Computation times

20:22.379 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_grad_boosting_comparison.py)	00:54.955	0.0
Model-based and sequential feature selection (../examples/feature_selection/plot_select_from_model_diabetes.py)	00:48.859	0.0
Selecting dimensionality reduction with Pipeline and GridSearchCV (../examples/compose/plot_compare_reduction.py)	00:46.425	0.0
Evaluation of outlier detection estimators (../examples/miscellaneous/plot_outlier_detection_bench.py)	00:45.845	0.0
Post-hoc tuning the cut-off point of decision function (../examples/model_selection/plot_tuned_decision_threshold.py)	00:37.089	0.0
Comparing Target Encoder with Other Encoders (../examples/preprocessing/plot_target_encoder.py)	00:28.125	0.0
Sample pipeline for text feature extraction and evaluation (../examples/model_selection/plot_grid_search_text_feature_extraction.py)	00:27.382	0.0
Post-tuning the decision threshold for cost-sensitive learning (../examples/model_selection/plot_cost_sensitive_learning.py)	00:26.961	0.0
Image denoising using dictionary learning (../examples/decomposition/plot_image_denoising.py)	00:26.295	0.0
Plotting Learning Curves and Checking Models’ Scalability (../examples/model_selection/plot_learning_curve.py)	00:24.395	0.0
Manifold learning on handwritten digits: Locally Linear Embedding, Isomap… (../examples/manifold/plot_lle_digits.py)	00:22.694	0.0
Combine predictors using stacking (../examples/ensemble/plot_stack_predictors.py)	00:22.671	0.0
Partial Dependence and Individual Conditional Expectation Plots (../examples/inspection/plot_partial_dependence.py)	00:21.336	0.0
The Johnson-Lindenstrauss bound for embedding with random projections (../examples/miscellaneous/plot_johnson_lindenstrauss_bound.py)	00:20.545	0.0
Features in Histogram Gradient Boosting Trees (../examples/ensemble/plot_hgbt_regression.py)	00:20.334	0.0
Early stopping of Stochastic Gradient Descent (../examples/linear_model/plot_sgd_early_stopping.py)	00:20.331	0.0
Scalable learning with polynomial kernel approximation (../examples/kernel_approximation/plot_scalable_poly_kernels.py)	00:20.303	0.0
Swiss Roll And Swiss-Hole Reduction (../examples/manifold/plot_swissroll.py)	00:18.884	0.0
Poisson regression and non-normal loss (../examples/linear_model/plot_poisson_regression_non_normal_loss.py)	00:18.732	0.0
Overview of multiclass training meta-estimators (../examples/multiclass/plot_multiclass_overview.py)	00:18.569	0.0
Scaling the regularization parameter for SVCs (../examples/svm/plot_svm_scale_c.py)	00:18.379	0.0
Prediction Latency (../examples/applications/plot_prediction_latency.py)	00:17.759	0.0
Demo of HDBSCAN clustering algorithm (../examples/cluster/plot_hdbscan.py)	00:16.296	0.0
Release Highlights for scikit-learn 0.24 (../examples/release_highlights/plot_release_highlights_0_24_0.py)	00:15.922	0.0
Comparison of Manifold Learning methods (../examples/manifold/plot_compare_methods.py)	00:13.803	0.0
Time-related feature engineering (../examples/applications/plot_cyclical_feature_engineering.py)	00:12.119	0.0
Test with permutations the significance of a classification score (../examples/model_selection/plot_permutation_tests_for_classification.py)	00:11.671	0.0
Common pitfalls in the interpretation of coefficients of linear models (../examples/inspection/plot_linear_model_coefficient_interpretation.py)	00:11.630	0.0
Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation (../examples/applications/plot_topics_extraction_with_nmf_lda.py)	00:10.675	0.0
Compare the effect of different scalers on data with outliers (../examples/preprocessing/plot_all_scaling.py)	00:10.671	0.0
Imputing missing values before building an estimator (../examples/impute/plot_missing_values.py)	00:10.651	0.0
Prediction Intervals for Gradient Boosting Regression (../examples/ensemble/plot_gradient_boosting_quantile.py)	00:09.830	0.0
