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<!DOCTYPE html>
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<meta property="og:title" content="Computation times" />
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<meta property="og:url" content="https://scikit-learn/stable/sg_execution_times.html" />
<meta property="og:site_name" content="scikit-learn" />
<meta property="og:description" content="20:46.782 total execution time for 279 files from all galleries:,,, Example, Time, Mem (MB),,, Comparing Random Forests and Histogram Gradient Boosting models(../examples/ensemble/plot_forest_hist_..." />
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<meta name="description" content="20:46.782 total execution time for 279 files from all galleries:,,, Example, Time, Mem (MB),,, Comparing Random Forests and Histogram Gradient Boosting models(../examples/ensemble/plot_forest_hist_..." />
<title>Computation times — scikit-learn 1.7.dev0 documentation</title>
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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>20:46.782</strong> total execution time for 279 files <strong>from all galleries</strong>:</p>
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<thead>
<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/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>00:57.179</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_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>00:50.062</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>00:49.605</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>00:46.693</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>00:34.382</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>00:31.774</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>00:30.176</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>00:29.729</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>00:28.202</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/decomposition/plot_image_denoising.html#sphx-glr-auto-examples-decomposition-plot-image-denoising-py"><span class="std std-ref">Image denoising using dictionary learning</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/decomposition/plot_image_denoising.py</span></code>)</p></td>
<td><p>00:25.937</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/inspection/plot_partial_dependence.html#sphx-glr-auto-examples-inspection-plot-partial-dependence-py"><span class="std std-ref">Partial Dependence and Individual Conditional Expectation Plots</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/inspection/plot_partial_dependence.py</span></code>)</p></td>
<td><p>00:23.279</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>00:23.266</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>00:22.755</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>00:22.444</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:21.404</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>00:20.612</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>00:20.480</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_poisson_regression_non_normal_loss.html#sphx-glr-auto-examples-linear-model-plot-poisson-regression-non-normal-loss-py"><span class="std std-ref">Poisson regression and non-normal loss</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_poisson_regression_non_normal_loss.py</span></code>)</p></td>
<td><p>00:20.220</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_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>00:19.498</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/manifold/plot_swissroll.html#sphx-glr-auto-examples-manifold-plot-swissroll-py"><span class="std std-ref">Swiss Roll And Swiss-Hole Reduction</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/manifold/plot_swissroll.py</span></code>)</p></td>
<td><p>00:17.778</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>00:17.352</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/cluster/plot_hdbscan.html#sphx-glr-auto-examples-cluster-plot-hdbscan-py"><span class="std std-ref">Demo of HDBSCAN clustering algorithm</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_hdbscan.py</span></code>)</p></td>
<td><p>00:15.628</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_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>00:15.415</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_permutation_tests_for_classification.html#sphx-glr-auto-examples-model-selection-plot-permutation-tests-for-classification-py"><span class="std std-ref">Test with permutations the significance of a classification score</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_permutation_tests_for_classification.py</span></code>)</p></td>
<td><p>00:12.829</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:12.570</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/applications/plot_cyclical_feature_engineering.html#sphx-glr-auto-examples-applications-plot-cyclical-feature-engineering-py"><span class="std std-ref">Time-related feature engineering</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_cyclical_feature_engineering.py</span></code>)</p></td>
<td><p>00:12.382</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/manifold/plot_compare_methods.html#sphx-glr-auto-examples-manifold-plot-compare-methods-py"><span class="std std-ref">Comparison of Manifold Learning methods</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/manifold/plot_compare_methods.py</span></code>)</p></td>
<td><p>00:12.257</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>00:11.017</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>00:10.804</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:10.722</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_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>00:10.175</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>00:09.705</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/applications/plot_tomography_l1_reconstruction.html#sphx-glr-auto-examples-applications-plot-tomography-l1-reconstruction-py"><span class="std std-ref">Compressive sensing: tomography reconstruction with L1 prior (Lasso)</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_tomography_l1_reconstruction.py</span></code>)</p></td>
