Computation times#

23:44.999 total execution time for 293 files from all galleries:

Example

Time

Mem (MB)

Release Highlights for scikit-learn 0.24 (../examples/release_highlights/plot_release_highlights_0_24_0.py)

01:14.219

0.0

Post-tuning the decision threshold for cost-sensitive learning (../examples/model_selection/plot_cost_sensitive_learning.py)

01:00.760

0.0

Evaluation of outlier detection estimators (../examples/miscellaneous/plot_outlier_detection_bench.py)

00:59.575

0.0

Comparing Random Forests and Histogram Gradient Boosting models (../examples/ensemble/plot_forest_hist_grad_boosting_comparison.py)

00:59.036

0.0

Selecting dimensionality reduction with Pipeline and GridSearchCV (../examples/compose/plot_compare_reduction.py)

00:42.494

0.0

Comparing Target Encoder with Other Encoders (../examples/preprocessing/plot_target_encoder.py)

00:39.551

0.0

Model-based and sequential feature selection (../examples/feature_selection/plot_select_from_model_diabetes.py)

00:33.623

0.0

Early stopping of Stochastic Gradient Descent (../examples/linear_model/plot_sgd_early_stopping.py)

00:31.447

0.0

Image denoising using dictionary learning (../examples/decomposition/plot_image_denoising.py)

00:31.134

0.0

Post-hoc tuning the cut-off point of decision function (../examples/model_selection/plot_tuned_decision_threshold.py)

00:30.948

0.0

Out-of-core classification of text documents (../examples/applications/plot_out_of_core_classification.py)

00:28.314

0.0

Sample pipeline for text feature extraction and evaluation (../examples/model_selection/plot_grid_search_text_feature_extraction.py)

00:28.004

0.0

Faces recognition example using eigenfaces and SVMs (../examples/applications/plot_face_recognition.py)

00:27.921

0.0

Combine predictors using stacking (../examples/ensemble/plot_stack_predictors.py)

00:27.697

0.0

Poisson regression and non-normal loss (../examples/linear_model/plot_poisson_regression_non_normal_loss.py)

00:25.986

0.0

Partial Dependence and Individual Conditional Expectation Plots (../examples/inspection/plot_partial_dependence.py)

00:25.696

0.0

Features in Histogram Gradient Boosting Trees (../examples/ensemble/plot_hgbt_regression.py)

00:23.776

0.0

Plotting Learning Curves and Checking Models’ Scalability (../examples/model_selection/plot_learning_curve.py)

00:22.965

0.0

Scalable learning with polynomial kernel approximation (../examples/kernel_approximation/plot_scalable_poly_kernels.py)

00:21.358

0.0

Overview of multiclass training meta-estimators (../examples/multiclass/plot_multiclass_overview.py)

00:20.235

0.0

Model Complexity Influence (../examples/applications/plot_model_complexity_influence.py)

00:19.112

0.0

Swiss Roll And Swiss-Hole Reduction (../examples/manifold/plot_swissroll.py)

00:16.909

0.0

Common pitfalls in the interpretation of coefficients of linear models (../examples/inspection/plot_linear_model_coefficient_interpretation.py)

00:16.874

0.0

Prediction Latency (../examples/applications/plot_prediction_latency.py)

00:16.640

0.0

Manifold learning on handwritten digits: Locally Linear Embedding, Isomap… (../examples/manifold/plot_lle_digits.py)

00:15.826

0.0

Scaling the regularization parameter for SVCs (../examples/svm/plot_svm_scale_c.py)

00:15.714

0.0

Biclustering documents with the Spectral Co-clustering algorithm (../examples/bicluster/plot_bicluster_newsgroups.py)

00:14.718

0.0

Demo of HDBSCAN clustering algorithm (../examples/cluster/plot_hdbscan.py)

00:14.312

0.0

Time-related feature engineering (../examples/applications/plot_cyclical_feature_engineering.py)

00:13.689

0.0

Comparison of Manifold Learning methods (../examples/manifold/plot_compare_methods.py)

00:13.437

0.0

Image denoising using kernel PCA (../examples/applications/plot_digits_denoising.py)

00:12.535

0.0

Lagged features for time series forecasting (../examples/applications/plot_time_series_lagged_features.py)

00:11.813

0.0

Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation (../examples/applications/plot_topics_extraction_with_nmf_lda.py)

00:11.487

0.0

Test with permutations the significance of a classification score (../examples/model_selection/plot_permutation_tests_for_classification.py)