Custom refit strategy of a grid search with cross-validation (../examples/model_selection/plot_grid_search_digits.py)	00:09.738	0.0
Compressive sensing: tomography reconstruction with L1 prior (Lasso) (../examples/applications/plot_tomography_l1_reconstruction.py)	00:09.635	0.0
Gradient Boosting Out-of-Bag estimates (../examples/ensemble/plot_gradient_boosting_oob.py)	00:09.572	0.0
Lagged features for time series forecasting (../examples/applications/plot_time_series_lagged_features.py)	00:09.460	0.0
Manifold Learning methods on a severed sphere (../examples/manifold/plot_manifold_sphere.py)	00:09.049	0.0
Semi-supervised Classification on a Text Dataset (../examples/semi_supervised/plot_semi_supervised_newsgroups.py)	00:09.010	0.0
Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification (../examples/classification/plot_lda.py)	00:08.639	0.0
Visualization of MLP weights on MNIST (../examples/neural_networks/plot_mnist_filters.py)	00:08.574	0.0
Nested versus non-nested cross-validation (../examples/model_selection/plot_nested_cross_validation_iris.py)	00:08.284	0.0
Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV (../examples/model_selection/plot_multi_metric_evaluation.py)	00:08.170	0.0
Gradient Boosting regularization (../examples/ensemble/plot_gradient_boosting_regularization.py)	00:08.146	0.0
Out-of-core classification of text documents (../examples/applications/plot_out_of_core_classification.py)	00:08.012	0.0
Comparison of kernel ridge regression and SVR (../examples/miscellaneous/plot_kernel_ridge_regression.py)	00:07.908	0.0
Faces dataset decompositions (../examples/decomposition/plot_faces_decomposition.py)	00:07.888	0.0
MNIST classification using multinomial logistic + L1 (../examples/linear_model/plot_sparse_logistic_regression_mnist.py)	00:07.204	0.0
Clustering text documents using k-means (../examples/text/plot_document_clustering.py)	00:07.133	0.0
Tweedie regression on insurance claims (../examples/linear_model/plot_tweedie_regression_insurance_claims.py)	00:07.125	0.0
Comparison between grid search and successive halving (../examples/model_selection/plot_successive_halving_heatmap.py)	00:06.908	0.0
Visualizing the stock market structure (../examples/applications/plot_stock_market.py)	00:06.886	0.0
Image denoising using kernel PCA (../examples/applications/plot_digits_denoising.py)	00:06.776	0.0
Classification of text documents using sparse features (../examples/text/plot_document_classification_20newsgroups.py)	00:06.630	0.0
Biclustering documents with the Spectral Co-clustering algorithm (../examples/bicluster/plot_bicluster_newsgroups.py)	00:06.342	0.0
Comparing different clustering algorithms on toy datasets (../examples/cluster/plot_cluster_comparison.py)	00:06.341	0.0
Plot the decision surfaces of ensembles of trees on the iris dataset (../examples/ensemble/plot_forest_iris.py)	00:06.218	0.0
Species distribution modeling (../examples/applications/plot_species_distribution_modeling.py)	00:06.110	0.0
A demo of K-Means clustering on the handwritten digits data (../examples/cluster/plot_kmeans_digits.py)	00:06.096	0.0
Faces recognition example using eigenfaces and SVMs (../examples/applications/plot_face_recognition.py)	00:05.763	0.0
Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture (../examples/mixture/plot_concentration_prior.py)	00:05.627	0.0
Imputing missing values with variants of IterativeImputer (../examples/impute/plot_iterative_imputer_variants_comparison.py)	00:05.578	0.0
Effect of varying threshold for self-training (../examples/semi_supervised/plot_self_training_varying_threshold.py)	00:05.547	0.0
Support Vector Regression (SVR) using linear and non-linear kernels (../examples/svm/plot_svm_regression.py)	00:05.509	0.0
Ability of Gaussian process regression (GPR) to estimate data noise-level (../examples/gaussian_process/plot_gpr_noisy.py)	00:05.464	0.0
Release Highlights for scikit-learn 1.2 (../examples/release_highlights/plot_release_highlights_1_2_0.py)	00:05.376	0.0
