 0.915 in 0.63s ( 1533 docs/s)
+ Perceptron classifier : 962 train docs ( 132 positive) 878 test docs ( 108 positive) accuracy: 0.855 in 0.63s ( 1526 docs/s)
+ NB Multinomial classifier : 962 train docs ( 132 positive) 878 test docs ( 108 positive) accuracy: 0.877 in 0.64s ( 1506 docs/s)
+ Passive-Aggressive classifier : 962 train docs ( 132 positive) 878 test docs ( 108 positive) accuracy: 0.933 in 0.64s ( 1500 docs/s)
- SGD classifier : 3911 train docs ( 517 positive) 878 test docs ( 108 positive) accuracy: 0.938 in 1.72s ( 2274 docs/s)
- Perceptron classifier : 3911 train docs ( 517 positive) 878 test docs ( 108 positive) accuracy: 0.936 in 1.72s ( 2271 docs/s)
- NB Multinomial classifier : 3911 train docs ( 517 positive) 878 test docs ( 108 positive) accuracy: 0.885 in 1.73s ( 2261 docs/s)
- Passive-Aggressive classifier : 3911 train docs ( 517 positive) 878 test docs ( 108 positive) accuracy: 0.941 in 1.73s ( 2258 docs/s)
+ SGD classifier : 3911 train docs ( 517 positive) 878 test docs ( 108 positive) accuracy: 0.938 in 1.82s ( 2153 docs/s)
+ Perceptron classifier : 3911 train docs ( 517 positive) 878 test docs ( 108 positive) accuracy: 0.936 in 1.82s ( 2150 docs/s)
+ NB Multinomial classifier : 3911 train docs ( 517 positive) 878 test docs ( 108 positive) accuracy: 0.885 in 1.83s ( 2140 docs/s)
+ Passive-Aggressive classifier : 3911 train docs ( 517 positive) 878 test docs ( 108 positive) accuracy: 0.941 in 1.83s ( 2137 docs/s)
- SGD classifier : 6821 train docs ( 891 positive) 878 test docs ( 108 positive) accuracy: 0.952 in 2.83s ( 2411 docs/s)
- Perceptron classifier : 6821 train docs ( 891 positive) 878 test docs ( 108 positive) accuracy: 0.952 in 2.83s ( 2409 docs/s)
- NB Multinomial classifier : 6821 train docs ( 891 positive) 878 test docs ( 108 positive) accuracy: 0.900 in 2.84s ( 2403 docs/s)
- Passive-Aggressive classifier : 6821 train docs ( 891 positive) 878 test docs ( 108 positive) accuracy: 0.953 in 2.84s ( 2401 docs/s)
+ SGD classifier : 6821 train docs ( 891 positive) 878 test docs ( 108 positive) accuracy: 0.952 in 2.95s ( 2314 docs/s)
+ Perceptron classifier : 6821 train docs ( 891 positive) 878 test docs ( 108 positive) accuracy: 0.952 in 2.95s ( 2312 docs/s)
+ NB Multinomial classifier : 6821 train docs ( 891 positive) 878 test docs ( 108 positive) accuracy: 0.900 in 2.96s ( 2306 docs/s)
+ Passive-Aggressive classifier : 6821 train docs ( 891 positive) 878 test docs ( 108 positive) accuracy: 0.953 in 2.96s ( 2304 docs/s)
- SGD classifier : 9759 train docs ( 1276 positive) 878 test docs ( 108 positive) accuracy: 0.949 in 4.03s ( 2422 docs/s)
- Perceptron classifier : 9759 train docs ( 1276 positive) 878 test docs ( 108 positive) accuracy: 0.953 in 4.03s ( 2420 docs/s)
- NB Multinomial classifier : 9759 train docs ( 1276 positive) 878 test docs ( 108 positive) accuracy: 0.909 in 4.04s ( 2415 docs/s)
- Passive-Aggressive classifier : 9759 train docs ( 1276 positive) 878 test docs ( 108 positive) accuracy: 0.958 in 4.04s ( 2414 docs/s)
+ SGD classifier : 9759 train docs ( 1276 positive) 878 test docs ( 108 positive) accuracy: 0.949 in 4.09s ( 2385 docs/s)
+ Perceptron classifier : 9759 train docs ( 1276 positive) 878 test docs ( 108 positive) accuracy: 0.953 in 4.09s ( 2383 docs/s)
+ NB Multinomial classifier : 9759 train docs ( 1276 positive) 878 test docs ( 108 positive) accuracy: 0.909 in 4.10s ( 2379 docs/s)
+ Passive-Aggressive classifier : 9759 train docs ( 1276 positive) 878 test docs ( 108 positive) accuracy: 0.958 in 4.10s ( 2377 docs/s)
- SGD classifier : 11680 train docs ( 1499 positive) 878 test docs ( 108 positive) accuracy: 0.944 in 5.01s ( 2331 docs/s)