00:11.445

0.0

Tweedie regression on insurance claims (../examples/linear_model/plot_tweedie_regression_insurance_claims.py)

00:11.224

0.0

Species distribution modeling (../examples/applications/plot_species_distribution_modeling.py)

00:10.610

0.0

The Johnson-Lindenstrauss bound for embedding with random projections (../examples/miscellaneous/plot_johnson_lindenstrauss_bound.py)

00:10.542

0.0

MNIST classification using multinomial logistic + L1 (../examples/linear_model/plot_sparse_logistic_regression_mnist.py)

00:10.214

0.0

Custom refit strategy of a grid search with cross-validation (../examples/model_selection/plot_grid_search_digits.py)

00:09.684

0.0

Compressive sensing: tomography reconstruction with L1 prior (Lasso) (../examples/applications/plot_tomography_l1_reconstruction.py)

00:09.621

0.0

Prediction Intervals for Gradient Boosting Regression (../examples/ensemble/plot_gradient_boosting_quantile.py)

00:09.440

0.0

Gradient Boosting Out-of-Bag estimates (../examples/ensemble/plot_gradient_boosting_oob.py)

00:09.435

0.0

Faces dataset decompositions (../examples/decomposition/plot_faces_decomposition.py)

00:09.132

0.0

Imputing missing values before building an estimator (../examples/impute/plot_missing_values.py)

00:08.392

0.0

Comparing various online solvers (../examples/linear_model/plot_sgd_comparison.py)

00:08.259

0.0

Gradient Boosting regularization (../examples/ensemble/plot_gradient_boosting_regularization.py)

00:08.129

0.0

Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification (../examples/classification/plot_lda.py)

00:08.053

0.0

Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV (../examples/model_selection/plot_multi_metric_evaluation.py)

00:07.960

0.0

Compare the effect of different scalers on data with outliers (../examples/preprocessing/plot_all_scaling.py)

00:07.905

0.0

Comparison of kernel ridge regression and SVR (../examples/miscellaneous/plot_kernel_ridge_regression.py)

00:07.782

0.0

Multilabel classification using a classifier chain (../examples/multioutput/plot_classifier_chain_yeast.py)

00:07.565

0.0

Comparison between grid search and successive halving (../examples/model_selection/plot_successive_halving_heatmap.py)

00:07.098

0.0

Clustering text documents using k-means (../examples/text/plot_document_clustering.py)

00:07.071

0.0

Visualization of MLP weights on MNIST (../examples/neural_networks/plot_mnist_filters.py)

00:06.609

0.0

Semi-supervised Classification on a Text Dataset (../examples/semi_supervised/plot_semi_supervised_newsgroups.py)

00:06.589

0.0

Train error vs Test error (../examples/model_selection/plot_train_error_vs_test_error.py)

00:06.513

0.0

Plot the decision surfaces of ensembles of trees on the iris dataset (../examples/ensemble/plot_forest_iris.py)

00:06.424

0.0

Multiclass sparse logistic regression on 20newgroups (../examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py)

00:06.318

0.0

Ability of Gaussian process regression (GPR) to estimate data noise-level (../examples/gaussian_process/plot_gpr_noisy.py)

00:06.278

0.0

Classification of text documents using sparse features (../examples/text/plot_document_classification_20newsgroups.py)

00:06.267

0.0

Nested versus non-nested cross-validation (../examples/model_selection/plot_nested_cross_validation_iris.py)

00:06.172

0.0

Release Highlights for scikit-learn 1.2 (../examples/release_highlights/plot_release_highlights_1_2_0.py)

00:06.168

0.0

Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR) (../examples/gaussian_process/plot_gpr_co2.py)

00:06.106

0.0

Segmenting the picture of greek coins in regions (../examples/cluster/plot_coin_segmentation.py)

00:06.074

0.0

Categorical Feature Support in Gradient Boosting (../examples/ensemble/plot_gradient_boosting_categorical.py)

00:06.017

0.0

Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture (../examples/mixture/plot_concentration_prior.py)

00:05.935

0.0

Comparing different clustering algorithms on toy datasets (../examples/cluster/plot_cluster_comparison.py)

00:05.853

0.0

Manifold Learning methods on a severed sphere (../examples/manifold/plot_manifold_sphere.py)

00:05.743

0.0

Visualizing the stock market structure (../examples/applications/plot_stock_market.py)

00:05.713

0.0

SVM Exercise (../examples/exercises/plot_iris_exercise.py)

00:05.293

0.0

Effect of varying threshold for self-training (../examples/semi_supervised/plot_self_training_varying_threshold.py)