<td><p>00:09.612</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_gradient_boosting_oob.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-oob-py"><span class="std std-ref">Gradient Boosting Out-of-Bag estimates</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_gradient_boosting_oob.py</span></code>)</p></td>
<td><p>00:09.427</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>00:09.422</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/applications/plot_time_series_lagged_features.html#sphx-glr-auto-examples-applications-plot-time-series-lagged-features-py"><span class="std std-ref">Lagged features for time series forecasting</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_time_series_lagged_features.py</span></code>)</p></td>
<td><p>00:09.313</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/text/plot_document_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>00:08.768</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>00:08.502</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/miscellaneous/plot_kernel_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>00:08.114</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>00:08.075</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>00:08.056</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-mnist-py"><span class="std std-ref">MNIST classification using multinomial logistic + L1</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_sparse_logistic_regression_mnist.py</span></code>)</p></td>
<td><p>00:08.016</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/decomposition/plot_faces_decomposition.html#sphx-glr-auto-examples-decomposition-plot-faces-decomposition-py"><span class="std std-ref">Faces dataset decompositions</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/decomposition/plot_faces_decomposition.py</span></code>)</p></td>
<td><p>00:07.984</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>00:07.938</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/text/plot_document_clustering.html#sphx-glr-auto-examples-text-plot-document-clustering-py"><span class="std std-ref">Clustering text documents using k-means</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/text/plot_document_clustering.py</span></code>)</p></td>
<td><p>00:07.927</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>00:07.919</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_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>00:07.856</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/model_selection/plot_successive_halving_heatmap.html#sphx-glr-auto-examples-model-selection-plot-successive-halving-heatmap-py"><span class="std std-ref">Comparison between grid search and successive halving</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/model_selection/plot_successive_halving_heatmap.py</span></code>)</p></td>
<td><p>00:07.529</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>00:07.276</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/neural_networks/plot_mnist_filters.html#sphx-glr-auto-examples-neural-networks-plot-mnist-filters-py"><span class="std std-ref">Visualization of MLP weights on MNIST</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neural_networks/plot_mnist_filters.py</span></code>)</p></td>
<td><p>00:07.056</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>00:06.943</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_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>00:06.913</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>00:06.688</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>00:06.546</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/applications/plot_digits_denoising.html#sphx-glr-auto-examples-applications-plot-digits-denoising-py"><span class="std std-ref">Image denoising using kernel PCA</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/applications/plot_digits_denoising.py</span></code>)</p></td>
<td><p>00:06.482</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">Comparing different clustering algorithms on toy datasets</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_cluster_comparison.py</span></code>)</p></td>
<td><p>00:06.469</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_forest_iris.html#sphx-glr-auto-examples-ensemble-plot-forest-iris-py"><span class="std std-ref">Plot the decision surfaces of ensembles of trees on the iris dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_forest_iris.py</span></code>)</p></td>
<td><p>00:06.267</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>00:06.210</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:05.894</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>00:05.856</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/impute/plot_iterative_imputer_variants_comparison.html#sphx-glr-auto-examples-impute-plot-iterative-imputer-variants-comparison-py"><span class="std std-ref">Imputing missing values with variants of IterativeImputer</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/impute/plot_iterative_imputer_variants_comparison.py</span></code>)</p></td>
<td><p>00:05.838</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/mixture/plot_concentration_prior.html#sphx-glr-auto-examples-mixture-plot-concentration-prior-py"><span class="std std-ref">Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/mixture/plot_concentration_prior.py</span></code>)</p></td>
<td><p>00:05.834</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/semi_supervised/plot_self_training_varying_threshold.html#sphx-glr-auto-examples-semi-supervised-plot-self-training-varying-threshold-py"><span class="std std-ref">Effect of varying threshold for self-training</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/semi_supervised/plot_self_training_varying_threshold.py</span></code>)</p></td>
<td><p>00:05.763</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><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>00:05.694</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_20newsgroups.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-20newsgroups-py"><span class="std std-ref">Multiclass sparse logistic regression on 20newgroups</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py</span></code>)</p></td>
