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<section id="computation-times">
<span id="sphx-glr-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">¶</a></h1>
<p><strong>23:23.043</strong> total execution time for 291 files <strong>from all galleries</strong>:</p>
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<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/release_highlights/plot_release_highlights_0_24_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-24-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.24</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_0_24_0.py</span></code>)</p></td>
<td><p>01:14.695</p></td>
<td><p>0.0</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/miscellaneous/plot_outlier_detection_bench.html#sphx-glr-auto-examples-miscellaneous-plot-outlier-detection-bench-py"><span class="std std-ref">Evaluation of outlier detection estimators</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_outlier_detection_bench.py</span></code>)</p></td>
<td><p>01:00.523</p></td>
<td><p>0.0</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_sgd_early_stopping.html#sphx-glr-auto-examples-linear-model-plot-sgd-early-stopping-py"><span class="std std-ref">Early stopping of Stochastic Gradient Descent</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_sgd_early_stopping.py</span></code>)</p></td>
<td><p>00:59.197</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.html#sphx-glr-auto-examples-ensemble-plot-forest-hist-grad-boosting-comparison-py"><span class="std std-ref">Comparing Random Forests and Histogram Gradient Boosting models</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_forest_hist_grad_boosting_comparison.py</span></code>)</p></td>
<td><p>00:54.562</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/gaussian_process/plot_gpr_co2.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-co2-py"><span class="std std-ref">Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_gpr_co2.py</span></code>)</p></td>
<td><p>00:45.839</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/release_highlights/plot_release_highlights_1_2_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-2-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.2</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_1_2_0.py</span></code>)</p></td>
<td><p>00:44.704</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/feature_selection/plot_select_from_model_diabetes.html#sphx-glr-auto-examples-feature-selection-plot-select-from-model-diabetes-py"><span class="std std-ref">Model-based and sequential feature selection</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/feature_selection/plot_select_from_model_diabetes.py</span></code>)</p></td>
<td><p>00:44.292</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/compose/plot_compare_reduction.html#sphx-glr-auto-examples-compose-plot-compare-reduction-py"><span class="std std-ref">Selecting dimensionality reduction with Pipeline and GridSearchCV</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/compose/plot_compare_reduction.py</span></code>)</p></td>
<td><p>00:43.483</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/preprocessing/plot_target_encoder.html#sphx-glr-auto-examples-preprocessing-plot-target-encoder-py"><span class="std std-ref">Comparing Target Encoder with Other Encoders</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/preprocessing/plot_target_encoder.py</span></code>)</p></td>
<td><p>00:32.789</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_stack_predictors.html#sphx-glr-auto-examples-ensemble-plot-stack-predictors-py"><span class="std std-ref">Combine predictors using stacking</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_stack_predictors.py</span></code>)</p></td>
<td><p>00:29.296</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_grid_search_text_feature_extraction.html#sphx-glr-auto-examples-model-selection-plot-grid-search-text-feature-extraction-py"><span class="std std-ref">Sample pipeline for text feature extraction and evaluation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_grid_search_text_feature_extraction.py</span></code>)</p></td>
<td><p>00:28.407</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/decomposition/plot_image_denoising.html#sphx-glr-auto-examples-decomposition-plot-image-denoising-py"><span class="std std-ref">Image denoising using dictionary learning</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/decomposition/plot_image_denoising.py</span></code>)</p></td>
<td><p>00:27.863</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/release_highlights/plot_release_highlights_1_4_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-4-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.4</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_1_4_0.py</span></code>)</p></td>
<td><p>00:27.320</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/svm/plot_svm_scale_c.html#sphx-glr-auto-examples-svm-plot-svm-scale-c-py"><span class="std std-ref">Scaling the regularization parameter for SVCs</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_svm_scale_c.py</span></code>)</p></td>
<td><p>00:24.277</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_learning_curve.html#sphx-glr-auto-examples-model-selection-plot-learning-curve-py"><span class="std std-ref">Plotting Learning Curves and Checking Models’ Scalability</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_learning_curve.py</span></code>)</p></td>
<td><p>00:23.725</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_poisson_regression_non_normal_loss.html#sphx-glr-auto-examples-linear-model-plot-poisson-regression-non-normal-loss-py"><span class="std std-ref">Poisson regression and non-normal loss</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_poisson_regression_non_normal_loss.py</span></code>)</p></td>
<td><p>00:22.955</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/applications/plot_face_recognition.html#sphx-glr-auto-examples-applications-plot-face-recognition-py"><span class="std std-ref">Faces recognition example using eigenfaces and SVMs</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_face_recognition.py</span></code>)</p></td>
<td><p>00:22.070</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/kernel_approximation/plot_scalable_poly_kernels.html#sphx-glr-auto-examples-kernel-approximation-plot-scalable-poly-kernels-py"><span class="std std-ref">Scalable learning with polynomial kernel approximation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/kernel_approximation/plot_scalable_poly_kernels.py</span></code>)</p></td>
<td><p>00:20.979</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/inspection/plot_partial_dependence.html#sphx-glr-auto-examples-inspection-plot-partial-dependence-py"><span class="std std-ref">Partial Dependence and Individual Conditional Expectation Plots</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/inspection/plot_partial_dependence.py</span></code>)</p></td>
<td><p>00:20.546</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/multiclass/plot_multiclass_overview.html#sphx-glr-auto-examples-multiclass-plot-multiclass-overview-py"><span class="std std-ref">Overview of multiclass training meta-estimators</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/multiclass/plot_multiclass_overview.py</span></code>)</p></td>
<td><p>00:18.937</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/applications/plot_model_complexity_influence.html#sphx-glr-auto-examples-applications-plot-model-complexity-influence-py"><span class="std std-ref">Model Complexity Influence</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_model_complexity_influence.py</span></code>)</p></td>
<td><p>00:18.200</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#sphx-glr-auto-examples-inspection-plot-linear-model-coefficient-interpretation-py"><span class="std std-ref">Common pitfalls in the interpretation of coefficients of linear models</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/inspection/plot_linear_model_coefficient_interpretation.py</span></code>)</p></td>
<td><p>00:17.747</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/applications/plot_prediction_latency.html#sphx-glr-auto-examples-applications-plot-prediction-latency-py"><span class="std std-ref">Prediction Latency</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_prediction_latency.py</span></code>)</p></td>