00:05.141

0.0

Imputing missing values with variants of IterativeImputer (../examples/impute/plot_iterative_imputer_variants_comparison.py)

00:05.027

0.0

FeatureHasher and DictVectorizer Comparison (../examples/text/plot_hashing_vs_dict_vectorizer.py)

00:05.027

0.0

Permutation Importance vs Random Forest Feature Importance (MDI) (../examples/inspection/plot_permutation_importance.py)

00:04.889

0.0

RBF SVM parameters (../examples/svm/plot_rbf_parameters.py)

00:04.803

0.0

Comparison of kernel ridge and Gaussian process regression (../examples/gaussian_process/plot_compare_gpr_krr.py)

00:04.750

0.0

Permutation Importance with Multicollinear or Correlated Features (../examples/inspection/plot_permutation_importance_multicollinear.py)

00:04.627

0.0

Comparing randomized search and grid search for hyperparameter estimation (../examples/model_selection/plot_randomized_search.py)

00:04.623

0.0

Successive Halving Iterations (../examples/model_selection/plot_successive_halving_iterations.py)

00:04.431

0.0

Gaussian process classification (GPC) on iris dataset (../examples/gaussian_process/plot_gpc_iris.py)

00:04.367

0.0

Release Highlights for scikit-learn 1.4 (../examples/release_highlights/plot_release_highlights_1_4_0.py)

00:04.339

0.0

Multi-class AdaBoosted Decision Trees (../examples/ensemble/plot_adaboost_multiclass.py)

00:04.312

0.0

Kernel Density Estimation (../examples/neighbors/plot_digits_kde_sampling.py)

00:04.266

0.0

Model selection with Probabilistic PCA and Factor Analysis (FA) (../examples/decomposition/plot_pca_vs_fa_model_selection.py)

00:04.247

0.0

OOB Errors for Random Forests (../examples/ensemble/plot_ensemble_oob.py)

00:03.641

0.0

Online learning of a dictionary of parts of faces (../examples/cluster/plot_dict_face_patches.py)

00:03.497

0.0

Recursive feature elimination (../examples/feature_selection/plot_rfe_digits.py)

00:03.430

0.0

Kernel Density Estimate of Species Distributions (../examples/neighbors/plot_species_kde.py)

00:03.409

0.0

Early stopping in Gradient Boosting (../examples/ensemble/plot_gradient_boosting_early_stopping.py)

00:03.389

0.0

Compare BIRCH and MiniBatchKMeans (../examples/cluster/plot_birch_vs_minibatchkmeans.py)

00:03.336

0.0

Feature discretization (../examples/preprocessing/plot_discretization_classification.py)

00:03.235

0.0

Comparing anomaly detection algorithms for outlier detection on toy datasets (../examples/miscellaneous/plot_anomaly_comparison.py)

00:03.089

0.0

Robust vs Empirical covariance estimate (../examples/covariance/plot_robust_vs_empirical_covariance.py)

00:03.030

0.0

t-SNE: The effect of various perplexity values on the shape (../examples/manifold/plot_t_sne_perplexity.py)

00:02.932

0.0

Failure of Machine Learning to infer causal effects (../examples/inspection/plot_causal_interpretation.py)

00:02.924

0.0

Feature transformations with ensembles of trees (../examples/ensemble/plot_feature_transformation.py)

00:02.864

0.0

Compare Stochastic learning strategies for MLPClassifier (../examples/neural_networks/plot_mlp_training_curves.py)

00:02.744

0.0

Comparison of Calibration of Classifiers (../examples/calibration/plot_compare_calibration.py)

00:02.646

0.0

Restricted Boltzmann Machine features for digit classification (../examples/neural_networks/plot_rbm_logistic_classification.py)

00:02.609

0.0

Advanced Plotting With Partial Dependence (../examples/miscellaneous/plot_partial_dependence_visualization_api.py)

00:02.441

0.0

Column Transformer with Heterogeneous Data Sources (../examples/compose/plot_column_transformer.py)

00:02.434

0.0

Ledoit-Wolf vs OAS estimation (../examples/covariance/plot_lw_vs_oas.py)

00:02.400

0.0

Inductive Clustering (../examples/cluster/plot_inductive_clustering.py)

00:02.396

0.0

Probability Calibration curves (../examples/calibration/plot_calibration_curve.py)

00:02.284

0.0

Vector Quantization Example (../examples/cluster/plot_face_compress.py)

00:02.133

0.0

Agglomerative clustering with and without structure (../examples/cluster/plot_agglomerative_clustering.py)