- Perceptron classifier : 11680 train docs ( 1499 positive) 878 test docs ( 108 positive) accuracy: 0.956 in 5.01s ( 2330 docs/s)
- NB Multinomial classifier : 11680 train docs ( 1499 positive) 878 test docs ( 108 positive) accuracy: 0.915 in 5.02s ( 2327 docs/s)
- Passive-Aggressive classifier : 11680 train docs ( 1499 positive) 878 test docs ( 108 positive) accuracy: 0.950 in 5.02s ( 2326 docs/s)
+ SGD classifier : 11680 train docs ( 1499 positive) 878 test docs ( 108 positive) accuracy: 0.944 in 5.05s ( 2312 docs/s)
+ Perceptron classifier : 11680 train docs ( 1499 positive) 878 test docs ( 108 positive) accuracy: 0.956 in 5.05s ( 2311 docs/s)
+ NB Multinomial classifier : 11680 train docs ( 1499 positive) 878 test docs ( 108 positive) accuracy: 0.915 in 5.06s ( 2307 docs/s)
+ Passive-Aggressive classifier : 11680 train docs ( 1499 positive) 878 test docs ( 108 positive) accuracy: 0.950 in 5.06s ( 2306 docs/s)
- SGD classifier : 14625 train docs ( 1865 positive) 878 test docs ( 108 positive) accuracy: 0.965 in 6.16s ( 2375 docs/s)
- Perceptron classifier : 14625 train docs ( 1865 positive) 878 test docs ( 108 positive) accuracy: 0.903 in 6.16s ( 2374 docs/s)
- NB Multinomial classifier : 14625 train docs ( 1865 positive) 878 test docs ( 108 positive) accuracy: 0.924 in 6.17s ( 2371 docs/s)
- Passive-Aggressive classifier : 14625 train docs ( 1865 positive) 878 test docs ( 108 positive) accuracy: 0.957 in 6.17s ( 2370 docs/s)
+ SGD classifier : 14625 train docs ( 1865 positive) 878 test docs ( 108 positive) accuracy: 0.965 in 6.20s ( 2359 docs/s)
+ Perceptron classifier : 14625 train docs ( 1865 positive) 878 test docs ( 108 positive) accuracy: 0.903 in 6.20s ( 2358 docs/s)
+ NB Multinomial classifier : 14625 train docs ( 1865 positive) 878 test docs ( 108 positive) accuracy: 0.924 in 6.21s ( 2355 docs/s)
+ Passive-Aggressive classifier : 14625 train docs ( 1865 positive) 878 test docs ( 108 positive) accuracy: 0.957 in 6.21s ( 2354 docs/s)
- SGD classifier : 17360 train docs ( 2179 positive) 878 test docs ( 108 positive) accuracy: 0.957 in 7.21s ( 2409 docs/s)
- Perceptron classifier : 17360 train docs ( 2179 positive) 878 test docs ( 108 positive) accuracy: 0.933 in 7.21s ( 2408 docs/s)
- NB Multinomial classifier : 17360 train docs ( 2179 positive) 878 test docs ( 108 positive) accuracy: 0.932 in 7.22s ( 2405 docs/s)
- Passive-Aggressive classifier : 17360 train docs ( 2179 positive) 878 test docs ( 108 positive) accuracy: 0.952 in 7.22s ( 2405 docs/s)
+ SGD classifier : 17360 train docs ( 2179 positive) 878 test docs ( 108 positive) accuracy: 0.957 in 7.23s ( 2401 docs/s)
+ Perceptron classifier : 17360 train docs ( 2179 positive) 878 test docs ( 108 positive) accuracy: 0.933 in 7.23s ( 2400 docs/s)
+ NB Multinomial classifier : 17360 train docs ( 2179 positive) 878 test docs ( 108 positive) accuracy: 0.932 in 7.24s ( 2398 docs/s)
+ Passive-Aggressive classifier : 17360 train docs ( 2179 positive) 878 test docs ( 108 positive) accuracy: 0.952 in 7.24s ( 2397 docs/s)
@@ -1459,7 +1459,7 @@ Plot results
Total running time of the script: (0 minutes 8.169 seconds)
+Total running time of the script: (0 minutes 8.194 seconds)
diff --git a/dev/auto_examples/applications/plot_outlier_detection_wine.html b/dev/auto_examples/applications/plot_outlier_detection_wine.html
index f10f25e4a57fe..e2cf2705b2a66 100644
--- a/dev/auto_examples/applications/plot_outlier_detection_wine.html
+++ b/dev/auto_examples/applications/plot_outlier_detection_wine.html
@@ -1123,7 +1123,7 @@ Second exampleplt.show()
-
Total running time of the script: (0 minutes 0.357 seconds)
+Total running time of the script: (0 minutes 0.359 seconds)