<td><p>00:16.832</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/manifold/plot_swissroll.html#sphx-glr-auto-examples-manifold-plot-swissroll-py"><span class="std std-ref">Swiss Roll And Swiss-Hole Reduction</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/manifold/plot_swissroll.py</span></code>)</p></td>
<td><p>00:16.777</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/manifold/plot_lle_digits.html#sphx-glr-auto-examples-manifold-plot-lle-digits-py"><span class="std std-ref">Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/manifold/plot_lle_digits.py</span></code>)</p></td>
<td><p>00:15.437</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/cluster/plot_hdbscan.html#sphx-glr-auto-examples-cluster-plot-hdbscan-py"><span class="std std-ref">Demo of HDBSCAN clustering algorithm</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_hdbscan.py</span></code>)</p></td>
<td><p>00:15.242</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_gradient_boosting_categorical.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-categorical-py"><span class="std std-ref">Categorical Feature Support in Gradient Boosting</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_gradient_boosting_categorical.py</span></code>)</p></td>
<td><p>00:14.872</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/applications/plot_time_series_lagged_features.html#sphx-glr-auto-examples-applications-plot-time-series-lagged-features-py"><span class="std std-ref">Lagged features for time series forecasting</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_time_series_lagged_features.py</span></code>)</p></td>
<td><p>00:14.788</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/applications/plot_cyclical_feature_engineering.html#sphx-glr-auto-examples-applications-plot-cyclical-feature-engineering-py"><span class="std std-ref">Time-related feature engineering</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_cyclical_feature_engineering.py</span></code>)</p></td>
<td><p>00:12.966</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/bicluster/plot_bicluster_newsgroups.html#sphx-glr-auto-examples-bicluster-plot-bicluster-newsgroups-py"><span class="std std-ref">Biclustering documents with the Spectral Co-clustering algorithm</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/bicluster/plot_bicluster_newsgroups.py</span></code>)</p></td>
<td><p>00:12.913</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/manifold/plot_compare_methods.html#sphx-glr-auto-examples-manifold-plot-compare-methods-py"><span class="std std-ref">Comparison of Manifold Learning methods</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/manifold/plot_compare_methods.py</span></code>)</p></td>
<td><p>00:11.760</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.html#sphx-glr-auto-examples-miscellaneous-plot-johnson-lindenstrauss-bound-py"><span class="std std-ref">The Johnson-Lindenstrauss bound for embedding with random projections</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_johnson_lindenstrauss_bound.py</span></code>)</p></td>
<td><p>00:11.391</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/applications/plot_digits_denoising.html#sphx-glr-auto-examples-applications-plot-digits-denoising-py"><span class="std std-ref">Image denoising using kernel PCA</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_digits_denoising.py</span></code>)</p></td>
<td><p>00:11.345</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_permutation_tests_for_classification.html#sphx-glr-auto-examples-model-selection-plot-permutation-tests-for-classification-py"><span class="std std-ref">Test with permutations the significance of a classification score</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_permutation_tests_for_classification.py</span></code>)</p></td>
<td><p>00:11.248</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/applications/plot_topics_extraction_with_nmf_lda.html#sphx-glr-auto-examples-applications-plot-topics-extraction-with-nmf-lda-py"><span class="std std-ref">Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_topics_extraction_with_nmf_lda.py</span></code>)</p></td>
<td><p>00:11.222</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/applications/plot_species_distribution_modeling.html#sphx-glr-auto-examples-applications-plot-species-distribution-modeling-py"><span class="std std-ref">Species distribution modeling</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_species_distribution_modeling.py</span></code>)</p></td>
<td><p>00:10.812</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/applications/plot_tomography_l1_reconstruction.html#sphx-glr-auto-examples-applications-plot-tomography-l1-reconstruction-py"><span class="std std-ref">Compressive sensing: tomography reconstruction with L1 prior (Lasso)</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_tomography_l1_reconstruction.py</span></code>)</p></td>
<td><p>00:09.684</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_grid_search_digits.html#sphx-glr-auto-examples-model-selection-plot-grid-search-digits-py"><span class="std std-ref">Custom refit strategy of a grid search with cross-validation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_grid_search_digits.py</span></code>)</p></td>
<td><p>00:09.567</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/applications/plot_out_of_core_classification.html#sphx-glr-auto-examples-applications-plot-out-of-core-classification-py"><span class="std std-ref">Out-of-core classification of text documents</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_out_of_core_classification.py</span></code>)</p></td>
<td><p>00:09.508</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_gradient_boosting_oob.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-oob-py"><span class="std std-ref">Gradient Boosting Out-of-Bag estimates</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_gradient_boosting_oob.py</span></code>)</p></td>
<td><p>00:09.296</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_gradient_boosting_quantile.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-quantile-py"><span class="std std-ref">Prediction Intervals for Gradient Boosting Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_gradient_boosting_quantile.py</span></code>)</p></td>
<td><p>00:09.113</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_tweedie_regression_insurance_claims.html#sphx-glr-auto-examples-linear-model-plot-tweedie-regression-insurance-claims-py"><span class="std std-ref">Tweedie regression on insurance claims</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_tweedie_regression_insurance_claims.py</span></code>)</p></td>
<td><p>00:09.101</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/decomposition/plot_faces_decomposition.html#sphx-glr-auto-examples-decomposition-plot-faces-decomposition-py"><span class="std std-ref">Faces dataset decompositions</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/decomposition/plot_faces_decomposition.py</span></code>)</p></td>
<td><p>00:09.027</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/impute/plot_missing_values.html#sphx-glr-auto-examples-impute-plot-missing-values-py"><span class="std std-ref">Imputing missing values before building an estimator</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/impute/plot_missing_values.py</span></code>)</p></td>
<td><p>00:08.378</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/preprocessing/plot_all_scaling.html#sphx-glr-auto-examples-preprocessing-plot-all-scaling-py"><span class="std std-ref">Compare the effect of different scalers on data with outliers</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/preprocessing/plot_all_scaling.py</span></code>)</p></td>
<td><p>00:08.205</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/miscellaneous/plot_kernel_ridge_regression.html#sphx-glr-auto-examples-miscellaneous-plot-kernel-ridge-regression-py"><span class="std std-ref">Comparison of kernel ridge regression and SVR</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_kernel_ridge_regression.py</span></code>)</p></td>
<td><p>00:08.155</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_sgd_comparison.html#sphx-glr-auto-examples-linear-model-plot-sgd-comparison-py"><span class="std std-ref">Comparing various online solvers</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_sgd_comparison.py</span></code>)</p></td>