00:02.113

0.0

Classifier comparison (../examples/classification/plot_classifier_comparison.py)

00:02.110

0.0

Probabilistic predictions with Gaussian process classification (GPC) (../examples/gaussian_process/plot_gpc.py)

00:02.063

0.0

Robust linear estimator fitting (../examples/linear_model/plot_robust_fit.py)

00:02.045

0.0

Dimensionality Reduction with Neighborhood Components Analysis (../examples/neighbors/plot_nca_dim_reduction.py)

00:01.931

0.0

Comparing different hierarchical linkage methods on toy datasets (../examples/cluster/plot_linkage_comparison.py)

00:01.831

0.0

Class Likelihood Ratios to measure classification performance (../examples/model_selection/plot_likelihood_ratios.py)

00:01.825

0.0

Map data to a normal distribution (../examples/preprocessing/plot_map_data_to_normal.py)

00:01.771

0.0

Illustration of prior and posterior Gaussian process for different kernels (../examples/gaussian_process/plot_gpr_prior_posterior.py)

00:01.716

0.0

Varying regularization in Multi-layer Perceptron (../examples/neural_networks/plot_mlp_alpha.py)

00:01.699

0.0

Explicit feature map approximation for RBF kernels (../examples/miscellaneous/plot_kernel_approximation.py)

00:01.694

0.0

Importance of Feature Scaling (../examples/preprocessing/plot_scaling_importance.py)

00:01.653

0.0

Visualizations with Display Objects (../examples/miscellaneous/plot_display_object_visualization.py)

00:01.648

0.0

Face completion with a multi-output estimators (../examples/miscellaneous/plot_multioutput_face_completion.py)

00:01.634

0.0

Various Agglomerative Clustering on a 2D embedding of digits (../examples/cluster/plot_digits_linkage.py)

00:01.625

0.0

Probability Calibration for 3-class classification (../examples/calibration/plot_calibration_multiclass.py)

00:01.573

0.0

Plot classification probability (../examples/classification/plot_classification_probability.py)

00:01.491

0.0

Demo of OPTICS clustering algorithm (../examples/cluster/plot_optics.py)

00:01.489

0.0

Statistical comparison of models using grid search (../examples/model_selection/plot_grid_search_stats.py)

00:01.452

0.0

Gaussian Mixture Model Selection (../examples/mixture/plot_gmm_selection.py)

00:01.439

0.0

Gradient Boosting regression (../examples/ensemble/plot_gradient_boosting_regression.py)

00:01.439

0.0

Effect of transforming the targets in regression model (../examples/compose/plot_transformed_target.py)

00:01.416

0.0

Release Highlights for scikit-learn 1.3 (../examples/release_highlights/plot_release_highlights_1_3_0.py)

00:01.321

0.0

Empirical evaluation of the impact of k-means initialization (../examples/cluster/plot_kmeans_stability_low_dim_dense.py)

00:01.294

0.0

Plot classification boundaries with different SVM Kernels (../examples/svm/plot_svm_kernels.py)

00:01.266

0.0

Caching nearest neighbors (../examples/neighbors/plot_caching_nearest_neighbors.py)

00:01.238

0.0

Column Transformer with Mixed Types (../examples/compose/plot_column_transformer_mixed_types.py)

00:01.219

0.0

Lasso on dense and sparse data (../examples/linear_model/plot_lasso_dense_vs_sparse_data.py)

00:01.196

0.0

Visualizing cross-validation behavior in scikit-learn (../examples/model_selection/plot_cv_indices.py)

00:01.190

0.0

Pipelining: chaining a PCA and a logistic regression (../examples/compose/plot_digits_pipe.py)

00:01.156

0.0

Pixel importances with a parallel forest of trees (../examples/ensemble/plot_forest_importances_faces.py)

00:01.144

0.0

Demonstration of k-means assumptions (../examples/cluster/plot_kmeans_assumptions.py)

00:01.130

0.0

Single estimator versus bagging: bias-variance decomposition (../examples/ensemble/plot_bias_variance.py)

00:01.130

0.0

Selecting the number of clusters with silhouette analysis on KMeans clustering (../examples/cluster/plot_kmeans_silhouette_analysis.py)

00:01.120

0.0

Release Highlights for scikit-learn 0.22 (../examples/release_highlights/plot_release_highlights_0_22_0.py)

00:01.113

0.0

Agglomerative clustering with different metrics (../examples/cluster/plot_agglomerative_clustering_metrics.py)

00:01.063

0.0

Adjustment for chance in clustering performance evaluation (../examples/cluster/plot_adjusted_for_chance_measures.py)