<td><p>00:05.690</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>00:05.648</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>00:05.550</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/release_highlights/plot_release_highlights_1_2_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-2-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.2</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_1_2_0.py</span></code>)</p></td>
<td><p>00:05.299</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>00:05.221</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>00:05.176</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/svm/plot_rbf_parameters.html#sphx-glr-auto-examples-svm-plot-rbf-parameters-py"><span class="std std-ref">RBF SVM parameters</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/svm/plot_rbf_parameters.py</span></code>)</p></td>
<td><p>00:04.965</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>00:04.773</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/inspection/plot_permutation_importance.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-py"><span class="std std-ref">Permutation Importance vs Random Forest Feature Importance (MDI)</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/inspection/plot_permutation_importance.py</span></code>)</p></td>
<td><p>00:04.567</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>00:04.437</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>00:04.391</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>00:04.270</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/gaussian_process/plot_compare_gpr_krr.html#sphx-glr-auto-examples-gaussian-process-plot-compare-gpr-krr-py"><span class="std std-ref">Comparison of kernel ridge and Gaussian process regression</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_compare_gpr_krr.py</span></code>)</p></td>
<td><p>00:04.263</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_adaboost_multiclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-multiclass-py"><span class="std std-ref">Multi-class AdaBoosted Decision Trees</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_adaboost_multiclass.py</span></code>)</p></td>
<td><p>00:04.252</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/gaussian_process/plot_gpr_co2.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-co2-py"><span class="std std-ref">Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_gpr_co2.py</span></code>)</p></td>
<td><p>00:04.178</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>00:04.130</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/cluster/plot_inductive_clustering.html#sphx-glr-auto-examples-cluster-plot-inductive-clustering-py"><span class="std std-ref">Inductive Clustering</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_inductive_clustering.py</span></code>)</p></td>
<td><p>00:03.966</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_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>00:03.886</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/cluster/plot_birch_vs_minibatchkmeans.html#sphx-glr-auto-examples-cluster-plot-birch-vs-minibatchkmeans-py"><span class="std std-ref">Compare BIRCH and MiniBatchKMeans</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/cluster/plot_birch_vs_minibatchkmeans.py</span></code>)</p></td>
<td><p>00:03.744</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_ensemble_oob.html#sphx-glr-auto-examples-ensemble-plot-ensemble-oob-py"><span class="std std-ref">OOB Errors for Random Forests</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_ensemble_oob.py</span></code>)</p></td>
<td><p>00:03.602</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><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>00:03.474</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_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>00:03.458</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>00:03.323</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>00:03.315</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>00:03.264</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/gaussian_process/plot_gpc_iris.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-iris-py"><span class="std std-ref">Gaussian process classification (GPC) on iris dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/gaussian_process/plot_gpc_iris.py</span></code>)</p></td>
<td><p>00:03.257</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/calibration/plot_compare_calibration.html#sphx-glr-auto-examples-calibration-plot-compare-calibration-py"><span class="std std-ref">Comparison of Calibration of Classifiers</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/calibration/plot_compare_calibration.py</span></code>)</p></td>
<td><p>00:03.194</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/neural_networks/plot_mlp_training_curves.html#sphx-glr-auto-examples-neural-networks-plot-mlp-training-curves-py"><span class="std std-ref">Compare Stochastic learning strategies for MLPClassifier</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/neural_networks/plot_mlp_training_curves.py</span></code>)</p></td>
<td><p>00:03.162</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/manifold/plot_t_sne_perplexity.html#sphx-glr-auto-examples-manifold-plot-t-sne-perplexity-py"><span class="std std-ref">t-SNE: The effect of various perplexity values on the shape</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/manifold/plot_t_sne_perplexity.py</span></code>)</p></td>
<td><p>00:03.082</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>00:03.026</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>00:02.867</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/ensemble/plot_feature_transformation.html#sphx-glr-auto-examples-ensemble-plot-feature-transformation-py"><span class="std std-ref">Feature transformations with ensembles of trees</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/ensemble/plot_feature_transformation.py</span></code>)</p></td>
<td><p>00:02.840</p></td>
<td><p>0.0</p></td>