<td><p>00:08.150</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-mnist-py"><span class="std std-ref">MNIST classification using multinomial logistic + L1</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_sparse_logistic_regression_mnist.py</span></code>)</p></td>
<td><p>00:08.085</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_gradient_boosting_regularization.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regularization-py"><span class="std std-ref">Gradient Boosting regularization</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_gradient_boosting_regularization.py</span></code>)</p></td>
<td><p>00:07.974</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_multi_metric_evaluation.html#sphx-glr-auto-examples-model-selection-plot-multi-metric-evaluation-py"><span class="std std-ref">Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_multi_metric_evaluation.py</span></code>)</p></td>
<td><p>00:07.799</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/classification/plot_lda.html#sphx-glr-auto-examples-classification-plot-lda-py"><span class="std std-ref">Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/classification/plot_lda.py</span></code>)</p></td>
<td><p>00:07.641</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_successive_halving_heatmap.html#sphx-glr-auto-examples-model-selection-plot-successive-halving-heatmap-py"><span class="std std-ref">Comparison between grid search and successive halving</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_successive_halving_heatmap.py</span></code>)</p></td>
<td><p>00:07.322</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/text/plot_document_clustering.html#sphx-glr-auto-examples-text-plot-document-clustering-py"><span class="std std-ref">Clustering text documents using k-means</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/text/plot_document_clustering.py</span></code>)</p></td>
<td><p>00:07.166</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/neural_networks/plot_mnist_filters.html#sphx-glr-auto-examples-neural-networks-plot-mnist-filters-py"><span class="std std-ref">Visualization of MLP weights on MNIST</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neural_networks/plot_mnist_filters.py</span></code>)</p></td>
<td><p>00:06.876</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/semi_supervised/plot_semi_supervised_newsgroups.html#sphx-glr-auto-examples-semi-supervised-plot-semi-supervised-newsgroups-py"><span class="std std-ref">Semi-supervised Classification on a Text Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/semi_supervised/plot_semi_supervised_newsgroups.py</span></code>)</p></td>
<td><p>00:06.803</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/multioutput/plot_classifier_chain_yeast.html#sphx-glr-auto-examples-multioutput-plot-classifier-chain-yeast-py"><span class="std std-ref">Multilabel classification using a classifier chain</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/multioutput/plot_classifier_chain_yeast.py</span></code>)</p></td>
<td><p>00:06.725</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_forest_iris.html#sphx-glr-auto-examples-ensemble-plot-forest-iris-py"><span class="std std-ref">Plot the decision surfaces of ensembles of trees on the iris dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_forest_iris.py</span></code>)</p></td>
<td><p>00:06.537</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/text/plot_document_classification_20newsgroups.html#sphx-glr-auto-examples-text-plot-document-classification-20newsgroups-py"><span class="std std-ref">Classification of text documents using sparse features</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/text/plot_document_classification_20newsgroups.py</span></code>)</p></td>
<td><p>00:06.507</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_train_error_vs_test_error.html#sphx-glr-auto-examples-model-selection-plot-train-error-vs-test-error-py"><span class="std std-ref">Train error vs Test error</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_train_error_vs_test_error.py</span></code>)</p></td>
<td><p>00:06.259</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">Comparing different clustering algorithms on toy datasets</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_cluster_comparison.py</span></code>)</p></td>
<td><p>00:06.252</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_nested_cross_validation_iris.html#sphx-glr-auto-examples-model-selection-plot-nested-cross-validation-iris-py"><span class="std std-ref">Nested versus non-nested cross-validation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_nested_cross_validation_iris.py</span></code>)</p></td>
<td><p>00:05.886</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/mixture/plot_concentration_prior.html#sphx-glr-auto-examples-mixture-plot-concentration-prior-py"><span class="std std-ref">Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/mixture/plot_concentration_prior.py</span></code>)</p></td>
<td><p>00:05.831</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/semi_supervised/plot_self_training_varying_threshold.html#sphx-glr-auto-examples-semi-supervised-plot-self-training-varying-threshold-py"><span class="std std-ref">Effect of varying threshold for self-training</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/semi_supervised/plot_self_training_varying_threshold.py</span></code>)</p></td>
<td><p>00:05.591</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/exercises/plot_iris_exercise.html#sphx-glr-auto-examples-exercises-plot-iris-exercise-py"><span class="std std-ref">SVM Exercise</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/exercises/plot_iris_exercise.py</span></code>)</p></td>
<td><p>00:05.509</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/manifold/plot_manifold_sphere.html#sphx-glr-auto-examples-manifold-plot-manifold-sphere-py"><span class="std std-ref">Manifold Learning methods on a severed sphere</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/manifold/plot_manifold_sphere.py</span></code>)</p></td>
<td><p>00:05.503</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-20newsgroups-py"><span class="std std-ref">Multiclass sparse logistic regression on 20newgroups</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py</span></code>)</p></td>
<td><p>00:05.439</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/impute/plot_iterative_imputer_variants_comparison.html#sphx-glr-auto-examples-impute-plot-iterative-imputer-variants-comparison-py"><span class="std std-ref">Imputing missing values with variants of IterativeImputer</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/impute/plot_iterative_imputer_variants_comparison.py</span></code>)</p></td>
<td><p>00:05.197</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/text/plot_hashing_vs_dict_vectorizer.html#sphx-glr-auto-examples-text-plot-hashing-vs-dict-vectorizer-py"><span class="std std-ref">FeatureHasher and DictVectorizer Comparison</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/text/plot_hashing_vs_dict_vectorizer.py</span></code>)</p></td>
<td><p>00:05.034</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/cluster/plot_coin_segmentation.html#sphx-glr-auto-examples-cluster-plot-coin-segmentation-py"><span class="std std-ref">Segmenting the picture of greek coins in regions</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_coin_segmentation.py</span></code>)</p></td>
<td><p>00:04.996</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/svm/plot_rbf_parameters.html#sphx-glr-auto-examples-svm-plot-rbf-parameters-py"><span class="std std-ref">RBF SVM parameters</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_rbf_parameters.py</span></code>)</p></td>
<td><p>00:04.827</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_randomized_search.html#sphx-glr-auto-examples-model-selection-plot-randomized-search-py"><span class="std std-ref">Comparing randomized search and grid search for hyperparameter estimation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_randomized_search.py</span></code>)</p></td>
<td><p>00:04.547</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_successive_halving_iterations.html#sphx-glr-auto-examples-model-selection-plot-successive-halving-iterations-py"><span class="std std-ref">Successive Halving Iterations</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_successive_halving_iterations.py</span></code>)</p></td>