Segmenting the picture of greek coins in regions (../examples/cluster/plot_coin_segmentation.py)	00:05.158	0.0
Multiclass sparse logistic regression on 20newgroups (../examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py)	00:05.136	0.0
FeatureHasher and DictVectorizer Comparison (../examples/text/plot_hashing_vs_dict_vectorizer.py)	00:05.064	0.0
RBF SVM parameters (../examples/svm/plot_rbf_parameters.py)	00:04.954	0.0
Effect of model regularization on training and test error (../examples/model_selection/plot_train_error_vs_test_error.py)	00:04.799	0.0
Model Complexity Influence (../examples/applications/plot_model_complexity_influence.py)	00:04.708	0.0
Successive Halving Iterations (../examples/model_selection/plot_successive_halving_iterations.py)	00:04.697	0.0
Feature discretization (../examples/preprocessing/plot_discretization_classification.py)	00:04.521	0.0
Kernel Density Estimation (../examples/neighbors/plot_digits_kde_sampling.py)	00:04.370	0.0
Multi-class AdaBoosted Decision Trees (../examples/ensemble/plot_adaboost_multiclass.py)	00:04.305	0.0
Permutation Importance with Multicollinear or Correlated Features (../examples/inspection/plot_permutation_importance_multicollinear.py)	00:04.219	0.0
Permutation Importance vs Random Forest Feature Importance (MDI) (../examples/inspection/plot_permutation_importance.py)	00:04.163	0.0
Comparison of kernel ridge and Gaussian process regression (../examples/gaussian_process/plot_compare_gpr_krr.py)	00:04.139	0.0
Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR) (../examples/gaussian_process/plot_gpr_co2.py)	00:03.985	0.0
Comparing anomaly detection algorithms for outlier detection on toy datasets (../examples/miscellaneous/plot_anomaly_comparison.py)	00:03.968	0.0
Comparing randomized search and grid search for hyperparameter estimation (../examples/model_selection/plot_randomized_search.py)	00:03.968	0.0
Column Transformer with Heterogeneous Data Sources (../examples/compose/plot_column_transformer.py)	00:03.958	0.0
OOB Errors for Random Forests (../examples/ensemble/plot_ensemble_oob.py)	00:03.751	0.0
Compare BIRCH and MiniBatchKMeans (../examples/cluster/plot_birch_vs_minibatchkmeans.py)	00:03.697	0.0
Categorical Feature Support in Gradient Boosting (../examples/ensemble/plot_gradient_boosting_categorical.py)	00:03.662	0.0
t-SNE: The effect of various perplexity values on the shape (../examples/manifold/plot_t_sne_perplexity.py)	00:03.600	0.0
Kernel Density Estimate of Species Distributions (../examples/neighbors/plot_species_kde.py)	00:03.583	0.0
Comparison of Calibration of Classifiers (../examples/calibration/plot_compare_calibration.py)	00:03.328	0.0
Early stopping in Gradient Boosting (../examples/ensemble/plot_gradient_boosting_early_stopping.py)	00:03.324	0.0
Compare Stochastic learning strategies for MLPClassifier (../examples/neural_networks/plot_mlp_training_curves.py)	00:03.048	0.0
Restricted Boltzmann Machine features for digit classification (../examples/neural_networks/plot_rbm_logistic_classification.py)	00:03.028	0.0
Model selection with Probabilistic PCA and Factor Analysis (FA) (../examples/decomposition/plot_pca_vs_fa_model_selection.py)	00:02.892	0.0
Advanced Plotting With Partial Dependence (../examples/miscellaneous/plot_partial_dependence_visualization_api.py)	00:02.804	0.0
Plot classification probability (../examples/classification/plot_classification_probability.py)	00:02.803	0.0
Robust vs Empirical covariance estimate (../examples/covariance/plot_robust_vs_empirical_covariance.py)	00:02.704	0.0
Feature transformations with ensembles of trees (../examples/ensemble/plot_feature_transformation.py)	00:02.702	0.0
Recursive feature elimination (../examples/feature_selection/plot_rfe_digits.py)	00:02.674	0.0
Probability Calibration curves (../examples/calibration/plot_calibration_curve.py)	00:02.611	0.0
Map data to a normal distribution (../examples/preprocessing/plot_map_data_to_normal.py)	00:02.461	0.0