diff --git a/dev/auto_examples/applications/plot_prediction_latency.html b/dev/auto_examples/applications/plot_prediction_latency.html
index 7ead905b605c4..e78aa9cafa4f5 100644
--- a/dev/auto_examples/applications/plot_prediction_latency.html
+++ b/dev/auto_examples/applications/plot_prediction_latency.html
@@ -1344,7 +1344,7 @@ Benchmark throughputplot_benchmark_throughput(throughputs, configuration)
-
Total running time of the script: (0 minutes 17.435 seconds)
+Total running time of the script: (0 minutes 17.716 seconds)
diff --git a/dev/auto_examples/applications/plot_species_distribution_modeling.html b/dev/auto_examples/applications/plot_species_distribution_modeling.html
index 5576ed6a04c62..7f787e48f9488 100644
--- a/dev/auto_examples/applications/plot_species_distribution_modeling.html
+++ b/dev/auto_examples/applications/plot_species_distribution_modeling.html
@@ -1035,7 +1035,7 @@ References
@@ -1251,7 +1251,7 @@ Referencesplt.show()
-Total running time of the script: (0 minutes 6.748 seconds)
+Total running time of the script: (0 minutes 6.175 seconds)
diff --git a/dev/auto_examples/applications/plot_stock_market.html b/dev/auto_examples/applications/plot_stock_market.html
index 29c51f220c56b..0460da990d611 100644
--- a/dev/auto_examples/applications/plot_stock_market.html
+++ b/dev/auto_examples/applications/plot_stock_market.html
@@ -1967,7 +1967,7 @@ Visualizationplt.show()
-
Total running time of the script: (0 minutes 4.877 seconds)
+Total running time of the script: (0 minutes 5.230 seconds)
diff --git a/dev/auto_examples/applications/plot_time_series_lagged_features.html b/dev/auto_examples/applications/plot_time_series_lagged_features.html
index 6c2bdf563d3d0..c13227afe13e5 100644
--- a/dev/auto_examples/applications/plot_time_series_lagged_features.html
+++ b/dev/auto_examples/applications/plot_time_series_lagged_features.html
@@ -1075,7 +1075,7 @@ Analyzing the Bike Sharing Demand datasetseason count cat u32 "1" 4242 "3" 4232 "2" 4409 "0" 4496
+shape: (4, 2)season count cat u32 "1" 4242 "0" 4496 "2" 4409 "3" 4232
@@ -1329,7 +1329,7 @@ Modeling predictive uncertainty via quantile regression
-shape: (6, 8)
loss fit_time MAPE RMSE MAE pinball_loss_05 pinball_loss_50 pinball_loss_95 str str str str str str str str "squared_error" "0.32 ± 0.01 s" "0.36 ± 0.07" "62.3 ± 3.5" "39.1 ± 2.3" "17.7 ± 1.3" "19.5 ± 1.1" "21.4 ± 2.4" "poisson" "0.34 ± 0.01 s" "0.32 ± 0.07" "64.2 ± 4.0" "39.3 ± 2.8" "16.7 ± 1.5" "19.7 ± 1.4" "22.6 ± 3.0" "absolute_error" "0.43 ± 0.01 s" "0.32 ± 0.06" "64.6 ± 3.8" "39.9 ± 3.2" "17.1 ± 1.1" "19.9 ± 1.6" "22.7 ± 3.1" "quantile 5" "0.54 ± 0.00 s" "0.41 ± 0.01" "145.6 ± 20.9" "92.5 ± 16.2" "5.9 ± 0.9" "46.2 ± 8.1" "86.6 ± 15.3" "quantile 50" "0.58 ± 0.01 s" "0.32 ± 0.06" "64.6 ± 3.8" "39.9 ± 3.2" "17.1 ± 1.1" "19.9 ± 1.6" "22.7 ± 3.1" "quantile 95" "0.55 ± 0.00 s" "1.07 ± 0.27" "99.6 ± 8.7" "72.0 ± 6.1" "62.9 ± 7.4" "36.0 ± 3.1" "9.1 ± 1.3"
+shape: (6, 8)loss fit_time MAPE RMSE MAE pinball_loss_05 pinball_loss_50 pinball_loss_95 str str str str str str str str "squared_error" "0.31 ± 0.01 s" "0.36 ± 0.07" "62.3 ± 3.5" "39.1 ± 2.3" "17.7 ± 1.3" "19.5 ± 1.1" "21.4 ± 2.4" "poisson" "0.34 ± 0.01 s" "0.32 ± 0.07" "64.2 ± 4.0" "39.3 ± 2.8" "16.7 ± 1.5" "19.7 ± 1.4" "22.6 ± 3.0" "absolute_error" "0.43 ± 0.01 s" "0.32 ± 0.06" "64.6 ± 3.8" "39.9 ± 3.2" "17.1 ± 1.1" "19.9 ± 1.6" "22.7 ± 3.1" "quantile 5" "0.55 ± 0.01 s" "0.41 ± 0.01" "145.6 ± 20.9" "92.5 ± 16.2" "5.9 ± 0.9" "46.2 ± 8.1" "86.6 ± 15.3" "quantile 50" "0.58 ± 0.01 s" "0.32 ± 0.06" "64.6 ± 3.8" "39.9 ± 3.2" "17.1 ± 1.1" "19.9 ± 1.6" "22.7 ± 3.1" "quantile 95" "0.57 ± 0.01 s" "1.07 ± 0.27" "99.6 ± 8.7" "72.0 ± 6.1" "62.9 ± 7.4" "36.0 ± 3.1" "9.1 ± 1.3"
Let us take a look at the losses that minimise each metric.
