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scikit-learn 1.3.2
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# Computation times

01:02.848 total execution time for auto_examples_miscellaneous files:

Evaluation of outlier detection estimators (`plot_outlier_detection_bench.py`) | 00:35.152 | 0.0 MB
The Johnson-Lindenstrauss bound for embedding with random projections (`plot_johnson_lindenstrauss_bound.py`) | 00:10.608 | 0.0 MB
Comparison of kernel ridge regression and SVR (`plot_kernel_ridge_regression.py`) | 00:04.819 | 0.0 MB
Visualizations with Display Objects (`plot_display_object_visualization.py`) | 00:03.535 | 0.0 MB
Comparing anomaly detection algorithms for outlier detection on toy datasets (`plot_anomaly_comparison.py`) | 00:02.565 | 0.0 MB
Advanced Plotting With Partial Dependence (`plot_partial_dependence_visualization_api.py`) | 00:02.267 | 0.0 MB
Explicit feature map approximation for RBF kernels (`plot_kernel_approximation.py`) | 00:02.144 | 0.0 MB
Face completion with a multi-output estimators (`plot_multioutput_face_completion.py`) | 00:01.211 | 0.0 MB
Multilabel classification (`plot_multilabel.py`) | 00:00.127 | 0.0 MB
ROC Curve with Visualization API (`plot_roc_curve_visualization_api.py`) | 00:00.103 | 0.0 MB
Isotonic Regression (`plot_isotonic_regression.py`) | 00:00.099 | 0.0 MB
Displaying Pipelines (`plot_pipeline_display.py`) | 00:00.087 | 0.0 MB
Introducing the set_output API (`plot_set_output.py`) | 00:00.080 | 0.0 MB
Metadata Routing (`plot_metadata_routing.py`) | 00:00.029 | 0.0 MB
Displaying estimators and complex pipelines (`plot_estimator_representation.py`) | 00:00.022 | 0.0 MB