Ledoit-Wolf vs OAS estimation (../examples/covariance/plot_lw_vs_oas.py)	00:02.417	0.0
Gaussian process classification (GPC) on iris dataset (../examples/gaussian_process/plot_gpc_iris.py)	00:02.407	0.0
Classifier comparison (../examples/classification/plot_classifier_comparison.py)	00:02.371	0.0
Explicit feature map approximation for RBF kernels (../examples/miscellaneous/plot_kernel_approximation.py)	00:02.338	0.0
Release Highlights for scikit-learn 1.4 (../examples/release_highlights/plot_release_highlights_1_4_0.py)	00:02.297	0.0
Importance of Feature Scaling (../examples/preprocessing/plot_scaling_importance.py)	00:02.266	0.0
Varying regularization in Multi-layer Perceptron (../examples/neural_networks/plot_mlp_alpha.py)	00:02.237	0.0
Online learning of a dictionary of parts of faces (../examples/cluster/plot_dict_face_patches.py)	00:02.168	0.0
Inductive Clustering (../examples/cluster/plot_inductive_clustering.py)	00:02.159	0.0
Vector Quantization Example (../examples/cluster/plot_face_compress.py)	00:02.130	0.0
Comparing different hierarchical linkage methods on toy datasets (../examples/cluster/plot_linkage_comparison.py)	00:02.061	0.0
Multilabel classification using a classifier chain (../examples/multioutput/plot_classifier_chain_yeast.py)	00:02.044	0.0
Principal Component Analysis (PCA) on Iris Dataset (../examples/decomposition/plot_pca_iris.py)	00:02.026	0.0
Agglomerative clustering with and without structure (../examples/cluster/plot_agglomerative_clustering.py)	00:02.023	0.0
Probabilistic predictions with Gaussian process classification (GPC) (../examples/gaussian_process/plot_gpc.py)	00:01.883	0.0
Failure of Machine Learning to infer causal effects (../examples/inspection/plot_causal_interpretation.py)	00:01.858	0.0
Dimensionality Reduction with Neighborhood Components Analysis (../examples/neighbors/plot_nca_dim_reduction.py)	00:01.815	0.0
Class Likelihood Ratios to measure classification performance (../examples/model_selection/plot_likelihood_ratios.py)	00:01.774	0.0
Robust linear estimator fitting (../examples/linear_model/plot_robust_fit.py)	00:01.752	0.0
Face completion with a multi-output estimators (../examples/miscellaneous/plot_multioutput_face_completion.py)	00:01.690	0.0
Effect of transforming the targets in regression model (../examples/compose/plot_transformed_target.py)	00:01.666	0.0
Release Highlights for scikit-learn 1.3 (../examples/release_highlights/plot_release_highlights_1_3_0.py)	00:01.544	0.0
Probability Calibration for 3-class classification (../examples/calibration/plot_calibration_multiclass.py)	00:01.538	0.0
Column Transformer with Mixed Types (../examples/compose/plot_column_transformer_mixed_types.py)	00:01.532	0.0
Caching nearest neighbors (../examples/neighbors/plot_caching_nearest_neighbors.py)	00:01.525	0.0
Plot classification boundaries with different SVM Kernels (../examples/svm/plot_svm_kernels.py)	00:01.515	0.0
Demo of OPTICS clustering algorithm (../examples/cluster/plot_optics.py)	00:01.512	0.0
Statistical comparison of models using grid search (../examples/model_selection/plot_grid_search_stats.py)	00:01.500	0.0
Various Agglomerative Clustering on a 2D embedding of digits (../examples/cluster/plot_digits_linkage.py)	00:01.488	0.0
Empirical evaluation of the impact of k-means initialization (../examples/cluster/plot_kmeans_stability_low_dim_dense.py)	00:01.462	0.0
Illustration of prior and posterior Gaussian process for different kernels (../examples/gaussian_process/plot_gpr_prior_posterior.py)	00:01.451	0.0
Release Highlights for scikit-learn 0.22 (../examples/release_highlights/plot_release_highlights_0_22_0.py)	00:01.436	0.0
Gradient Boosting regression (../examples/ensemble/plot_gradient_boosting_regression.py)	00:01.342	0.0
Agglomerative clustering with different metrics (../examples/cluster/plot_agglomerative_clustering_metrics.py)	00:01.321	0.0