<td><p>00:04.370</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/gaussian_process/plot_compare_gpr_krr.html#sphx-glr-auto-examples-gaussian-process-plot-compare-gpr-krr-py"><span class="std std-ref">Comparison of kernel ridge and Gaussian process regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_compare_gpr_krr.py</span></code>)</p></td>
<td><p>00:04.349</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_adaboost_multiclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-multiclass-py"><span class="std std-ref">Multi-class AdaBoosted Decision Trees</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_adaboost_multiclass.py</span></code>)</p></td>
<td><p>00:04.342</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/neighbors/plot_digits_kde_sampling.html#sphx-glr-auto-examples-neighbors-plot-digits-kde-sampling-py"><span class="std std-ref">Kernel Density Estimation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_digits_kde_sampling.py</span></code>)</p></td>
<td><p>00:04.238</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/inspection/plot_permutation_importance_multicollinear.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-multicollinear-py"><span class="std std-ref">Permutation Importance with Multicollinear or Correlated Features</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/inspection/plot_permutation_importance_multicollinear.py</span></code>)</p></td>
<td><p>00:04.051</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/inspection/plot_permutation_importance.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-py"><span class="std std-ref">Permutation Importance vs Random Forest Feature Importance (MDI)</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/inspection/plot_permutation_importance.py</span></code>)</p></td>
<td><p>00:03.940</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/feature_selection/plot_rfe_digits.html#sphx-glr-auto-examples-feature-selection-plot-rfe-digits-py"><span class="std std-ref">Recursive feature elimination</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/feature_selection/plot_rfe_digits.py</span></code>)</p></td>
<td><p>00:03.646</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/cluster/plot_birch_vs_minibatchkmeans.html#sphx-glr-auto-examples-cluster-plot-birch-vs-minibatchkmeans-py"><span class="std std-ref">Compare BIRCH and MiniBatchKMeans</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_birch_vs_minibatchkmeans.py</span></code>)</p></td>
<td><p>00:03.642</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_ensemble_oob.html#sphx-glr-auto-examples-ensemble-plot-ensemble-oob-py"><span class="std std-ref">OOB Errors for Random Forests</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_ensemble_oob.py</span></code>)</p></td>
<td><p>00:03.634</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/cluster/plot_dict_face_patches.html#sphx-glr-auto-examples-cluster-plot-dict-face-patches-py"><span class="std std-ref">Online learning of a dictionary of parts of faces</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_dict_face_patches.py</span></code>)</p></td>
<td><p>00:03.550</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/neighbors/plot_species_kde.html#sphx-glr-auto-examples-neighbors-plot-species-kde-py"><span class="std std-ref">Kernel Density Estimate of Species Distributions</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_species_kde.py</span></code>)</p></td>
<td><p>00:03.395</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_gradient_boosting_early_stopping.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-early-stopping-py"><span class="std std-ref">Early stopping in Gradient Boosting</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_gradient_boosting_early_stopping.py</span></code>)</p></td>
<td><p>00:03.297</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/decomposition/plot_pca_vs_fa_model_selection.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-fa-model-selection-py"><span class="std std-ref">Model selection with Probabilistic PCA and Factor Analysis (FA)</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/decomposition/plot_pca_vs_fa_model_selection.py</span></code>)</p></td>
<td><p>00:03.284</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/preprocessing/plot_discretization_classification.html#sphx-glr-auto-examples-preprocessing-plot-discretization-classification-py"><span class="std std-ref">Feature discretization</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/preprocessing/plot_discretization_classification.py</span></code>)</p></td>
<td><p>00:03.217</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/miscellaneous/plot_anomaly_comparison.html#sphx-glr-auto-examples-miscellaneous-plot-anomaly-comparison-py"><span class="std std-ref">Comparing anomaly detection algorithms for outlier detection on toy datasets</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_anomaly_comparison.py</span></code>)</p></td>
<td><p>00:03.081</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/manifold/plot_t_sne_perplexity.html#sphx-glr-auto-examples-manifold-plot-t-sne-perplexity-py"><span class="std std-ref">t-SNE: The effect of various perplexity values on the shape</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/manifold/plot_t_sne_perplexity.py</span></code>)</p></td>
<td><p>00:03.037</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/gaussian_process/plot_gpc_iris.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-iris-py"><span class="std std-ref">Gaussian process classification (GPC) on iris dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_gpc_iris.py</span></code>)</p></td>
<td><p>00:03.032</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/covariance/plot_robust_vs_empirical_covariance.html#sphx-glr-auto-examples-covariance-plot-robust-vs-empirical-covariance-py"><span class="std std-ref">Robust vs Empirical covariance estimate</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/covariance/plot_robust_vs_empirical_covariance.py</span></code>)</p></td>
<td><p>00:02.952</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/neural_networks/plot_mlp_training_curves.html#sphx-glr-auto-examples-neural-networks-plot-mlp-training-curves-py"><span class="std std-ref">Compare Stochastic learning strategies for MLPClassifier</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neural_networks/plot_mlp_training_curves.py</span></code>)</p></td>
<td><p>00:02.876</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/neural_networks/plot_rbm_logistic_classification.html#sphx-glr-auto-examples-neural-networks-plot-rbm-logistic-classification-py"><span class="std std-ref">Restricted Boltzmann Machine features for digit classification</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neural_networks/plot_rbm_logistic_classification.py</span></code>)</p></td>
<td><p>00:02.779</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_feature_transformation.html#sphx-glr-auto-examples-ensemble-plot-feature-transformation-py"><span class="std std-ref">Feature transformations with ensembles of trees</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_feature_transformation.py</span></code>)</p></td>
<td><p>00:02.692</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/calibration/plot_compare_calibration.html#sphx-glr-auto-examples-calibration-plot-compare-calibration-py"><span class="std std-ref">Comparison of Calibration of Classifiers</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/calibration/plot_compare_calibration.py</span></code>)</p></td>
<td><p>00:02.647</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/gaussian_process/plot_gpr_noisy.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-noisy-py"><span class="std std-ref">Ability of Gaussian process regression (GPR) to estimate data noise-level</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_gpr_noisy.py</span></code>)</p></td>
<td><p>00:02.641</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/applications/plot_stock_market.html#sphx-glr-auto-examples-applications-plot-stock-market-py"><span class="std std-ref">Visualizing the stock market structure</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_stock_market.py</span></code>)</p></td>