00:01.008

0.0

Balance model complexity and cross-validated score (../examples/model_selection/plot_grid_search_refit_callable.py)

00:01.005

0.0

Plot individual and voting regression predictions (../examples/ensemble/plot_voting_regressor.py)

00:00.972

0.0

Lasso model selection: AIC-BIC / cross-validation (../examples/linear_model/plot_lasso_model_selection.py)

00:00.955

0.0

Bisecting K-Means and Regular K-Means Performance Comparison (../examples/cluster/plot_bisect_kmeans.py)

00:00.941

0.0

Feature importances with a forest of trees (../examples/ensemble/plot_forest_importances.py)

00:00.941

0.0

SVM Tie Breaking Example (../examples/svm/plot_svm_tie_breaking.py)

00:00.931

0.0

Plot the decision surface of decision trees trained on the iris dataset (../examples/tree/plot_iris_dtc.py)

00:00.893

0.0

Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset (../examples/semi_supervised/plot_semi_supervised_versus_svm_iris.py)

00:00.887

0.0

Release Highlights for scikit-learn 1.1 (../examples/release_highlights/plot_release_highlights_1_1_0.py)

00:00.880

0.0

A demo of K-Means clustering on the handwritten digits data (../examples/cluster/plot_kmeans_digits.py)

00:00.806

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)

00:00.774

0.0

Comparing Nearest Neighbors with and without Neighborhood Components Analysis (../examples/neighbors/plot_nca_classification.py)

00:00.751

0.0

Ridge coefficients as a function of the L2 Regularization (../examples/linear_model/plot_ridge_coeffs.py)

00:00.720

0.0

Release Highlights for scikit-learn 1.5 (../examples/release_highlights/plot_release_highlights_1_5_0.py)

00:00.719

0.0

Kernel PCA (../examples/decomposition/plot_kernel_pca.py)

00:00.711

0.0

Comparing Linear Bayesian Regressors (../examples/linear_model/plot_ard.py)

00:00.671

0.0

Demonstrating the different strategies of KBinsDiscretizer (../examples/preprocessing/plot_discretization_strategies.py)

00:00.658

0.0

Two-class AdaBoost (../examples/ensemble/plot_adaboost_twoclass.py)

00:00.651

0.0

Plot the decision boundaries of a VotingClassifier (../examples/ensemble/plot_voting_decision_regions.py)

00:00.645

0.0

Multiclass Receiver Operating Characteristic (ROC) (../examples/model_selection/plot_roc.py)

00:00.644

0.0

Novelty detection with Local Outlier Factor (LOF) (../examples/neighbors/plot_lof_novelty_detection.py)

00:00.629

0.0

Theil-Sen Regression (../examples/linear_model/plot_theilsen.py)

00:00.625

0.0

Plotting Validation Curves (../examples/model_selection/plot_validation_curve.py)

00:00.619

0.0

GMM Initialization Methods (../examples/mixture/plot_gmm_init.py)

00:00.605

0.0

Release Highlights for scikit-learn 0.23 (../examples/release_highlights/plot_release_highlights_0_23_0.py)

00:00.592

0.0

Quantile regression (../examples/linear_model/plot_quantile_regression.py)

00:00.592

0.0

Sparse inverse covariance estimation (../examples/covariance/plot_sparse_cov.py)

00:00.583

0.0

Monotonic Constraints (../examples/ensemble/plot_monotonic_constraints.py)

00:00.579

0.0

Simple 1D Kernel Density Estimation (../examples/neighbors/plot_kde_1d.py)

00:00.561

0.0

Lasso and Elastic Net (../examples/linear_model/plot_lasso_coordinate_descent_path.py)

00:00.557

0.0

Cross-validation on diabetes Dataset Exercise (../examples/exercises/plot_cv_diabetes.py)

00:00.532

0.0

Comparing random forests and the multi-output meta estimator (../examples/ensemble/plot_random_forest_regression_multioutput.py)

00:00.531

0.0

L1 Penalty and Sparsity in Logistic Regression (../examples/linear_model/plot_logistic_l1_l2_sparsity.py)

00:00.531

0.0

Spectral clustering for image segmentation (../examples/cluster/plot_segmentation_toy.py)

00:00.530

0.0

Principal Component Regression vs Partial Least Squares Regression (../examples/cross_decomposition/plot_pcr_vs_pls.py)

00:00.529

0.0

Nearest Neighbors Classification (../examples/neighbors/plot_classification.py)

00:00.528

0.0

Feature agglomeration vs. univariate selection (../examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py)