Gaussian Mixture Model Selection (../examples/mixture/plot_gmm_selection.py)	00:01.291	0.0
Demonstration of k-means assumptions (../examples/cluster/plot_kmeans_assumptions.py)	00:01.225	0.0
Bisecting K-Means and Regular K-Means Performance Comparison (../examples/cluster/plot_bisect_kmeans.py)	00:01.225	0.0
Lasso on dense and sparse data (../examples/linear_model/plot_lasso_dense_vs_sparse_data.py)	00:01.193	0.0
Visualizing cross-validation behavior in scikit-learn (../examples/model_selection/plot_cv_indices.py)	00:01.178	0.0
Adjustment for chance in clustering performance evaluation (../examples/cluster/plot_adjusted_for_chance_measures.py)	00:01.171	0.0
Single estimator versus bagging: bias-variance decomposition (../examples/ensemble/plot_bias_variance.py)	00:01.122	0.0
Selecting the number of clusters with silhouette analysis on KMeans clustering (../examples/cluster/plot_kmeans_silhouette_analysis.py)	00:01.117	0.0
Plot individual and voting regression predictions (../examples/ensemble/plot_voting_regressor.py)	00:01.112	0.0
Pipelining: chaining a PCA and a logistic regression (../examples/compose/plot_digits_pipe.py)	00:01.101	0.0
Comparing Nearest Neighbors with and without Neighborhood Components Analysis (../examples/neighbors/plot_nca_classification.py)	00:01.066	0.0
Balance model complexity and cross-validated score (../examples/model_selection/plot_grid_search_refit_callable.py)	00:01.028	0.0
Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset (../examples/semi_supervised/plot_semi_supervised_versus_svm_iris.py)	00:00.977	0.0
SVM Tie Breaking Example (../examples/svm/plot_svm_tie_breaking.py)	00:00.972	0.0
Release Highlights for scikit-learn 1.1 (../examples/release_highlights/plot_release_highlights_1_1_0.py)	00:00.950	0.0
Feature importances with a forest of trees (../examples/ensemble/plot_forest_importances.py)	00:00.920	0.0
Plot the decision surface of decision trees trained on the iris dataset (../examples/tree/plot_iris_dtc.py)	00:00.884	0.0
Quantile regression (../examples/linear_model/plot_quantile_regression.py)	00:00.826	0.0
Lasso model selection: AIC-BIC / cross-validation (../examples/linear_model/plot_lasso_model_selection.py)	00:00.823	0.0
Multiclass Receiver Operating Characteristic (ROC) (../examples/model_selection/plot_roc.py)	00:00.809	0.0
Release Highlights for scikit-learn 1.5 (../examples/release_highlights/plot_release_highlights_1_5_0.py)	00:00.759	0.0
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples (../examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py)	00:00.752	0.0
Lasso, Lasso-LARS, and Elastic Net paths (../examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.py)	00:00.752	0.0
Novelty detection with Local Outlier Factor (LOF) (../examples/neighbors/plot_lof_novelty_detection.py)	00:00.736	0.0
Demonstrating the different strategies of KBinsDiscretizer (../examples/preprocessing/plot_discretization_strategies.py)	00:00.728	0.0
Simple 1D Kernel Density Estimation (../examples/neighbors/plot_kde_1d.py)	00:00.725	0.0
Nearest Neighbors Classification (../examples/neighbors/plot_classification.py)	00:00.703	0.0
Recursive feature elimination with cross-validation (../examples/feature_selection/plot_rfe_with_cross_validation.py)	00:00.685	0.0
Two-class AdaBoost (../examples/ensemble/plot_adaboost_twoclass.py)	00:00.655	0.0
Plot the decision boundaries of a VotingClassifier (../examples/ensemble/plot_voting_decision_regions.py)	00:00.630	0.0
Ridge coefficients as a function of the L2 Regularization (../examples/linear_model/plot_ridge_coeffs.py)	00:00.628	0.0
Release Highlights for scikit-learn 0.23 (../examples/release_highlights/plot_release_highlights_0_23_0.py)	00:00.628	0.0
Label Propagation digits active learning (../examples/semi_supervised/plot_label_propagation_digits_active_learning.py)	00:00.623	0.0
Monotonic Constraints (../examples/ensemble/plot_monotonic_constraints.py)	00:00.604	0.0