<td><p>00:02.544</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/covariance/plot_lw_vs_oas.html#sphx-glr-auto-examples-covariance-plot-lw-vs-oas-py"><span class="std std-ref">Ledoit-Wolf vs OAS estimation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/covariance/plot_lw_vs_oas.py</span></code>)</p></td>
<td><p>00:02.527</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/inspection/plot_causal_interpretation.html#sphx-glr-auto-examples-inspection-plot-causal-interpretation-py"><span class="std std-ref">Failure of Machine Learning to infer causal effects</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/inspection/plot_causal_interpretation.py</span></code>)</p></td>
<td><p>00:02.501</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/compose/plot_column_transformer.html#sphx-glr-auto-examples-compose-plot-column-transformer-py"><span class="std std-ref">Column Transformer with Heterogeneous Data Sources</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/compose/plot_column_transformer.py</span></code>)</p></td>
<td><p>00:02.491</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/miscellaneous/plot_partial_dependence_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-partial-dependence-visualization-api-py"><span class="std std-ref">Advanced Plotting With Partial Dependence</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_partial_dependence_visualization_api.py</span></code>)</p></td>
<td><p>00:02.432</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/calibration/plot_calibration_curve.html#sphx-glr-auto-examples-calibration-plot-calibration-curve-py"><span class="std std-ref">Probability Calibration curves</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/calibration/plot_calibration_curve.py</span></code>)</p></td>
<td><p>00:02.269</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py"><span class="std std-ref">Classifier comparison</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/classification/plot_classifier_comparison.py</span></code>)</p></td>
<td><p>00:02.237</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/gaussian_process/plot_gpc.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-py"><span class="std std-ref">Probabilistic predictions with Gaussian process classification (GPC)</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_gpc.py</span></code>)</p></td>
<td><p>00:02.189</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_likelihood_ratios.html#sphx-glr-auto-examples-model-selection-plot-likelihood-ratios-py"><span class="std std-ref">Class Likelihood Ratios to measure classification performance</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_likelihood_ratios.py</span></code>)</p></td>
<td><p>00:02.109</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/cluster/plot_face_compress.html#sphx-glr-auto-examples-cluster-plot-face-compress-py"><span class="std std-ref">Vector Quantization Example</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_face_compress.py</span></code>)</p></td>
<td><p>00:02.095</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/cluster/plot_inductive_clustering.html#sphx-glr-auto-examples-cluster-plot-inductive-clustering-py"><span class="std std-ref">Inductive Clustering</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_inductive_clustering.py</span></code>)</p></td>
<td><p>00:01.989</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/preprocessing/plot_map_data_to_normal.html#sphx-glr-auto-examples-preprocessing-plot-map-data-to-normal-py"><span class="std std-ref">Map data to a normal distribution</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/preprocessing/plot_map_data_to_normal.py</span></code>)</p></td>
<td><p>00:01.934</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/cluster/plot_agglomerative_clustering.html#sphx-glr-auto-examples-cluster-plot-agglomerative-clustering-py"><span class="std std-ref">Agglomerative clustering with and without structure</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_agglomerative_clustering.py</span></code>)</p></td>
<td><p>00:01.933</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/neural_networks/plot_mlp_alpha.html#sphx-glr-auto-examples-neural-networks-plot-mlp-alpha-py"><span class="std std-ref">Varying regularization in Multi-layer Perceptron</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neural_networks/plot_mlp_alpha.py</span></code>)</p></td>
<td><p>00:01.915</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/cluster/plot_linkage_comparison.html#sphx-glr-auto-examples-cluster-plot-linkage-comparison-py"><span class="std std-ref">Comparing different hierarchical linkage methods on toy datasets</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_linkage_comparison.py</span></code>)</p></td>
<td><p>00:01.835</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_robust_fit.html#sphx-glr-auto-examples-linear-model-plot-robust-fit-py"><span class="std std-ref">Robust linear estimator fitting</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_robust_fit.py</span></code>)</p></td>
<td><p>00:01.825</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/miscellaneous/plot_kernel_approximation.html#sphx-glr-auto-examples-miscellaneous-plot-kernel-approximation-py"><span class="std std-ref">Explicit feature map approximation for RBF kernels</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_kernel_approximation.py</span></code>)</p></td>
<td><p>00:01.740</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/preprocessing/plot_scaling_importance.html#sphx-glr-auto-examples-preprocessing-plot-scaling-importance-py"><span class="std std-ref">Importance of Feature Scaling</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/preprocessing/plot_scaling_importance.py</span></code>)</p></td>
<td><p>00:01.710</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/neighbors/plot_nca_dim_reduction.html#sphx-glr-auto-examples-neighbors-plot-nca-dim-reduction-py"><span class="std std-ref">Dimensionality Reduction with Neighborhood Components Analysis</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_nca_dim_reduction.py</span></code>)</p></td>
<td><p>00:01.699</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/miscellaneous/plot_multioutput_face_completion.html#sphx-glr-auto-examples-miscellaneous-plot-multioutput-face-completion-py"><span class="std std-ref">Face completion with a multi-output estimators</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_multioutput_face_completion.py</span></code>)</p></td>
<td><p>00:01.646</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/cluster/plot_optics.html#sphx-glr-auto-examples-cluster-plot-optics-py"><span class="std std-ref">Demo of OPTICS clustering algorithm</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_optics.py</span></code>)</p></td>
<td><p>00:01.639</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/miscellaneous/plot_display_object_visualization.html#sphx-glr-auto-examples-miscellaneous-plot-display-object-visualization-py"><span class="std std-ref">Visualizations with Display Objects</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_display_object_visualization.py</span></code>)</p></td>
<td><p>00:01.606</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/gaussian_process/plot_gpr_prior_posterior.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-prior-posterior-py"><span class="std std-ref">Illustration of prior and posterior Gaussian process for different kernels</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_gpr_prior_posterior.py</span></code>)</p></td>
<td><p>00:01.582</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/cluster/plot_digits_linkage.html#sphx-glr-auto-examples-cluster-plot-digits-linkage-py"><span class="std std-ref">Various Agglomerative Clustering on a 2D embedding of digits</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_digits_linkage.py</span></code>)</p></td>
<td><p>00:01.559</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/compose/plot_digits_pipe.html#sphx-glr-auto-examples-compose-plot-digits-pipe-py"><span class="std std-ref">Pipelining: chaining a PCA and a logistic regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/compose/plot_digits_pipe.py</span></code>)</p></td>
<td><p>00:01.554</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/classification/plot_classification_probability.html#sphx-glr-auto-examples-classification-plot-classification-probability-py"><span class="std std-ref">Plot classification probability</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/classification/plot_classification_probability.py</span></code>)</p></td>
