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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>25:32.931</strong> total execution time for 281 files <strong>from all galleries</strong>:</p>
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<tr class="row-odd"><th class="head"><p>Example</p></th>
<th class="head"><p>Time</p></th>
<th class="head"><p>Mem (MB)</p></th>
</tr>
</thead>
<tbody>
<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>01:17.695</p></td>
<td><p>0.0</p></td>
</tr>
<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>01:06.323</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_cost_sensitive_learning.html#sphx-glr-auto-examples-model-selection-plot-cost-sensitive-learning-py"><span class="std std-ref">Post-tuning the decision threshold for cost-sensitive learning</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_cost_sensitive_learning.py</span></code>)</p></td>
<td><p>00:58.898</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>00:57.589</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:51.332</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:42.884</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>00:36.823</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_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>00:34.984</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_tuned_decision_threshold.html#sphx-glr-auto-examples-model-selection-plot-tuned-decision-threshold-py"><span class="std std-ref">Post-hoc tuning the cut-off point of decision function</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_tuned_decision_threshold.py</span></code>)</p></td>
<td><p>00:33.308</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_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>00:32.990</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>00:31.037</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_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>00:28.903</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:26.760</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>00:26.481</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:25.547</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:24.343</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_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>00:24.317</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>00:24.107</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_hgbt_regression.html#sphx-glr-auto-examples-ensemble-plot-hgbt-regression-py"><span class="std std-ref">Features in Histogram Gradient Boosting Trees</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_hgbt_regression.py</span></code>)</p></td>
<td><p>00:23.187</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:22.639</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>00:22.141</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:20.311</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>00:19.632</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>00:19.168</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>00:18.795</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:18.491</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:17.879</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>00:15.176</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_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>00:15.062</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>00:14.993</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:14.416</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>00:13.889</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>00:13.646</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:12.803</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_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>00:11.407</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>00:11.037</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:10.519</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_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>00:10.495</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:10.492</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>00:10.403</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_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>00:10.237</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>00:10.085</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_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>00:09.736</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:09.715</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>00:08.972</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>00:08.840</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_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>00:08.640</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:08.592</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>00:08.495</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:08.446</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_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>00:08.411</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>00:08.403</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>00:08.277</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:07.911</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>00:07.712</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_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>00:07.687</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>00:07.420</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:07.330</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>00:07.001</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>00:06.944</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_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>00:06.649</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>00:06.555</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:06.552</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>00:06.478</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>00:06.381</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_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>00:06.326</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:06.256</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:06.220</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>00:05.999</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>00:05.942</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>00:05.807</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>00:05.641</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">Effect of model regularization on training and 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>00:05.496</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.