Comparing Linear Bayesian Regressors (../examples/linear_model/plot_ard.py)	00:00.599	0.0
GMM Initialization Methods (../examples/mixture/plot_gmm_init.py)	00:00.595	0.0
Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression (../examples/linear_model/plot_logistic_multinomial.py)	00:00.571	0.0
A demo of the mean-shift clustering algorithm (../examples/cluster/plot_mean_shift.py)	00:00.548	0.0
Kernel PCA (../examples/decomposition/plot_kernel_pca.py)	00:00.545	0.0
Theil-Sen Regression (../examples/linear_model/plot_theilsen.py)	00:00.529	0.0
Comparing random forests and the multi-output meta estimator (../examples/ensemble/plot_random_forest_regression_multioutput.py)	00:00.525	0.0
Concatenating multiple feature extraction methods (../examples/compose/plot_feature_union.py)	00:00.525	0.0
Feature agglomeration vs. univariate selection (../examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py)	00:00.524	0.0
Principal Component Regression vs Partial Least Squares Regression (../examples/cross_decomposition/plot_pcr_vs_pls.py)	00:00.524	0.0
A demo of the Spectral Biclustering algorithm (../examples/bicluster/plot_spectral_biclustering.py)	00:00.507	0.0
Factor Analysis (with rotation) to visualize patterns (../examples/decomposition/plot_varimax_fa.py)	00:00.482	0.0
Gaussian Processes regression: basic introductory example (../examples/gaussian_process/plot_gpr_noisy_targets.py)	00:00.470	0.0
Linear and Quadratic Discriminant Analysis with covariance ellipsoid (../examples/classification/plot_lda_qda.py)	00:00.470	0.0
Precision-Recall (../examples/model_selection/plot_precision_recall.py)	00:00.465	0.0
Sparse inverse covariance estimation (../examples/covariance/plot_sparse_cov.py)	00:00.455	0.0
Post pruning decision trees with cost complexity pruning (../examples/tree/plot_cost_complexity_pruning.py)	00:00.451	0.0
IsolationForest example (../examples/ensemble/plot_isolation_forest.py)	00:00.442	0.0
Spectral clustering for image segmentation (../examples/cluster/plot_segmentation_toy.py)	00:00.441	0.0
Decision Tree Regression with AdaBoost (../examples/ensemble/plot_adaboost_regression.py)	00:00.439	0.0
Recognizing hand-written digits (../examples/classification/plot_digits_classification.py)	00:00.430	0.0
Probability calibration of classifiers (../examples/calibration/plot_calibration.py)	00:00.426	0.0
Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood (../examples/covariance/plot_covariance_estimation.py)	00:00.424	0.0
L1 Penalty and Sparsity in Logistic Regression (../examples/linear_model/plot_logistic_l1_l2_sparsity.py)	00:00.423	0.0
Polynomial and Spline interpolation (../examples/linear_model/plot_polynomial_interpolation.py)	00:00.414	0.0
Hierarchical clustering: structured vs unstructured ward (../examples/cluster/plot_ward_structured_vs_unstructured.py)	00:00.412	0.0
L1-based models for Sparse Signals (../examples/linear_model/plot_lasso_and_elasticnet.py)	00:00.409	0.0
Illustration of Gaussian process classification (GPC) on the XOR dataset (../examples/gaussian_process/plot_gpc_xor.py)	00:00.406	0.0
Visualizations with Display Objects (../examples/miscellaneous/plot_display_object_visualization.py)	00:00.401	0.0
Gaussian Mixture Model Sine Curve (../examples/mixture/plot_gmm_sin.py)	00:00.398	0.0
Ordinary Least Squares and Ridge Regression (../examples/linear_model/plot_ols_ridge.py)	00:00.398	0.0
Label Propagation digits: Demonstrating performance (../examples/semi_supervised/plot_label_propagation_digits.py)	00:00.398	0.0
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent (../examples/linear_model/plot_sgdocsvm_vs_ocsvm.py)	00:00.387	0.0
FastICA on 2D point clouds (../examples/decomposition/plot_ica_vs_pca.py)	00:00.376	0.0
Blind source separation using FastICA (../examples/decomposition/plot_ica_blind_source_separation.py)	00:00.375	0.0
Outlier detection on a real data set (../examples/applications/plot_outlier_detection_wine.py)	00:00.363	0.0