<td><p>00:01.493</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_grid_search_stats.html#sphx-glr-auto-examples-model-selection-plot-grid-search-stats-py"><span class="std std-ref">Statistical comparison of models using grid search</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_grid_search_stats.py</span></code>)</p></td>
<td><p>00:01.462</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/svm/plot_svm_nonlinear.html#sphx-glr-auto-examples-svm-plot-svm-nonlinear-py"><span class="std std-ref">Non-linear SVM</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_svm_nonlinear.py</span></code>)</p></td>
<td><p>00:01.436</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/calibration/plot_calibration_multiclass.html#sphx-glr-auto-examples-calibration-plot-calibration-multiclass-py"><span class="std std-ref">Probability Calibration for 3-class classification</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/calibration/plot_calibration_multiclass.py</span></code>)</p></td>
<td><p>00:01.433</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/compose/plot_transformed_target.html#sphx-glr-auto-examples-compose-plot-transformed-target-py"><span class="std std-ref">Effect of transforming the targets in regression model</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/compose/plot_transformed_target.py</span></code>)</p></td>
<td><p>00:01.402</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/release_highlights/plot_release_highlights_1_3_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-3-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.3</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_1_3_0.py</span></code>)</p></td>
<td><p>00:01.382</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/cluster/plot_kmeans_stability_low_dim_dense.html#sphx-glr-auto-examples-cluster-plot-kmeans-stability-low-dim-dense-py"><span class="std std-ref">Empirical evaluation of the impact of k-means initialization</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_kmeans_stability_low_dim_dense.py</span></code>)</p></td>
<td><p>00:01.370</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_gradient_boosting_regression.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regression-py"><span class="std std-ref">Gradient Boosting regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_gradient_boosting_regression.py</span></code>)</p></td>
<td><p>00:01.364</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/mixture/plot_gmm_selection.html#sphx-glr-auto-examples-mixture-plot-gmm-selection-py"><span class="std std-ref">Gaussian Mixture Model Selection</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/mixture/plot_gmm_selection.py</span></code>)</p></td>
<td><p>00:01.314</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.html#sphx-glr-auto-examples-linear-model-plot-lasso-dense-vs-sparse-data-py"><span class="std std-ref">Lasso on dense and sparse data</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_lasso_dense_vs_sparse_data.py</span></code>)</p></td>
<td><p>00:01.300</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_grid_search_refit_callable.html#sphx-glr-auto-examples-model-selection-plot-grid-search-refit-callable-py"><span class="std std-ref">Balance model complexity and cross-validated score</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_grid_search_refit_callable.py</span></code>)</p></td>
<td><p>00:01.261</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/neighbors/plot_caching_nearest_neighbors.html#sphx-glr-auto-examples-neighbors-plot-caching-nearest-neighbors-py"><span class="std std-ref">Caching nearest neighbors</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_caching_nearest_neighbors.py</span></code>)</p></td>
<td><p>00:01.250</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_forest_importances_faces.html#sphx-glr-auto-examples-ensemble-plot-forest-importances-faces-py"><span class="std std-ref">Pixel importances with a parallel forest of trees</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_forest_importances_faces.py</span></code>)</p></td>
<td><p>00:01.200</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py"><span class="std std-ref">Column Transformer with Mixed Types</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/compose/plot_column_transformer_mixed_types.py</span></code>)</p></td>
<td><p>00:01.188</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_cv_indices.html#sphx-glr-auto-examples-model-selection-plot-cv-indices-py"><span class="std std-ref">Visualizing cross-validation behavior in scikit-learn</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_cv_indices.py</span></code>)</p></td>
<td><p>00:01.178</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/release_highlights/plot_release_highlights_0_22_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-22-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.22</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_0_22_0.py</span></code>)</p></td>
<td><p>00:01.150</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_bias_variance.html#sphx-glr-auto-examples-ensemble-plot-bias-variance-py"><span class="std std-ref">Single estimator versus bagging: bias-variance decomposition</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_bias_variance.py</span></code>)</p></td>
<td><p>00:01.141</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/cluster/plot_kmeans_silhouette_analysis.html#sphx-glr-auto-examples-cluster-plot-kmeans-silhouette-analysis-py"><span class="std std-ref">Selecting the number of clusters with silhouette analysis on KMeans clustering</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_kmeans_silhouette_analysis.py</span></code>)</p></td>
<td><p>00:01.140</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/cluster/plot_kmeans_assumptions.html#sphx-glr-auto-examples-cluster-plot-kmeans-assumptions-py"><span class="std std-ref">Demonstration of k-means assumptions</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_kmeans_assumptions.py</span></code>)</p></td>
<td><p>00:01.087</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/cluster/plot_agglomerative_clustering_metrics.html#sphx-glr-auto-examples-cluster-plot-agglomerative-clustering-metrics-py"><span class="std std-ref">Agglomerative clustering with different metrics</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_agglomerative_clustering_metrics.py</span></code>)</p></td>
<td><p>00:01.077</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/cluster/plot_adjusted_for_chance_measures.html#sphx-glr-auto-examples-cluster-plot-adjusted-for-chance-measures-py"><span class="std std-ref">Adjustment for chance in clustering performance evaluation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_adjusted_for_chance_measures.py</span></code>)</p></td>
<td><p>00:01.045</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/cluster/plot_bisect_kmeans.html#sphx-glr-auto-examples-cluster-plot-bisect-kmeans-py"><span class="std std-ref">Bisecting K-Means and Regular K-Means Performance Comparison</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_bisect_kmeans.py</span></code>)</p></td>
<td><p>00:01.008</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_voting_regressor.html#sphx-glr-auto-examples-ensemble-plot-voting-regressor-py"><span class="std std-ref">Plot individual and voting regression predictions</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_voting_regressor.py</span></code>)</p></td>
<td><p>00:00.973</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/svm/plot_svm_tie_breaking.html#sphx-glr-auto-examples-svm-plot-svm-tie-breaking-py"><span class="std std-ref">SVM Tie Breaking Example</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_svm_tie_breaking.py</span></code>)</p></td>
<td><p>00:00.944</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/release_highlights/plot_release_highlights_1_1_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-1-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.1</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_1_1_0.py</span></code>)</p></td>
<td><p>00:00.912</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_lasso_model_selection.html#sphx-glr-auto-examples-linear-model-plot-lasso-model-selection-py"><span class="std std-ref">Lasso model selection: AIC-BIC / cross-validation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_lasso_model_selection.py</span></code>)</p></td>