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>00:05.468</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_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>00:05.454</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>00:05.308</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>00:05.278</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_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>00:05.146</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:04.748</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_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>00:04.644</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_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>00:04.586</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:04.565</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>00:04.543</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:04.258</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:04.050</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:03.644</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_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>00:03.631</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>00:03.597</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>00:03.590</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:03.466</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:03.465</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:03.273</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:03.199</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:03.082</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_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>00:03.081</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:03.074</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/decomposition/plot_pca_iris.html#sphx-glr-auto-examples-decomposition-plot-pca-iris-py"><span class="std std-ref">Principal Component Analysis (PCA) on Iris Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/decomposition/plot_pca_iris.py</span></code>)</p></td>
<td><p>00:02.996</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_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>00:02.917</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:02.807</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:02.780</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:02.712</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>00:02.641</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>00:02.600</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>00:02.563</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_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>00:02.404</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:02.341</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.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>00:02.298</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:02.263</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:02.188</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:02.146</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>00:02.095</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>00:02.066</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>00:02.044</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>00:01.971</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_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>00:01.922</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:01.834</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>00:01.794</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>00:01.768</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:01.760</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/miscellaneous/plot_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>00:01.753</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>00:01.709</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:01.631</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>00:01.595</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_5_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-5-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.5</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_1_5_0.py</span></code>)</p></td>
<td><p>00:01.568</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:01.563</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_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>00:01.473</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>00:01.455</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>00:01.451</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:01.435</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:01.424</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>00:01.394</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>00:01.387</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>00:01.383</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>00:01.350</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>00:01.340</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>00:01.339</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:01.331</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_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>00:01.330</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>00:01.280</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_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>00:01.158</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_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>00:01.155</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>00:01.150</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_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>00:01.099</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_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>00:01.094</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>00:01.054</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>00:01.053</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:01.033</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_lasso_lasso_lars_elasticnet_path.html#sphx-glr-auto-examples-linear-model-plot-lasso-lasso-lars-elasticnet-path-py"><span class="std std-ref">Lasso, Lasso-LARS, and Elastic Net paths</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.py</span></code>)</p></td>
<td><p>00:01.028</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/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>00:00.992</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_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>00:00.989</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>00:00.986</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>00:00.935</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:00.861</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_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>00:00.776</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_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>00:00.761</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>00:00.747</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>00:00.731</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>00:00.710</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>00:00.705</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:00.694</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>00:00.673</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_logistic_multinomial.html#sphx-glr-auto-examples-linear-model-plot-logistic-multinomial-py"><span class="std std-ref">Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_logistic_multinomial.py</span></code>)</p></td>