@@ -1504,7 +1504,7 @@ Conclusionsktime
can be used to extend scikit-learn estimators by making use of recursive time
series forecasting, that enables dynamic predictions of future values.
-
Total running time of the script: (0 minutes 9.022 seconds)
+Total running time of the script: (0 minutes 9.106 seconds)
diff --git a/dev/auto_examples/applications/plot_tomography_l1_reconstruction.html b/dev/auto_examples/applications/plot_tomography_l1_reconstruction.html
index cc4e9500dceeb..812e1211e8c1e 100644
--- a/dev/auto_examples/applications/plot_tomography_l1_reconstruction.html
+++ b/dev/auto_examples/applications/plot_tomography_l1_reconstruction.html
@@ -1130,7 +1130,7 @@
plt.show()
-Total running time of the script: (0 minutes 1.416 seconds)
+Total running time of the script: (0 minutes 1.418 seconds)
-Total running time of the script: (0 minutes 10.682 seconds)
+Total running time of the script: (0 minutes 10.873 seconds)
diff --git a/dev/auto_examples/bicluster/plot_bicluster_newsgroups.html b/dev/auto_examples/bicluster/plot_bicluster_newsgroups.html
index 7af20cc848774..aa8d80660cd6a 100644
--- a/dev/auto_examples/bicluster/plot_bicluster_newsgroups.html
+++ b/dev/auto_examples/bicluster/plot_bicluster_newsgroups.html
@@ -1010,9 +1010,9 @@
achieve a better V-measure than clusters found by MiniBatchKMeans.
Vectorizing...
Coclustering...
-Done in 1.13s. V-measure: 0.4415
+Done in 1.20s. V-measure: 0.4415
MiniBatchKMeans...
-Done in 2.15s. V-measure: 0.3015
+Done in 2.43s. V-measure: 0.3015
Best biclusters:
----------------
@@ -1180,7 +1180,7 @@
print(f"words : {', '.join(important_words)}\n")
-Total running time of the script: (0 minutes 5.820 seconds)
+Total running time of the script: (0 minutes 6.393 seconds)
diff --git a/dev/auto_examples/bicluster/plot_spectral_biclustering.html b/dev/auto_examples/bicluster/plot_spectral_biclustering.html
index a277fb97f8752..59c4a6db4473f 100644
--- a/dev/auto_examples/bicluster/plot_spectral_biclustering.html
+++ b/dev/auto_examples/bicluster/plot_spectral_biclustering.html
@@ -1110,7 +1110,7 @@ Plotting results
The outer product of the row and column label vectors shows a representation
of the checkerboard structure, where different combinations of row and column
labels are represented by different shades of blue.
-Total running time of the script: (0 minutes 0.488 seconds)
+Total running time of the script: (0 minutes 0.532 seconds)
diff --git a/dev/auto_examples/bicluster/plot_spectral_coclustering.html b/dev/auto_examples/bicluster/plot_spectral_coclustering.html
index df8a44212d611..45605a8346fd7 100644
--- a/dev/auto_examples/bicluster/plot_spectral_coclustering.html
+++ b/dev/auto_examples/bicluster/plot_spectral_coclustering.html
@@ -1054,7 +1054,7 @@
plt.show()
-Total running time of the script: (0 minutes 0.311 seconds)
+Total running time of the script: (0 minutes 0.320 seconds)
diff --git a/dev/auto_examples/calibration/plot_calibration.html b/dev/auto_examples/calibration/plot_calibration.html
index a1dc225081d22..ad1cef3afedce 100644
--- a/dev/auto_examples/calibration/plot_calibration.html
+++ b/dev/auto_examples/calibration/plot_calibration.html
@@ -1139,7 +1139,7 @@ Plot data and the predicted probabilities
Total running time of the script: (0 minutes 0.348 seconds)
+Total running time of the script: (0 minutes 0.381 seconds)
diff --git a/dev/auto_examples/calibration/plot_calibration_curve.html b/dev/auto_examples/calibration/plot_calibration_curve.html
index 28d90ab7fca07..13311dfa4cda1 100644
--- a/dev/auto_examples/calibration/plot_calibration_curve.html
+++ b/dev/auto_examples/calibration/plot_calibration_curve.html
@@ -1470,7 +1470,7 @@ ReferencesTotal running time of the script: (0 minutes 2.243 seconds)
+Total running time of the script: (0 minutes 2.738 seconds)
diff --git a/dev/auto_examples/calibration/plot_calibration_multiclass.html b/dev/auto_examples/calibration/plot_calibration_multiclass.html
index 0c5a59f1fffd0..848e09e8e879d 100644
--- a/dev/auto_examples/calibration/plot_calibration_multiclass.html
+++ b/dev/auto_examples/calibration/plot_calibration_multiclass.html
@@ -2795,7 +2795,7 @@ Compare probabilities
All in all, the One-vs-Rest multiclass-calibration strategy implemented in
CalibratedClassifierCV should not be trusted blindly.