00:00.517

0.0

Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood (../examples/covariance/plot_covariance_estimation.py)

00:00.515

0.0

Gaussian Processes regression: basic introductory example (../examples/gaussian_process/plot_gpr_noisy_targets.py)

00:00.511

0.0

Recursive feature elimination with cross-validation (../examples/feature_selection/plot_rfe_with_cross_validation.py)

00:00.510

0.0

Gaussian Mixture Model Sine Curve (../examples/mixture/plot_gmm_sin.py)

00:00.508

0.0

Polynomial and Spline interpolation (../examples/linear_model/plot_polynomial_interpolation.py)

00:00.502

0.0

Illustration of Gaussian process classification (GPC) on the XOR dataset (../examples/gaussian_process/plot_gpc_xor.py)

00:00.498

0.0

Recognizing hand-written digits (../examples/classification/plot_digits_classification.py)

00:00.497

0.0

Post pruning decision trees with cost complexity pruning (../examples/tree/plot_cost_complexity_pruning.py)

00:00.488

0.0

A demo of the Spectral Biclustering algorithm (../examples/bicluster/plot_spectral_biclustering.py)

00:00.482

0.0

SVM: Weighted samples (../examples/svm/plot_weighted_samples.py)

00:00.474

0.0

Color Quantization using K-Means (../examples/cluster/plot_color_quantization.py)

00:00.467

0.0

One-Class SVM versus One-Class SVM using Stochastic Gradient Descent (../examples/linear_model/plot_sgdocsvm_vs_ocsvm.py)

00:00.465

0.0

A demo of structured Ward hierarchical clustering on an image of coins (../examples/cluster/plot_coin_ward_segmentation.py)

00:00.462

0.0

Label Propagation digits active learning (../examples/semi_supervised/plot_label_propagation_digits_active_learning.py)

00:00.460

0.0

Decision Tree Regression with AdaBoost (../examples/ensemble/plot_adaboost_regression.py)

00:00.455

0.0

L1-based models for Sparse Signals (../examples/linear_model/plot_lasso_and_elasticnet.py)

00:00.446

0.0

Factor Analysis (with rotation) to visualize patterns (../examples/decomposition/plot_varimax_fa.py)

00:00.444

0.0

A demo of the mean-shift clustering algorithm (../examples/cluster/plot_mean_shift.py)

00:00.438

0.0

IsolationForest example (../examples/ensemble/plot_isolation_forest.py)

00:00.436

0.0

Concatenating multiple feature extraction methods (../examples/compose/plot_feature_union.py)

00:00.421

0.0

FastICA on 2D point clouds (../examples/decomposition/plot_ica_vs_pca.py)

00:00.404

0.0

Support Vector Regression (SVR) using linear and non-linear kernels (../examples/svm/plot_svm_regression.py)

00:00.396

0.0

Blind source separation using FastICA (../examples/decomposition/plot_ica_blind_source_separation.py)

00:00.396

0.0

Linear and Quadratic Discriminant Analysis with covariance ellipsoid (../examples/classification/plot_lda_qda.py)

00:00.396

0.0

Outlier detection on a real data set (../examples/applications/plot_outlier_detection_wine.py)

00:00.383

0.0

Precision-Recall (../examples/model_selection/plot_precision_recall.py)

00:00.377

0.0

Hierarchical clustering: structured vs unstructured ward (../examples/cluster/plot_ward_structured_vs_unstructured.py)

00:00.370

0.0

Hashing feature transformation using Totally Random Trees (../examples/ensemble/plot_random_forest_embedding.py)

00:00.350

0.0

Sparse coding with a precomputed dictionary (../examples/decomposition/plot_sparse_coding.py)

00:00.349

0.0

Plot randomly generated classification dataset (../examples/datasets/plot_random_dataset.py)

00:00.345

0.0

Plot Ridge coefficients as a function of the regularization (../examples/linear_model/plot_ridge_path.py)

00:00.341

0.0

Demo of affinity propagation clustering algorithm (../examples/cluster/plot_affinity_propagation.py)

00:00.338

0.0

A demo of the Spectral Co-Clustering algorithm (../examples/bicluster/plot_spectral_coclustering.py)

00:00.334

0.0

Probability calibration of classifiers (../examples/calibration/plot_calibration.py)

00:00.333

0.0

Plot class probabilities calculated by the VotingClassifier (../examples/ensemble/plot_voting_probas.py)

00:00.323

0.0

K-means Clustering (../examples/cluster/plot_cluster_iris.py)