<td><p>00:00.671</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>00:00.636</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_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>00:00.619</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:00.608</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>00:00.600</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>00:00.593</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:00.590</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:00.584</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_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>00:00.574</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:00.563</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>00:00.557</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>00:00.553</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>00:00.553</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:00.548</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>00:00.548</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:00.534</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_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>00:00.531</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:00.520</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:00.511</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_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>00:00.506</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:00.493</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:00.480</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>00:00.474</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>00:00.467</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:00.463</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:00.451</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>00:00.447</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>00:00.441</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>00:00.438</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_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py"><span class="std std-ref">Precision-Recall</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_precision_recall.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/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>00:00.436</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>00:00.428</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>00:00.425</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:00.416</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_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>00:00.414</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_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>00:00.409</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_anova.html#sphx-glr-auto-examples-svm-plot-svm-anova-py"><span class="std std-ref">SVM-Anova: SVM with univariate feature selection</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_svm_anova.py</span></code>)</p></td>
<td><p>00:00.399</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_vs_pca.html#sphx-glr-auto-examples-decomposition-plot-ica-vs-pca-py"><span class="std std-ref">FastICA on 2D point clouds</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/decomposition/plot_ica_vs_pca.py</span></code>)</p></td>
<td><p>00:00.397</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_voting_probas.html#sphx-glr-auto-examples-ensemble-plot-voting-probas-py"><span class="std std-ref">Plot class probabilities calculated by the VotingClassifier</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_voting_probas.py</span></code>)</p></td>
<td><p>00:00.384</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/applications/plot_outlier_detection_wine.html#sphx-glr-auto-examples-applications-plot-outlier-detection-wine-py"><span class="std std-ref">Outlier detection on a real data set</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_outlier_detection_wine.py</span></code>)</p></td>
<td><p>00:00.375</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/tree/plot_tree_regression.html#sphx-glr-auto-examples-tree-plot-tree-regression-py"><span class="std std-ref">Decision Tree Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/tree/plot_tree_regression.py</span></code>)</p></td>
<td><p>00:00.375</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>00:00.374</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_ridge_path.html#sphx-glr-auto-examples-linear-model-plot-ridge-path-py"><span class="std std-ref">Plot Ridge coefficients as a function of the regularization</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_ridge_path.py</span></code>)</p></td>
<td><p>00:00.357</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/preprocessing/plot_target_encoder_cross_val.html#sphx-glr-auto-examples-preprocessing-plot-target-encoder-cross-val-py"><span class="std std-ref">Target Encoder’s Internal Cross fitting</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/preprocessing/plot_target_encoder_cross_val.py</span></code>)</p></td>
<td><p>00:00.355</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/cluster/plot_ward_structured_vs_unstructured.html#sphx-glr-auto-examples-cluster-plot-ward-structured-vs-unstructured-py"><span class="std std-ref">Hierarchical clustering: structured vs unstructured ward</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_ward_structured_vs_unstructured.py</span></code>)</p></td>
<td><p>00:00.354</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.html#sphx-glr-auto-examples-calibration-plot-calibration-py"><span class="std std-ref">Probability calibration of classifiers</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/calibration/plot_calibration.py</span></code>)</p></td>
<td><p>00:00.351</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_coclustering.html#sphx-glr-auto-examples-bicluster-plot-spectral-coclustering-py"><span class="std std-ref">A demo of the Spectral Co-Clustering algorithm</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/bicluster/plot_spectral_coclustering.py</span></code>)</p></td>
<td><p>00:00.347</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/semi_supervised/plot_label_propagation_digits.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-py"><span class="std std-ref">Label Propagation digits: Demonstrating performance</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/semi_supervised/plot_label_propagation_digits.py</span></code>)</p></td>
<td><p>00:00.346</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_ward_segmentation.html#sphx-glr-auto-examples-cluster-plot-coin-ward-segmentation-py"><span class="std std-ref">A demo of structured Ward hierarchical clustering on an image of coins</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_coin_ward_segmentation.py</span></code>)</p></td>
<td><p>00:00.344</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_random_forest_embedding.html#sphx-glr-auto-examples-ensemble-plot-random-forest-embedding-py"><span class="std std-ref">Hashing feature transformation using Totally Random Trees</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_random_forest_embedding.py</span></code>)</p></td>