-Total running time of the script: (0 minutes 1.335 seconds)
+Total running time of the script: (0 minutes 1.419 seconds)
diff --git a/dev/auto_examples/calibration/plot_compare_calibration.html b/dev/auto_examples/calibration/plot_compare_calibration.html
index 25a94b68bbf00..bee9b20104bd0 100644
--- a/dev/auto_examples/calibration/plot_compare_calibration.html
+++ b/dev/auto_examples/calibration/plot_compare_calibration.html
@@ -1256,7 +1256,7 @@ ReferencesTotal running time of the script: (0 minutes 2.872 seconds)
+Total running time of the script: (0 minutes 3.051 seconds)
diff --git a/dev/auto_examples/classification/plot_classification_probability.html b/dev/auto_examples/classification/plot_classification_probability.html
index 44ca4bf2f0f0e..9ca9f504e8ebe 100644
--- a/dev/auto_examples/classification/plot_classification_probability.html
+++ b/dev/auto_examples/classification/plot_classification_probability.html
@@ -1295,7 +1295,7 @@ Analysis
various non-linear models. However, for this particular dataset, using the
raw features is enough to train a competitive model. This would not
necessarily the case for other datasets.
-Total running time of the script: (0 minutes 2.489 seconds)
+Total running time of the script: (0 minutes 2.578 seconds)
diff --git a/dev/auto_examples/classification/plot_classifier_comparison.html b/dev/auto_examples/classification/plot_classifier_comparison.html
index 14da31a9b1b47..07a7caba3f441 100644
--- a/dev/auto_examples/classification/plot_classifier_comparison.html
+++ b/dev/auto_examples/classification/plot_classifier_comparison.html
@@ -1143,7 +1143,7 @@
plt.show()
-Total running time of the script: (0 minutes 2.001 seconds)
+Total running time of the script: (0 minutes 2.242 seconds)
diff --git a/dev/auto_examples/classification/plot_digits_classification.html b/dev/auto_examples/classification/plot_digits_classification.html
index e953c538b3827..0faee3bdd33a6 100644
--- a/dev/auto_examples/classification/plot_digits_classification.html
+++ b/dev/auto_examples/classification/plot_digits_classification.html
@@ -1157,7 +1157,7 @@ ClassificationTotal running time of the script: (0 minutes 0.409 seconds)
+Total running time of the script: (0 minutes 0.424 seconds)
diff --git a/dev/auto_examples/classification/plot_lda.html b/dev/auto_examples/classification/plot_lda.html
index d7bad4c95d292..3b3f6608881a6 100644
--- a/dev/auto_examples/classification/plot_lda.html
+++ b/dev/auto_examples/classification/plot_lda.html
@@ -11,10 +11,10 @@
-
+
-
+
Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification — scikit-learn 1.8.dev0 documentation
@@ -1095,7 +1095,7 @@
plt.show()
-Total running time of the script: (0 minutes 7.457 seconds)
+Total running time of the script: (0 minutes 7.955 seconds)
diff --git a/dev/auto_examples/classification/plot_lda_qda.html b/dev/auto_examples/classification/plot_lda_qda.html
index 711c49daaf5ee..8f41a5848b84a 100644
--- a/dev/auto_examples/classification/plot_lda_qda.html
+++ b/dev/auto_examples/classification/plot_lda_qda.html
@@ -1210,7 +1210,7 @@ Comparison of LDA and QDATotal running time of the script: (0 minutes 0.389 seconds)
+Total running time of the script: (0 minutes 0.420 seconds)
diff --git a/dev/auto_examples/cluster/plot_adjusted_for_chance_measures.html b/dev/auto_examples/cluster/plot_adjusted_for_chance_measures.html
index 2f7bb72281695..a8873f2c01101 100644
--- a/dev/auto_examples/cluster/plot_adjusted_for_chance_measures.html
+++ b/dev/auto_examples/cluster/plot_adjusted_for_chance_measures.html
@@ -2090,7 +2090,7 @@ Second experiment: varying number of classes and clustersTotal running time of the script: (0 minutes 1.050 seconds)
+Total running time of the script: (0 minutes 1.077 seconds)
diff --git a/dev/auto_examples/cluster/plot_affinity_propagation.html b/dev/auto_examples/cluster/plot_affinity_propagation.html