Target Encoder’s Internal Cross fitting (../examples/preprocessing/plot_target_encoder_cross_val.py)	00:00.362	0.0
A demo of structured Ward hierarchical clustering on an image of coins (../examples/cluster/plot_coin_ward_segmentation.py)	00:00.358	0.0
Plot class probabilities calculated by the VotingClassifier (../examples/ensemble/plot_voting_probas.py)	00:00.333	0.0
Hashing feature transformation using Totally Random Trees (../examples/ensemble/plot_random_forest_embedding.py)	00:00.330	0.0
A demo of the Spectral Co-Clustering algorithm (../examples/bicluster/plot_spectral_coclustering.py)	00:00.329	0.0
SVM-Anova: SVM with univariate feature selection (../examples/svm/plot_svm_anova.py)	00:00.328	0.0
Demo of affinity propagation clustering algorithm (../examples/cluster/plot_affinity_propagation.py)	00:00.322	0.0
Decision Tree Regression (../examples/tree/plot_tree_regression.py)	00:00.320	0.0
Plot Ridge coefficients as a function of the regularization (../examples/linear_model/plot_ridge_path.py)	00:00.310	0.0
SVM: Weighted samples (../examples/svm/plot_weighted_samples.py)	00:00.290	0.0
Using KBinsDiscretizer to discretize continuous features (../examples/preprocessing/plot_discretization.py)	00:00.286	0.0
Multi-dimensional scaling (../examples/manifold/plot_mds.py)	00:00.276	0.0
Robust covariance estimation and Mahalanobis distances relevance (../examples/covariance/plot_mahalanobis_distances.py)	00:00.273	0.0
Curve Fitting with Bayesian Ridge Regression (../examples/linear_model/plot_bayesian_ridge_curvefit.py)	00:00.266	0.0
Nearest Neighbors regression (../examples/neighbors/plot_regression.py)	00:00.260	0.0
Sparse coding with a precomputed dictionary (../examples/decomposition/plot_sparse_coding.py)	00:00.256	0.0
SGD: Penalties (../examples/linear_model/plot_sgd_penalties.py)	00:00.245	0.0
Plot the support vectors in LinearSVC (../examples/svm/plot_linearsvc_support_vectors.py)	00:00.241	0.0
Plotting Cross-Validated Predictions (../examples/model_selection/plot_cv_predict.py)	00:00.240	0.0
Incremental PCA (../examples/decomposition/plot_incremental_pca.py)	00:00.226	0.0
Plot different SVM classifiers in the iris dataset (../examples/svm/plot_iris_svc.py)	00:00.225	0.0
Multilabel classification (../examples/miscellaneous/plot_multilabel.py)	00:00.213	0.0
Comparison of F-test and mutual information (../examples/feature_selection/plot_f_test_vs_mi.py)	00:00.210	0.0
Gaussian processes on discrete data structures (../examples/gaussian_process/plot_gpr_on_structured_data.py)	00:00.210	0.0
Joint feature selection with multi-task Lasso (../examples/linear_model/plot_multi_task_lasso_support.py)	00:00.209	0.0
Receiver Operating Characteristic (ROC) with cross validation (../examples/model_selection/plot_roc_crossval.py)	00:00.203	0.0
Detection error tradeoff (DET) curve (../examples/model_selection/plot_det.py)	00:00.200	0.0
Comparison of LDA and PCA 2D projection of Iris dataset (../examples/decomposition/plot_pca_vs_lda.py)	00:00.199	0.0
Label Propagation learning a complex structure (../examples/semi_supervised/plot_label_propagation_structure.py)	00:00.198	0.0
Compare cross decomposition methods (../examples/cross_decomposition/plot_compare_cross_decomposition.py)	00:00.196	0.0
Nearest Centroid Classification (../examples/neighbors/plot_nearest_centroid.py)	00:00.196	0.0
Demo of DBSCAN clustering algorithm (../examples/cluster/plot_dbscan.py)	00:00.195	0.0
Gaussian Mixture Model Ellipsoids (../examples/mixture/plot_gmm.py)	00:00.194	0.0
ROC Curve with Visualization API (../examples/miscellaneous/plot_roc_curve_visualization_api.py)	00:00.193	0.0
Underfitting vs. Overfitting (../examples/model_selection/plot_underfitting_overfitting.py)	00:00.191	0.0
Comparison of the K-Means and MiniBatchKMeans clustering algorithms (../examples/cluster/plot_mini_batch_kmeans.py)	00:00.189	0.0
Orthogonal Matching Pursuit (../examples/linear_model/plot_omp.py)	00:00.185	0.0