<td><p>00:00.338</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/covariance/plot_mahalanobis_distances.html#sphx-glr-auto-examples-covariance-plot-mahalanobis-distances-py"><span class="std std-ref">Robust covariance estimation and Mahalanobis distances relevance</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/covariance/plot_mahalanobis_distances.py</span></code>)</p></td>
<td><p>00:00.311</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/cluster/plot_affinity_propagation.html#sphx-glr-auto-examples-cluster-plot-affinity-propagation-py"><span class="std std-ref">Demo of affinity propagation clustering algorithm</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_affinity_propagation.py</span></code>)</p></td>
<td><p>00:00.302</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_bayesian_ridge_curvefit.html#sphx-glr-auto-examples-linear-model-plot-bayesian-ridge-curvefit-py"><span class="std std-ref">Curve Fitting with Bayesian Ridge Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_bayesian_ridge_curvefit.py</span></code>)</p></td>
<td><p>00:00.288</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_sgd_penalties.html#sphx-glr-auto-examples-linear-model-plot-sgd-penalties-py"><span class="std std-ref">SGD: Penalties</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_sgd_penalties.py</span></code>)</p></td>
<td><p>00:00.281</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_ols_ridge_variance.html#sphx-glr-auto-examples-linear-model-plot-ols-ridge-variance-py"><span class="std std-ref">Ordinary Least Squares and Ridge Regression Variance</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_ols_ridge_variance.py</span></code>)</p></td>
<td><p>00:00.276</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/decomposition/plot_sparse_coding.html#sphx-glr-auto-examples-decomposition-plot-sparse-coding-py"><span class="std std-ref">Sparse coding with a precomputed dictionary</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/decomposition/plot_sparse_coding.py</span></code>)</p></td>
<td><p>00:00.275</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.html#sphx-glr-auto-examples-preprocessing-plot-discretization-py"><span class="std std-ref">Using KBinsDiscretizer to discretize continuous features</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/preprocessing/plot_discretization.py</span></code>)</p></td>
<td><p>00:00.270</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/decomposition/plot_incremental_pca.html#sphx-glr-auto-examples-decomposition-plot-incremental-pca-py"><span class="std std-ref">Incremental PCA</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/decomposition/plot_incremental_pca.py</span></code>)</p></td>
<td><p>00:00.265</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_f_test_vs_mi.html#sphx-glr-auto-examples-feature-selection-plot-f-test-vs-mi-py"><span class="std std-ref">Comparison of F-test and mutual information</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/feature_selection/plot_f_test_vs_mi.py</span></code>)</p></td>
<td><p>00:00.250</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_underfitting_overfitting.html#sphx-glr-auto-examples-model-selection-plot-underfitting-overfitting-py"><span class="std std-ref">Underfitting vs. Overfitting</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_underfitting_overfitting.py</span></code>)</p></td>
<td><p>00:00.250</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_multi_task_lasso_support.html#sphx-glr-auto-examples-linear-model-plot-multi-task-lasso-support-py"><span class="std std-ref">Joint feature selection with multi-task Lasso</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_multi_task_lasso_support.py</span></code>)</p></td>
<td><p>00:00.248</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/svm/plot_iris_svc.html#sphx-glr-auto-examples-svm-plot-iris-svc-py"><span class="std std-ref">Plot different SVM classifiers in the iris dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_iris_svc.py</span></code>)</p></td>
<td><p>00:00.232</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_on_structured_data.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-on-structured-data-py"><span class="std std-ref">Gaussian processes on discrete data structures</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_gpr_on_structured_data.py</span></code>)</p></td>
<td><p>00:00.224</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/neighbors/plot_regression.html#sphx-glr-auto-examples-neighbors-plot-regression-py"><span class="std std-ref">Nearest Neighbors regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_regression.py</span></code>)</p></td>
<td><p>00:00.222</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_roc_crossval.html#sphx-glr-auto-examples-model-selection-plot-roc-crossval-py"><span class="std std-ref">Receiver Operating Characteristic (ROC) with cross validation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_roc_crossval.py</span></code>)</p></td>
<td><p>00:00.218</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_feature_selection.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-py"><span class="std std-ref">Univariate Feature Selection</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/feature_selection/plot_feature_selection.py</span></code>)</p></td>
<td><p>00:00.217</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/mixture/plot_gmm_covariances.html#sphx-glr-auto-examples-mixture-plot-gmm-covariances-py"><span class="std std-ref">GMM covariances</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/mixture/plot_gmm_covariances.py</span></code>)</p></td>
<td><p>00:00.210</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.html#sphx-glr-auto-examples-mixture-plot-gmm-py"><span class="std std-ref">Gaussian Mixture Model Ellipsoids</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/mixture/plot_gmm.py</span></code>)</p></td>
<td><p>00:00.208</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_compare_cross_decomposition.html#sphx-glr-auto-examples-cross-decomposition-plot-compare-cross-decomposition-py"><span class="std std-ref">Compare cross decomposition methods</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cross_decomposition/plot_compare_cross_decomposition.py</span></code>)</p></td>
<td><p>00:00.207</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_omp.html#sphx-glr-auto-examples-linear-model-plot-omp-py"><span class="std std-ref">Orthogonal Matching Pursuit</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_omp.py</span></code>)</p></td>
<td><p>00:00.202</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_cv_predict.html#sphx-glr-auto-examples-model-selection-plot-cv-predict-py"><span class="std std-ref">Plotting Cross-Validated Predictions</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_cv_predict.py</span></code>)</p></td>
<td><p>00:00.200</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_det.html#sphx-glr-auto-examples-model-selection-plot-det-py"><span class="std std-ref">Detection error tradeoff (DET) curve</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_det.py</span></code>)</p></td>
<td><p>00:00.199</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/decomposition/plot_pca_vs_lda.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-lda-py"><span class="std std-ref">Comparison of LDA and PCA 2D projection of Iris dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/decomposition/plot_pca_vs_lda.py</span></code>)</p></td>