index d0ed065466f59..2fd48ae782056 100644
--- a/dev/auto_examples/cluster/plot_affinity_propagation.html
+++ b/dev/auto_examples/cluster/plot_affinity_propagation.html
@@ -1077,7 +1077,7 @@ Plot resultplt.show()
-
Total running time of the script: (0 minutes 0.293 seconds)
+Total running time of the script: (0 minutes 0.355 seconds)
diff --git a/dev/auto_examples/cluster/plot_agglomerative_clustering.html b/dev/auto_examples/cluster/plot_agglomerative_clustering.html
index 31a20c42d3589..7dda45e5be537 100644
--- a/dev/auto_examples/cluster/plot_agglomerative_clustering.html
+++ b/dev/auto_examples/cluster/plot_agglomerative_clustering.html
@@ -1014,8 +1014,8 @@
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
@@ -1077,7 +1077,7 @@
plt.show()
-Total running time of the script: (0 minutes 1.812 seconds)
+Total running time of the script: (0 minutes 1.895 seconds)
diff --git a/dev/auto_examples/cluster/plot_agglomerative_clustering_metrics.html b/dev/auto_examples/cluster/plot_agglomerative_clustering_metrics.html
index a6a32e89421a7..7a7e6eff6b44b 100644
--- a/dev/auto_examples/cluster/plot_agglomerative_clustering_metrics.html
+++ b/dev/auto_examples/cluster/plot_agglomerative_clustering_metrics.html
@@ -1140,7 +1140,7 @@
plt.show()
-Total running time of the script: (0 minutes 1.047 seconds)
+Total running time of the script: (0 minutes 1.061 seconds)
diff --git a/dev/auto_examples/cluster/plot_agglomerative_dendrogram.html b/dev/auto_examples/cluster/plot_agglomerative_dendrogram.html
index e2c726851e753..814b8943e16b3 100644
--- a/dev/auto_examples/cluster/plot_agglomerative_dendrogram.html
+++ b/dev/auto_examples/cluster/plot_agglomerative_dendrogram.html
@@ -1041,7 +1041,7 @@
plt.show()
-Total running time of the script: (0 minutes 0.085 seconds)
+Total running time of the script: (0 minutes 0.098 seconds)
diff --git a/dev/auto_examples/cluster/plot_birch_vs_minibatchkmeans.html b/dev/auto_examples/cluster/plot_birch_vs_minibatchkmeans.html
index ce222926732ed..bfd509c7f8a14 100644
--- a/dev/auto_examples/cluster/plot_birch_vs_minibatchkmeans.html
+++ b/dev/auto_examples/cluster/plot_birch_vs_minibatchkmeans.html
@@ -1009,9 +1009,9 @@
158 clusters to 100 clusters.
BIRCH without global clustering as the final step took 0.46 seconds
n_clusters : 158
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n_clusters : 100
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+Time taken to run MiniBatchKMeans 0.22 seconds
@@ -1104,7 +1104,7 @@
plt.show()
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diff --git a/dev/auto_examples/cluster/plot_bisect_kmeans.html b/dev/auto_examples/cluster/plot_bisect_kmeans.html
index 2ec09cd468340..1172ad952ea1d 100644
--- a/dev/auto_examples/cluster/plot_bisect_kmeans.html
+++ b/dev/auto_examples/cluster/plot_bisect_kmeans.html
@@ -1055,7 +1055,7 @@
plt.show()
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diff --git a/dev/auto_examples/cluster/plot_cluster_comparison.html b/dev/auto_examples/cluster/plot_cluster_comparison.html
index 1aafbec1e0ab4..c8fd7ad6cd758 100644
--- a/dev/auto_examples/cluster/plot_cluster_comparison.html
+++ b/dev/auto_examples/cluster/plot_cluster_comparison.html
@@ -1273,7 +1273,7 @@
plt.show()
-Total running time of the script: (0 minutes 5.428 seconds)
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-
-Spectral clustering: kmeans, 1.61s
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diff --git a/dev/auto_examples/cluster/plot_coin_ward_segmentation.html b/dev/auto_examples/cluster/plot_coin_ward_segmentation.html
index 3f3621d9b5df4..dbd4733e4f0cb 100644
--- a/dev/auto_examples/cluster/plot_coin_ward_segmentation.html
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@@ -1056,7 +1056,7 @@ Compute clusteringCompute structured hierarchical clustering...