00:00.320

0.0

SGD: Penalties (../examples/linear_model/plot_sgd_penalties.py)

00:00.309

0.0

Label Propagation digits: Demonstrating performance (../examples/semi_supervised/plot_label_propagation_digits.py)

00:00.306

0.0

SVM-Anova: SVM with univariate feature selection (../examples/svm/plot_svm_anova.py)

00:00.300

0.0

Incremental PCA (../examples/decomposition/plot_incremental_pca.py)

00:00.293

0.0

Robust covariance estimation and Mahalanobis distances relevance (../examples/covariance/plot_mahalanobis_distances.py)

00:00.286

0.0

Target Encoder’s Internal Cross fitting (../examples/preprocessing/plot_target_encoder_cross_val.py)

00:00.279

0.0

Curve Fitting with Bayesian Ridge Regression (../examples/linear_model/plot_bayesian_ridge_curvefit.py)

00:00.279

0.0

Joint feature selection with multi-task Lasso (../examples/linear_model/plot_multi_task_lasso_support.py)

00:00.273

0.0

Ordinary Least Squares and Ridge Regression Variance (../examples/linear_model/plot_ols_ridge_variance.py)

00:00.272

0.0

Compare cross decomposition methods (../examples/cross_decomposition/plot_compare_cross_decomposition.py)

00:00.241

0.0

Multi-output Decision Tree Regression (../examples/tree/plot_tree_regression_multioutput.py)

00:00.241

0.0

Comparison of LDA and PCA 2D projection of Iris dataset (../examples/decomposition/plot_pca_vs_lda.py)

00:00.232

0.0

Nearest Neighbors regression (../examples/neighbors/plot_regression.py)

00:00.226

0.0

Gaussian processes on discrete data structures (../examples/gaussian_process/plot_gpr_on_structured_data.py)

00:00.222

0.0

Comparison of F-test and mutual information (../examples/feature_selection/plot_f_test_vs_mi.py)

00:00.222

0.0

Univariate Feature Selection (../examples/feature_selection/plot_feature_selection.py)

00:00.213

0.0

Gaussian Mixture Model Ellipsoids (../examples/mixture/plot_gmm.py)

00:00.209

0.0

Using KBinsDiscretizer to discretize continuous features (../examples/preprocessing/plot_discretization.py)

00:00.206

0.0

Orthogonal Matching Pursuit (../examples/linear_model/plot_omp.py)

00:00.203

0.0

GMM covariances (../examples/mixture/plot_gmm_covariances.py)

00:00.198

0.0

Sparsity Example: Fitting only features 1 and 2 (../examples/linear_model/plot_ols_3d.py)

00:00.194

0.0

Plot different SVM classifiers in the iris dataset (../examples/svm/plot_iris_svc.py)

00:00.193

0.0

Plot multinomial and One-vs-Rest Logistic Regression (../examples/linear_model/plot_logistic_multinomial.py)

00:00.192

0.0

The Iris Dataset (../examples/datasets/plot_iris_dataset.py)

00:00.191

0.0

Underfitting vs. Overfitting (../examples/model_selection/plot_underfitting_overfitting.py)

00:00.190

0.0

Plot the support vectors in LinearSVC (../examples/svm/plot_linearsvc_support_vectors.py)

00:00.183

0.0

Plotting Cross-Validated Predictions (../examples/model_selection/plot_cv_predict.py)

00:00.180

0.0

Detection error tradeoff (DET) curve (../examples/model_selection/plot_det.py)

00:00.180

0.0

Receiver Operating Characteristic (ROC) with cross validation (../examples/model_selection/plot_roc_crossval.py)

00:00.177

0.0

Multilabel classification (../examples/miscellaneous/plot_multilabel.py)

00:00.176

0.0

Confusion matrix (../examples/model_selection/plot_confusion_matrix.py)

00:00.173

0.0

Multi-dimensional scaling (../examples/manifold/plot_mds.py)

00:00.171

0.0

Comparison of the K-Means and MiniBatchKMeans clustering algorithms (../examples/cluster/plot_mini_batch_kmeans.py)

00:00.170

0.0

Demo of DBSCAN clustering algorithm (../examples/cluster/plot_dbscan.py)

00:00.165

0.0

SVM: Separating hyperplane for unbalanced classes (../examples/svm/plot_separating_hyperplane_unbalanced.py)

00:00.160

0.0

Density Estimation for a Gaussian mixture (../examples/mixture/plot_gmm_pdf.py)

00:00.160

0.0

Nearest Centroid Classification (../examples/neighbors/plot_nearest_centroid.py)