<td><p>00:00.199</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/svm/plot_linearsvc_support_vectors.html#sphx-glr-auto-examples-svm-plot-linearsvc-support-vectors-py"><span class="std std-ref">Plot the support vectors in LinearSVC</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_linearsvc_support_vectors.py</span></code>)</p></td>
<td><p>00:00.196</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/manifold/plot_mds.html#sphx-glr-auto-examples-manifold-plot-mds-py"><span class="std std-ref">Multi-dimensional scaling</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/manifold/plot_mds.py</span></code>)</p></td>
<td><p>00:00.189</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_isoprobability.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-isoprobability-py"><span class="std std-ref">Iso-probability lines for Gaussian Processes classification (GPC)</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_gpc_isoprobability.py</span></code>)</p></td>
<td><p>00:00.186</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/miscellaneous/plot_multilabel.html#sphx-glr-auto-examples-miscellaneous-plot-multilabel-py"><span class="std std-ref">Multilabel classification</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_multilabel.py</span></code>)</p></td>
<td><p>00:00.186</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_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py"><span class="std std-ref">Confusion matrix</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_confusion_matrix.py</span></code>)</p></td>
<td><p>00:00.184</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_structure.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-structure-py"><span class="std std-ref">Label Propagation learning a complex structure</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/semi_supervised/plot_label_propagation_structure.py</span></code>)</p></td>
<td><p>00:00.182</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/cluster/plot_mini_batch_kmeans.html#sphx-glr-auto-examples-cluster-plot-mini-batch-kmeans-py"><span class="std std-ref">Comparison of the K-Means and MiniBatchKMeans clustering algorithms</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_mini_batch_kmeans.py</span></code>)</p></td>
<td><p>00:00.176</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/neighbors/plot_nearest_centroid.html#sphx-glr-auto-examples-neighbors-plot-nearest-centroid-py"><span class="std std-ref">Nearest Centroid Classification</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_nearest_centroid.py</span></code>)</p></td>
<td><p>00:00.168</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_set_output.py</span></code>)</p></td>
<td><p>00:00.168</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/svm/plot_separating_hyperplane_unbalanced.html#sphx-glr-auto-examples-svm-plot-separating-hyperplane-unbalanced-py"><span class="std std-ref">SVM: Separating hyperplane for unbalanced classes</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_separating_hyperplane_unbalanced.py</span></code>)</p></td>
<td><p>00:00.168</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/miscellaneous/plot_isotonic_regression.html#sphx-glr-auto-examples-miscellaneous-plot-isotonic-regression-py"><span class="std std-ref">Isotonic Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_isotonic_regression.py</span></code>)</p></td>
<td><p>00:00.165</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/cluster/plot_dbscan.html#sphx-glr-auto-examples-cluster-plot-dbscan-py"><span class="std std-ref">Demo of DBSCAN clustering algorithm</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_dbscan.py</span></code>)</p></td>
<td><p>00:00.162</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/datasets/plot_random_multilabel_dataset.html#sphx-glr-auto-examples-datasets-plot-random-multilabel-dataset-py"><span class="std std-ref">Plot randomly generated multilabel dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/datasets/plot_random_multilabel_dataset.py</span></code>)</p></td>
<td><p>00:00.158</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/miscellaneous/plot_roc_curve_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-roc-curve-visualization-api-py"><span class="std std-ref">ROC Curve with Visualization API</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_roc_curve_visualization_api.py</span></code>)</p></td>
<td><p>00:00.157</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_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py"><span class="std std-ref">Ordinary Least Squares Example</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_ols.py</span></code>)</p></td>
<td><p>00:00.156</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_illustration.html#sphx-glr-auto-examples-neighbors-plot-nca-illustration-py"><span class="std std-ref">Neighborhood Components Analysis Illustration</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_nca_illustration.py</span></code>)</p></td>
<td><p>00:00.156</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_agglomeration.html#sphx-glr-auto-examples-cluster-plot-digits-agglomeration-py"><span class="std std-ref">Feature agglomeration</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_digits_agglomeration.py</span></code>)</p></td>
<td><p>00:00.141</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/svm/plot_oneclass.html#sphx-glr-auto-examples-svm-plot-oneclass-py"><span class="std std-ref">One-class SVM with non-linear kernel (RBF)</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_oneclass.py</span></code>)</p></td>
<td><p>00:00.141</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_pdf.html#sphx-glr-auto-examples-mixture-plot-gmm-pdf-py"><span class="std std-ref">Density Estimation for a Gaussian mixture</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/mixture/plot_gmm_pdf.py</span></code>)</p></td>
<td><p>00:00.126</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_logistic.html#sphx-glr-auto-examples-linear-model-plot-logistic-py"><span class="std std-ref">Logistic function</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_logistic.py</span></code>)</p></td>
<td><p>00:00.115</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_path.html#sphx-glr-auto-examples-linear-model-plot-logistic-path-py"><span class="std std-ref">Regularization path of L1- Logistic Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_logistic_path.py</span></code>)</p></td>
<td><p>00:00.114</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_iris.html#sphx-glr-auto-examples-linear-model-plot-sgd-iris-py"><span class="std std-ref">Plot multi-class SGD on the iris dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_sgd_iris.py</span></code>)</p></td>
<td><p>00:00.113</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_ransac.html#sphx-glr-auto-examples-linear-model-plot-ransac-py"><span class="std std-ref">Robust linear model estimation using RANSAC</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_ransac.py</span></code>)</p></td>