Neighborhood Components Analysis Illustration (../examples/neighbors/plot_nca_illustration.py)	00:00.181	0.0
GMM covariances (../examples/mixture/plot_gmm_covariances.py)	00:00.181	0.0
Isotonic Regression (../examples/miscellaneous/plot_isotonic_regression.py)	00:00.179	0.0
Univariate Feature Selection (../examples/feature_selection/plot_feature_selection.py)	00:00.175	0.0
Introducing the set_output API (../examples/miscellaneous/plot_set_output.py)	00:00.174	0.0
One-class SVM with non-linear kernel (RBF) (../examples/svm/plot_oneclass.py)	00:00.167	0.0
SVM: Separating hyperplane for unbalanced classes (../examples/svm/plot_separating_hyperplane_unbalanced.py)	00:00.166	0.0
Confusion matrix (../examples/model_selection/plot_confusion_matrix.py)	00:00.161	0.0
Regularization path of L1- Logistic Regression (../examples/linear_model/plot_logistic_path.py)	00:00.160	0.0
Displaying Pipelines (../examples/miscellaneous/plot_pipeline_display.py)	00:00.150	0.0
Plot randomly generated multilabel dataset (../examples/datasets/plot_random_multilabel_dataset.py)	00:00.134	0.0
Iso-probability lines for Gaussian Processes classification (GPC) (../examples/gaussian_process/plot_gpc_isoprobability.py)	00:00.127	0.0
Feature agglomeration (../examples/cluster/plot_digits_agglomeration.py)	00:00.121	0.0
Release Highlights for scikit-learn 1.6 (../examples/release_highlights/plot_release_highlights_1_6_0.py)	00:00.119	0.0
Density Estimation for a Gaussian mixture (../examples/mixture/plot_gmm_pdf.py)	00:00.112	0.0
Logistic function (../examples/linear_model/plot_logistic.py)	00:00.107	0.0
Plot multi-class SGD on the iris dataset (../examples/linear_model/plot_sgd_iris.py)	00:00.106	0.0
Outlier detection with Local Outlier Factor (LOF) (../examples/neighbors/plot_lof_outlier_detection.py)	00:00.100	0.0
HuberRegressor vs Ridge on dataset with strong outliers (../examples/linear_model/plot_huber_vs_ridge.py)	00:00.099	0.0
SGD: convex loss functions (../examples/linear_model/plot_sgd_loss_functions.py)	00:00.093	0.0
SVM with custom kernel (../examples/svm/plot_custom_kernel.py)	00:00.093	0.0
Lasso model selection via information criteria (../examples/linear_model/plot_lasso_lars_ic.py)	00:00.092	0.0
Robust linear model estimation using RANSAC (../examples/linear_model/plot_ransac.py)	00:00.089	0.0
Plot Hierarchical Clustering Dendrogram (../examples/cluster/plot_agglomerative_dendrogram.py)	00:00.085	0.0
Understanding the decision tree structure (../examples/tree/plot_unveil_tree_structure.py)	00:00.085	0.0
SVM: Maximum margin separating hyperplane (../examples/svm/plot_separating_hyperplane.py)	00:00.069	0.0
SGD: Weighted samples (../examples/linear_model/plot_sgd_weighted_samples.py)	00:00.069	0.0
SGD: Maximum margin separating hyperplane (../examples/linear_model/plot_sgd_separating_hyperplane.py)	00:00.067	0.0
SVM Margins Example (../examples/svm/plot_svm_margin.py)	00:00.066	0.0
An example of K-Means++ initialization (../examples/cluster/plot_kmeans_plusplus.py)	00:00.062	0.0
Non-negative least squares (../examples/linear_model/plot_nnls.py)	00:00.061	0.0
Metadata Routing (../examples/miscellaneous/plot_metadata_routing.py)	00:00.059	0.0
Displaying estimators and complex pipelines (../examples/miscellaneous/plot_estimator_representation.py)	00:00.042	0.0
Examples of Using FrozenEstimator (../examples/frozen/plot_frozen_examples.py)	00:00.016	0.0
Release Highlights for scikit-learn 1.0 (../examples/release_highlights/plot_release_highlights_1_0_0.py)	00:00.015	0.0
Pipeline ANOVA SVM (../examples/feature_selection/plot_feature_selection_pipeline.py)	00:00.013	0.0
Wikipedia principal eigenvector (../examples/applications/wikipedia_principal_eigenvector.py)	00:00.000	0.0
__sklearn_is_fitted__ as Developer API (../examples/developing_estimators/sklearn_is_fitted.py)	00:00.000	0.0
Approximate nearest neighbors in TSNE (../examples/neighbors/approximate_nearest_neighbors.py)	00:00.000	0.0