<td><p>00:00.909</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_forest_importances.html#sphx-glr-auto-examples-ensemble-plot-forest-importances-py"><span class="std std-ref">Feature importances with a forest of trees</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_forest_importances.py</span></code>)</p></td>
<td><p>00:00.907</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.html#sphx-glr-auto-examples-semi-supervised-plot-semi-supervised-versus-svm-iris-py"><span class="std std-ref">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/semi_supervised/plot_semi_supervised_versus_svm_iris.py</span></code>)</p></td>
<td><p>00:00.871</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/tree/plot_iris_dtc.html#sphx-glr-auto-examples-tree-plot-iris-dtc-py"><span class="std std-ref">Plot the decision surface of decision trees trained on the iris dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/tree/plot_iris_dtc.py</span></code>)</p></td>
<td><p>00:00.857</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/neighbors/plot_nca_classification.html#sphx-glr-auto-examples-neighbors-plot-nca-classification-py"><span class="std std-ref">Comparing Nearest Neighbors with and without Neighborhood Components Analysis</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_nca_classification.py</span></code>)</p></td>
<td><p>00:00.797</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.html#sphx-glr-auto-examples-linear-model-plot-elastic-net-precomputed-gram-matrix-with-weighted-samples-py"><span class="std std-ref">Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py</span></code>)</p></td>
<td><p>00:00.765</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/cluster/plot_kmeans_digits.html#sphx-glr-auto-examples-cluster-plot-kmeans-digits-py"><span class="std std-ref">A demo of K-Means clustering on the handwritten digits data</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_kmeans_digits.py</span></code>)</p></td>
<td><p>00:00.724</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Plot the decision boundaries of a VotingClassifier</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_voting_decision_regions.py</span></code>)</p></td>
<td><p>00:00.680</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_ard.html#sphx-glr-auto-examples-linear-model-plot-ard-py"><span class="std std-ref">Comparing Linear Bayesian Regressors</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_ard.py</span></code>)</p></td>
<td><p>00:00.674</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/neighbors/plot_lof_novelty_detection.html#sphx-glr-auto-examples-neighbors-plot-lof-novelty-detection-py"><span class="std std-ref">Novelty detection with Local Outlier Factor (LOF)</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_lof_novelty_detection.py</span></code>)</p></td>
<td><p>00:00.665</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_adaboost_twoclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-twoclass-py"><span class="std std-ref">Two-class AdaBoost</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_adaboost_twoclass.py</span></code>)</p></td>
<td><p>00:00.664</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_ridge_coeffs.html#sphx-glr-auto-examples-linear-model-plot-ridge-coeffs-py"><span class="std std-ref">Ridge coefficients as a function of the L2 Regularization</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_ridge_coeffs.py</span></code>)</p></td>
<td><p>00:00.658</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/preprocessing/plot_discretization_strategies.html#sphx-glr-auto-examples-preprocessing-plot-discretization-strategies-py"><span class="std std-ref">Demonstrating the different strategies of KBinsDiscretizer</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/preprocessing/plot_discretization_strategies.py</span></code>)</p></td>
<td><p>00:00.652</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_roc.html#sphx-glr-auto-examples-model-selection-plot-roc-py"><span class="std std-ref">Multiclass Receiver Operating Characteristic (ROC)</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_roc.py</span></code>)</p></td>
<td><p>00:00.626</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/release_highlights/plot_release_highlights_0_23_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-23-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.23</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_0_23_0.py</span></code>)</p></td>
<td><p>00:00.616</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/neighbors/plot_kde_1d.html#sphx-glr-auto-examples-neighbors-plot-kde-1d-py"><span class="std std-ref">Simple 1D Kernel Density Estimation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_kde_1d.py</span></code>)</p></td>
<td><p>00:00.594</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_monotonic_constraints.html#sphx-glr-auto-examples-ensemble-plot-monotonic-constraints-py"><span class="std std-ref">Monotonic Constraints</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_monotonic_constraints.py</span></code>)</p></td>
<td><p>00:00.581</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/mixture/plot_gmm_init.html#sphx-glr-auto-examples-mixture-plot-gmm-init-py"><span class="std std-ref">GMM Initialization Methods</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/mixture/plot_gmm_init.py</span></code>)</p></td>
<td><p>00:00.571</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/decomposition/plot_kernel_pca.html#sphx-glr-auto-examples-decomposition-plot-kernel-pca-py"><span class="std std-ref">Kernel PCA</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/decomposition/plot_kernel_pca.py</span></code>)</p></td>
<td><p>00:00.559</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_validation_curve.html#sphx-glr-auto-examples-model-selection-plot-validation-curve-py"><span class="std std-ref">Plotting Validation Curves</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_validation_curve.py</span></code>)</p></td>
<td><p>00:00.554</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py"><span class="std std-ref">Nearest Neighbors Classification</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_classification.py</span></code>)</p></td>
<td><p>00:00.553</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_theilsen.html#sphx-glr-auto-examples-linear-model-plot-theilsen-py"><span class="std std-ref">Theil-Sen Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_theilsen.py</span></code>)</p></td>
<td><p>00:00.546</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/bicluster/plot_spectral_biclustering.html#sphx-glr-auto-examples-bicluster-plot-spectral-biclustering-py"><span class="std std-ref">A demo of the Spectral Biclustering algorithm</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/bicluster/plot_spectral_biclustering.py</span></code>)</p></td>
<td><p>00:00.541</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_quantile_regression.html#sphx-glr-auto-examples-linear-model-plot-quantile-regression-py"><span class="std std-ref">Quantile regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_quantile_regression.py</span></code>)</p></td>
<td><p>00:00.539</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/covariance/plot_sparse_cov.html#sphx-glr-auto-examples-covariance-plot-sparse-cov-py"><span class="std std-ref">Sparse inverse covariance estimation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/covariance/plot_sparse_cov.py</span></code>)</p></td>
<td><p>00:00.533</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/cluster/plot_color_quantization.html#sphx-glr-auto-examples-cluster-plot-color-quantization-py"><span class="std std-ref">Color Quantization using K-Means</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_color_quantization.py</span></code>)</p></td>
<td><p>00:00.531</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/cross_decomposition/plot_pcr_vs_pls.html#sphx-glr-auto-examples-cross-decomposition-plot-pcr-vs-pls-py"><span class="std std-ref">Principal Component Regression vs Partial Least Squares Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cross_decomposition/plot_pcr_vs_pls.py</span></code>)</p></td>