<td><p>00:00.111</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_6_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-6-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.6</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_1_6_0.py</span></code>)</p></td>
<td><p>00:00.111</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_huber_vs_ridge.html#sphx-glr-auto-examples-linear-model-plot-huber-vs-ridge-py"><span class="std std-ref">HuberRegressor vs Ridge on dataset with strong outliers</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_huber_vs_ridge.py</span></code>)</p></td>
<td><p>00:00.103</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/svm/plot_custom_kernel.html#sphx-glr-auto-examples-svm-plot-custom-kernel-py"><span class="std std-ref">SVM with custom kernel</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_custom_kernel.py</span></code>)</p></td>
<td><p>00:00.103</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_lasso_lars_ic.html#sphx-glr-auto-examples-linear-model-plot-lasso-lars-ic-py"><span class="std std-ref">Lasso model selection via information criteria</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_lasso_lars_ic.py</span></code>)</p></td>
<td><p>00:00.101</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_dendrogram.html#sphx-glr-auto-examples-cluster-plot-agglomerative-dendrogram-py"><span class="std std-ref">Plot Hierarchical Clustering Dendrogram</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_agglomerative_dendrogram.py</span></code>)</p></td>
<td><p>00:00.100</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_sgd_loss_functions.html#sphx-glr-auto-examples-linear-model-plot-sgd-loss-functions-py"><span class="std std-ref">SGD: convex loss functions</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_sgd_loss_functions.py</span></code>)</p></td>
<td><p>00:00.098</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/miscellaneous/plot_pipeline_display.html#sphx-glr-auto-examples-miscellaneous-plot-pipeline-display-py"><span class="std std-ref">Displaying Pipelines</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_pipeline_display.py</span></code>)</p></td>
<td><p>00:00.098</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_margin.html#sphx-glr-auto-examples-svm-plot-svm-margin-py"><span class="std std-ref">SVM Margins Example</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_svm_margin.py</span></code>)</p></td>
<td><p>00:00.084</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/tree/plot_unveil_tree_structure.html#sphx-glr-auto-examples-tree-plot-unveil-tree-structure-py"><span class="std std-ref">Understanding the decision tree structure</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/tree/plot_unveil_tree_structure.py</span></code>)</p></td>
<td><p>00:00.083</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_outlier_detection.html#sphx-glr-auto-examples-neighbors-plot-lof-outlier-detection-py"><span class="std std-ref">Outlier detection with Local Outlier Factor (LOF)</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/plot_lof_outlier_detection.py</span></code>)</p></td>
<td><p>00:00.083</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_separating_hyperplane.html#sphx-glr-auto-examples-linear-model-plot-sgd-separating-hyperplane-py"><span class="std std-ref">SGD: Maximum margin separating hyperplane</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_sgd_separating_hyperplane.py</span></code>)</p></td>
<td><p>00:00.079</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_sgd_weighted_samples.html#sphx-glr-auto-examples-linear-model-plot-sgd-weighted-samples-py"><span class="std std-ref">SGD: Weighted samples</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_sgd_weighted_samples.py</span></code>)</p></td>
<td><p>00:00.077</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/svm/plot_separating_hyperplane.html#sphx-glr-auto-examples-svm-plot-separating-hyperplane-py"><span class="std std-ref">SVM: Maximum margin separating hyperplane</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_separating_hyperplane.py</span></code>)</p></td>
<td><p>00:00.071</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_nnls.html#sphx-glr-auto-examples-linear-model-plot-nnls-py"><span class="std std-ref">Non-negative least squares</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_nnls.py</span></code>)</p></td>
<td><p>00:00.069</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_plusplus.html#sphx-glr-auto-examples-cluster-plot-kmeans-plusplus-py"><span class="std std-ref">An example of K-Means++ initialization</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_kmeans_plusplus.py</span></code>)</p></td>
<td><p>00:00.061</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/miscellaneous/plot_metadata_routing.html#sphx-glr-auto-examples-miscellaneous-plot-metadata-routing-py"><span class="std std-ref">Metadata Routing</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_metadata_routing.py</span></code>)</p></td>
<td><p>00:00.043</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/miscellaneous/plot_estimator_representation.html#sphx-glr-auto-examples-miscellaneous-plot-estimator-representation-py"><span class="std std-ref">Displaying estimators and complex pipelines</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/miscellaneous/plot_estimator_representation.py</span></code>)</p></td>
<td><p>00:00.028</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_feature_selection_pipeline.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-pipeline-py"><span class="std std-ref">Pipeline ANOVA SVM</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/feature_selection/plot_feature_selection_pipeline.py</span></code>)</p></td>
<td><p>00:00.020</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/frozen/plot_frozen_examples.html#sphx-glr-auto-examples-frozen-plot-frozen-examples-py"><span class="std std-ref">Examples of Using FrozenEstimator</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/frozen/plot_frozen_examples.py</span></code>)</p></td>
<td><p>00:00.020</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_0_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-0-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.0</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_1_0_0.py</span></code>)</p></td>
<td><p>00:00.015</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/applications/wikipedia_principal_eigenvector.html#sphx-glr-auto-examples-applications-wikipedia-principal-eigenvector-py"><span class="std std-ref">Wikipedia principal eigenvector</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/wikipedia_principal_eigenvector.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/developing_estimators/sklearn_is_fitted.html#sphx-glr-auto-examples-developing-estimators-sklearn-is-fitted-py"><span class="std std-ref">__sklearn_is_fitted__ as Developer API</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/developing_estimators/sklearn_is_fitted.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/neighbors/approximate_nearest_neighbors.html#sphx-glr-auto-examples-neighbors-approximate-nearest-neighbors-py"><span class="std std-ref">Approximate nearest neighbors in TSNE</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neighbors/approximate_nearest_neighbors.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
</tbody>
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