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Number of pixels: 4697
Number of clusters: 27
@@ -1082,7 +1082,7 @@ Plot the results on an imageplt.show()
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Total running time of the script: (0 minutes 0.323 seconds)
+Total running time of the script: (0 minutes 0.333 seconds)
-
Total running time of the script: (0 minutes 0.166 seconds)
+Total running time of the script: (0 minutes 0.172 seconds)
diff --git a/dev/auto_examples/cluster/plot_dict_face_patches.html b/dev/auto_examples/cluster/plot_dict_face_patches.html
index 3ce853f03f78c..78a1e32480476 100644
--- a/dev/auto_examples/cluster/plot_dict_face_patches.html
+++ b/dev/auto_examples/cluster/plot_dict_face_patches.html
@@ -1088,7 +1088,7 @@ Learn the dictionary of imagesplt.show()
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Total running time of the script: (0 minutes 1.937 seconds)
+Total running time of the script: (0 minutes 2.151 seconds)
diff --git a/dev/auto_examples/cluster/plot_digits_agglomeration.html b/dev/auto_examples/cluster/plot_digits_agglomeration.html
index 3efac244b05c6..55c366ea26df9 100644
--- a/dev/auto_examples/cluster/plot_digits_agglomeration.html
+++ b/dev/auto_examples/cluster/plot_digits_agglomeration.html
@@ -11,10 +11,10 @@
-
+
-
+
Feature agglomeration — scikit-learn 1.8.dev0 documentation
@@ -1046,7 +1046,7 @@
plt.show()
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index d31a9a80aba56..b3a1951a78285 100644
--- a/dev/auto_examples/cluster/plot_digits_linkage.html
+++ b/dev/auto_examples/cluster/plot_digits_linkage.html
@@ -1018,10 +1018,10 @@
Computing embedding
Done.
-ward : 0.05s
-average : 0.05s
-complete : 0.05s
-single : 0.02s
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@@ -1088,7 +1088,7 @@
plt.show()
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diff --git a/dev/auto_examples/cluster/plot_face_compress.html b/dev/auto_examples/cluster/plot_face_compress.html
index 018aca2744b26..9b34270c17d3d 100644
--- a/dev/auto_examples/cluster/plot_face_compress.html
+++ b/dev/auto_examples/cluster/plot_face_compress.html
@@ -1184,7 +1184,7 @@ Memory footprintTotal running time of the script: (0 minutes 2.717 seconds)
+Total running time of the script: (0 minutes 3.102 seconds)
Anova univariate feature selection followed by BayesianRidge
@@ -1146,7 +1146,7 @@
shutil.rmtree(cachedir, ignore_errors=True)
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Total running time of the script: (0 minutes 14.763 seconds)
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diff --git a/dev/auto_examples/cluster/plot_inductive_clustering.html b/dev/auto_examples/cluster/plot_inductive_clustering.html
index cee99f49e2bc6..cdc27f6727a71 100644
--- a/dev/auto_examples/cluster/plot_inductive_clustering.html
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@@ -1117,7 +1117,7 @@
plt.show()
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index 33866b5fa6d3b..a0ac04459cd2d 100644
--- a/dev/auto_examples/cluster/plot_kmeans_assumptions.html
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@@ -1156,7 +1156,7 @@ Final remarksTotal running time of the script: (0 minutes 1.104 seconds)
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index f3838acb79600..d1d12db7ff6ea 100644
--- a/dev/auto_examples/cluster/plot_kmeans_digits.html
+++ b/dev/auto_examples/cluster/plot_kmeans_digits.html
@@ -1153,8 +1153,8 @@ Run the benchmark__________________________________________________________________________________
init time inertia homo compl v-meas ARI AMI silhouette
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PCA-based 0.013s 69513 0.600 0.647 0.622 0.468 0.618 0.162
__________________________________________________________________________________
@@ -1219,7 +1219,7 @@ Visualize the results on PCA-reduced dataplt.show()
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Total running time of the script: (0 minutes 5.421 seconds)
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diff --git a/dev/auto_examples/cluster/plot_kmeans_plusplus.html b/dev/auto_examples/cluster/plot_kmeans_plusplus.html
index c59dfe47d62b4..7d39b06338eb6 100644
--- a/dev/auto_examples/cluster/plot_kmeans_plusplus.html
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@@ -1032,7 +1032,7 @@
plt.show()
-Total running time of the script: (0 minutes 0.059 seconds)
+Total running time of the script: (0 minutes 0.058 seconds)
diff --git a/dev/auto_examples/cluster/plot_kmeans_silhouette_analysis.html b/dev/auto_examples/cluster/plot_kmeans_silhouette_analysis.html
index 7a86611d8eae4..85c0a89338047 100644
--- a/dev/auto_examples/cluster/plot_kmeans_silhouette_analysis.html
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@@ -1164,7 +1164,7 @@
plt.show()
-Total running time of the script: (0 minutes 1.073 seconds)
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diff --git a/dev/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.html b/dev/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.html
index 74e6971c394cc..e0206b70f00bf 100644
--- a/dev/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.html
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@@ -1130,7 +1130,7 @@
plt.show()
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diff --git a/dev/auto_examples/cluster/plot_linkage_comparison.html b/dev/auto_examples/cluster/plot_linkage_comparison.html
index 57ac23f14139a..69fa09a2774ab 100644
--- a/dev/auto_examples/cluster/plot_linkage_comparison.html
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@@ -1163,7 +1163,7 @@
plt.show()