00:00.156

0.0

Feature agglomeration (../examples/cluster/plot_digits_agglomeration.py)

00:00.153

0.0

Neighborhood Components Analysis Illustration (../examples/neighbors/plot_nca_illustration.py)

00:00.150

0.0

Iso-probability lines for Gaussian Processes classification (GPC) (../examples/gaussian_process/plot_gpc_isoprobability.py)

00:00.146

0.0

ROC Curve with Visualization API (../examples/miscellaneous/plot_roc_curve_visualization_api.py)

00:00.146

0.0

Label Propagation learning a complex structure (../examples/semi_supervised/plot_label_propagation_structure.py)

00:00.144

0.0

One-class SVM with non-linear kernel (RBF) (../examples/svm/plot_oneclass.py)

00:00.138

0.0

Isotonic Regression (../examples/miscellaneous/plot_isotonic_regression.py)

00:00.137

0.0

Introducing the set_output API (../examples/miscellaneous/plot_set_output.py)

00:00.135

0.0

Logistic function (../examples/linear_model/plot_logistic.py)

00:00.134

0.0

Plot Hierarchical Clustering Dendrogram (../examples/cluster/plot_agglomerative_dendrogram.py)

00:00.128

0.0

Plot randomly generated multilabel dataset (../examples/datasets/plot_random_multilabel_dataset.py)

00:00.124

0.0

SGD: convex loss functions (../examples/linear_model/plot_sgd_loss_functions.py)

00:00.122

0.0

Regularization path of L1- Logistic Regression (../examples/linear_model/plot_logistic_path.py)

00:00.121

0.0

Plot multi-class SGD on the iris dataset (../examples/linear_model/plot_sgd_iris.py)

00:00.114

0.0

Lasso model selection via information criteria (../examples/linear_model/plot_lasso_lars_ic.py)

00:00.111

0.0

HuberRegressor vs Ridge on dataset with strong outliers (../examples/linear_model/plot_huber_vs_ridge.py)

00:00.108

0.0

PCA example with Iris Data-set (../examples/decomposition/plot_pca_iris.py)

00:00.107

0.0

Robust linear model estimation using RANSAC (../examples/linear_model/plot_ransac.py)

00:00.101

0.0

SVM with custom kernel (../examples/svm/plot_custom_kernel.py)

00:00.093

0.0

Displaying Pipelines (../examples/miscellaneous/plot_pipeline_display.py)

00:00.091

0.0

Decision Tree Regression (../examples/tree/plot_tree_regression.py)

00:00.091

0.0

SGD: Maximum margin separating hyperplane (../examples/linear_model/plot_sgd_separating_hyperplane.py)

00:00.085

0.0

Understanding the decision tree structure (../examples/tree/plot_unveil_tree_structure.py)

00:00.084

0.0

Lasso path using LARS (../examples/linear_model/plot_lasso_lars.py)

00:00.082

0.0

Outlier detection with Local Outlier Factor (LOF) (../examples/neighbors/plot_lof_outlier_detection.py)

00:00.079

0.0

SGD: Weighted samples (../examples/linear_model/plot_sgd_weighted_samples.py)

00:00.079

0.0

Digits Classification Exercise (../examples/exercises/plot_digits_classification_exercise.py)

00:00.067

0.0

SVM: Maximum margin separating hyperplane (../examples/svm/plot_separating_hyperplane.py)

00:00.066

0.0

Non-negative least squares (../examples/linear_model/plot_nnls.py)

00:00.064

0.0

SVM Margins Example (../examples/svm/plot_svm_margin.py)

00:00.063

0.0

An example of K-Means++ initialization (../examples/cluster/plot_kmeans_plusplus.py)

00:00.061

0.0

The Digit Dataset (../examples/datasets/plot_digits_last_image.py)

00:00.056

0.0

Logistic Regression 3-class Classifier (../examples/linear_model/plot_iris_logistic.py)

00:00.046

0.0

Metadata Routing (../examples/miscellaneous/plot_metadata_routing.py)

00:00.040

0.0

Linear Regression Example (../examples/linear_model/plot_ols.py)

00:00.036

0.0

Displaying estimators and complex pipelines (../examples/miscellaneous/plot_estimator_representation.py)

00:00.026

0.0

Pipeline ANOVA SVM (../examples/feature_selection/plot_feature_selection_pipeline.py)

00:00.015

0.0

Release Highlights for scikit-learn 1.0 (../examples/release_highlights/plot_release_highlights_1_0_0.py)

00:00.015

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