<td><p>00:00.531</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/exercises/plot_cv_diabetes.html#sphx-glr-auto-examples-exercises-plot-cv-diabetes-py"><span class="std std-ref">Cross-validation on diabetes Dataset Exercise</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/exercises/plot_cv_diabetes.py</span></code>)</p></td>
<td><p>00:00.516</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py"><span class="std std-ref">Comparing random forests and the multi-output meta estimator</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_random_forest_regression_multioutput.py</span></code>)</p></td>
<td><p>00:00.515</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/cluster/plot_segmentation_toy.html#sphx-glr-auto-examples-cluster-plot-segmentation-toy-py"><span class="std std-ref">Spectral clustering for image segmentation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_segmentation_toy.py</span></code>)</p></td>
<td><p>00:00.514</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/svm/plot_weighted_samples.html#sphx-glr-auto-examples-svm-plot-weighted-samples-py"><span class="std std-ref">SVM: Weighted samples</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_weighted_samples.py</span></code>)</p></td>
<td><p>00:00.506</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.html#sphx-glr-auto-examples-cluster-plot-feature-agglomeration-vs-univariate-selection-py"><span class="std std-ref">Feature agglomeration vs. univariate selection</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py</span></code>)</p></td>
<td><p>00:00.499</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_lasso_and_elasticnet.html#sphx-glr-auto-examples-linear-model-plot-lasso-and-elasticnet-py"><span class="std std-ref">L1-based models for Sparse Signals</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_lasso_and_elasticnet.py</span></code>)</p></td>
<td><p>00:00.490</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/feature_selection/plot_rfe_with_cross_validation.html#sphx-glr-auto-examples-feature-selection-plot-rfe-with-cross-validation-py"><span class="std std-ref">Recursive feature elimination with cross-validation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/feature_selection/plot_rfe_with_cross_validation.py</span></code>)</p></td>
<td><p>00:00.476</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits active learning</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/semi_supervised/plot_label_propagation_digits_active_learning.py</span></code>)</p></td>
<td><p>00:00.474</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/gaussian_process/plot_gpr_noisy_targets.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-noisy-targets-py"><span class="std std-ref">Gaussian Processes regression: basic introductory example</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_gpr_noisy_targets.py</span></code>)</p></td>
<td><p>00:00.474</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/gaussian_process/plot_gpc_xor.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-xor-py"><span class="std std-ref">Illustration of Gaussian process classification (GPC) on the XOR dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_gpc_xor.py</span></code>)</p></td>
<td><p>00:00.466</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/mixture/plot_gmm_sin.html#sphx-glr-auto-examples-mixture-plot-gmm-sin-py"><span class="std std-ref">Gaussian Mixture Model Sine Curve</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/mixture/plot_gmm_sin.py</span></code>)</p></td>
<td><p>00:00.456</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py"><span class="std std-ref">Post pruning decision trees with cost complexity pruning</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/tree/plot_cost_complexity_pruning.py</span></code>)</p></td>
<td><p>00:00.455</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_adaboost_regression.html#sphx-glr-auto-examples-ensemble-plot-adaboost-regression-py"><span class="std std-ref">Decision Tree Regression with AdaBoost</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_adaboost_regression.py</span></code>)</p></td>
<td><p>00:00.450</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/covariance/plot_covariance_estimation.html#sphx-glr-auto-examples-covariance-plot-covariance-estimation-py"><span class="std std-ref">Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/covariance/plot_covariance_estimation.py</span></code>)</p></td>
<td><p>00:00.449</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_logistic_l1_l2_sparsity.html#sphx-glr-auto-examples-linear-model-plot-logistic-l1-l2-sparsity-py"><span class="std std-ref">L1 Penalty and Sparsity in Logistic Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_logistic_l1_l2_sparsity.py</span></code>)</p></td>
<td><p>00:00.444</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/classification/plot_digits_classification.html#sphx-glr-auto-examples-classification-plot-digits-classification-py"><span class="std std-ref">Recognizing hand-written digits</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/classification/plot_digits_classification.py</span></code>)</p></td>
<td><p>00:00.439</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/decomposition/plot_varimax_fa.html#sphx-glr-auto-examples-decomposition-plot-varimax-fa-py"><span class="std std-ref">Factor Analysis (with rotation) to visualize patterns</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/decomposition/plot_varimax_fa.py</span></code>)</p></td>
<td><p>00:00.439</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/classification/plot_lda_qda.html#sphx-glr-auto-examples-classification-plot-lda-qda-py"><span class="std std-ref">Linear and Quadratic Discriminant Analysis with covariance ellipsoid</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/classification/plot_lda_qda.py</span></code>)</p></td>
<td><p>00:00.438</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py"><span class="std std-ref">IsolationForest example</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_isolation_forest.py</span></code>)</p></td>
<td><p>00:00.436</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_polynomial_interpolation.html#sphx-glr-auto-examples-linear-model-plot-polynomial-interpolation-py"><span class="std std-ref">Polynomial and Spline interpolation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_polynomial_interpolation.py</span></code>)</p></td>
<td><p>00:00.432</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/decomposition/plot_ica_blind_source_separation.html#sphx-glr-auto-examples-decomposition-plot-ica-blind-source-separation-py"><span class="std std-ref">Blind source separation using FastICA</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/decomposition/plot_ica_blind_source_separation.py</span></code>)</p></td>
<td><p>00:00.421</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/svm/plot_svm_kernels.html#sphx-glr-auto-examples-svm-plot-svm-kernels-py"><span class="std std-ref">Plot classification boundaries with different SVM Kernels</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_svm_kernels.py</span></code>)</p></td>
<td><p>00:00.417</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/cluster/plot_mean_shift.html#sphx-glr-auto-examples-cluster-plot-mean-shift-py"><span class="std std-ref">A demo of the mean-shift clustering algorithm</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_mean_shift.py</span></code>)</p></td>
<td><p>00:00.404</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/svm/plot_svm_regression.html#sphx-glr-auto-examples-svm-plot-svm-regression-py"><span class="std std-ref">Support Vector Regression (SVR) using linear and non-linear kernels</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_svm_regression.py</span></code>)</p></td>
<td><p>00:00.400</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.html#sphx-glr-auto-examples-linear-model-plot-sgdocsvm-vs-ocsvm-py"><span class="std std-ref">One-Class SVM versus One-Class SVM using Stochastic Gradient Descent</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_sgdocsvm_vs_ocsvm.py</span></code>)</p></td>
<td><p>00:00.394</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/compose/plot_feature_union.html#sphx-glr-auto-examples-compose-plot-feature-union-py"><span class="std std-ref">Concatenating multiple feature extraction methods</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/compose/plot_feature_union.py</span></code>)</p></td>
<td><p>00:00.390</p></td>