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Total running time of the script: (0 minutes 1.797 seconds)
+Total running time of the script: (0 minutes 1.937 seconds)
diff --git a/dev/auto_examples/cluster/plot_mean_shift.html b/dev/auto_examples/cluster/plot_mean_shift.html
index ce036e861eeb8..f0fce85b14598 100644
--- a/dev/auto_examples/cluster/plot_mean_shift.html
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@@ -1060,7 +1060,7 @@ Plot resultplt.show()
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Total running time of the script: (0 minutes 0.409 seconds)
+Total running time of the script: (0 minutes 0.398 seconds)
diff --git a/dev/auto_examples/cluster/plot_mini_batch_kmeans.html b/dev/auto_examples/cluster/plot_mini_batch_kmeans.html
index cb0fbb611bd27..61d3ac76bcd9e 100644
--- a/dev/auto_examples/cluster/plot_mini_batch_kmeans.html
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@@ -1131,7 +1131,7 @@ Plotting the resultsplt.show()
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Total running time of the script: (0 minutes 0.171 seconds)
+Total running time of the script: (0 minutes 0.174 seconds)
-Total running time of the script: (0 minutes 1.530 seconds)
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diff --git a/dev/auto_examples/cluster/plot_segmentation_toy.html b/dev/auto_examples/cluster/plot_segmentation_toy.html
index adad8a70a4009..b5df4f33d1cad 100644
--- a/dev/auto_examples/cluster/plot_segmentation_toy.html
+++ b/dev/auto_examples/cluster/plot_segmentation_toy.html
@@ -1103,7 +1103,7 @@ Plotting two circlesplt.show()
-
Total running time of the script: (0 minutes 0.421 seconds)
+Total running time of the script: (0 minutes 0.486 seconds)
diff --git a/dev/auto_examples/cluster/plot_ward_structured_vs_unstructured.html b/dev/auto_examples/cluster/plot_ward_structured_vs_unstructured.html
index 6fb99f375e7a6..87587dbefc8c3 100644
--- a/dev/auto_examples/cluster/plot_ward_structured_vs_unstructured.html
+++ b/dev/auto_examples/cluster/plot_ward_structured_vs_unstructured.html
@@ -1094,7 +1094,7 @@ Compute clusteringCompute structured hierarchical clustering...
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Number of points: 1500
@@ -1119,7 +1119,7 @@ Plot result#<
plt.show()
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Total running time of the script: (0 minutes 0.394 seconds)
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diff --git a/dev/auto_examples/compose/plot_column_transformer.html b/dev/auto_examples/compose/plot_column_transformer.html
index c23f29438feff..95c603bc2a565 100644
--- a/dev/auto_examples/compose/plot_column_transformer.html
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@@ -1188,7 +1188,7 @@ Classification pipeline
[Pipeline] ....... (step 1 of 3) Processing subjectbody, total= 0.0s
-[Pipeline] ............. (step 2 of 3) Processing union, total= 0.5s
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[Pipeline] ............... (step 3 of 3) Processing svc, total= 0.0s
Classification report:
@@ -1202,7 +1202,7 @@ Classification pipeline
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@@ -1937,7 +1937,7 @@
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>
score_func
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@@ -2705,14 +2705,14 @@
SimpleImputer(strategy='median')),
('scaler',
StandardScaler())]),
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb818bdaaa0>),
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a64abda50>),
('cat',
Pipeline(steps=[('encoder',
OneHotEncoder(handle_unknown='ignore')),
('selector',
SelectPercentile(percentile=50,
- score_func=<function chi2 at 0x7fb81b1ab7f0>))]),
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb818bdbb80>)])),
+ score_func=<function chi2 at 0x7f7a8437b7f0>))]),
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a64abc970>)])),
('classifier', LogisticRegression())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
@@ -2853,7 +2853,7 @@
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Parameters
@@ -2963,7 +2963,7 @@
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Parameters
@@ -3066,7 +3066,7 @@
this.parentElement.nextElementSibling)"
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score_func
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@@ -3808,13 +3808,13 @@
SimpleImputer(strategy='median')),
('scaler',
StandardScaler())]),
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+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a64abda50>),
('cat',
Pipeline(steps=[('encoder',
OneHotEncoder(handle_unknown='ignore')),
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- score_func=<function chi2 at 0x7fb81b1ab7f0>))]),
- <sklearn.compose._column_transformer.make_column_selector object at 0x7fb818bdbb80>)])),
+ score_func=<function chi2 at 0x7f7a8437b7f0>))]),
+ <sklearn.compose._column_transformer.make_column_selector object at 0x7f7a64abc970>)])),
('classifier',
LogisticRegression())]),
param_distributions={'classifier__C': [0.1, 1.0, 10, 100],
@@ -4044,7 +4044,7 @@
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diff --git a/dev/auto_examples/compose/plot_column_transformer_mixed_types.html b/dev/auto_examples/compose/plot_column_transformer_mixed_types.html
index 221585b395ae2..95b6e019a2960 100644
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@@ -1581,7 +1581,7 @@
OneHotEncoder(handle_unknown='ignore')),
('selector',
SelectPercentile(percentile=50,
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+ score_func=<function chi2 at 0x7f7a8437b7f0>))]),
['embarked', 'sex',
'pclass'])])),
('classifier', LogisticRegression())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.