Computation times#

19:36.220 total execution time for 282 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)

00:58.358

0.0

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

00:46.449

0.0

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

00:44.280

0.0

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

00:31.115

0.0

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

00:30.988

0.0

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

00:30.580

0.0

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

00:28.600

0.0

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

00:26.395

0.0

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

00:26.243

0.0

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

00:24.967

0.0

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

00:22.986

0.0

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

00:22.598

0.0

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

00:21.775

0.0

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

00:21.266

0.0

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

00:19.701

0.0

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

00:19.357

0.0

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

00:18.938

0.0

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

00:18.646

0.0

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

00:16.940

0.0

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

00:16.784

0.0

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

00:16.744

0.0

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

00:16.234

0.0

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

00:13.678

0.0

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

00:13.639

0.0

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

00:12.894

0.0

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

00:12.364

0.0

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

00:12.039

0.0

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

00:11.163

0.0

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

00:10.490

0.0

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

00:09.787

0.0

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

00:09.595

0.0

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

00:09.542

0.0

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

00:09.207

0.0

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

00:09.097

0.0

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

00:08.866

0.0

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

00:08.615

0.0

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

00:08.286

0.0

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

00:08.283

0.0

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

00:08.120

0.0

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

00:08.032

0.0

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

00:07.976

0.0

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

00:07.933

0.0

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

00:07.801

0.0

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

00:07.741

0.0

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

00:07.722

0.0

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

00:07.662

0.0

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

00:07.445

0.0

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

00:07.435

0.0

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

00:07.406

0.0

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

00:07.239

0.0

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

00:07.026

0.0

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

00:06.560

0.0

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

00:06.549

0.0

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

00:06.410

0.0

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

00:06.222

0.0

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

00:06.193

0.0

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

00:06.111

0.0

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

00:06.089

0.0

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

00:06.078

0.0

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

00:05.779

0.0

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

00:05.575

0.0

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

00:05.508

0.0

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

00:05.332

0.0

Effect of model regularization on training and test error (../examples/model_selection/plot_train_error_vs_test_error.py)

00:05.283

0.0

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

00:05.281

0.0

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

00:05.280

0.0

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

00:05.249

0.0

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

00:05.215

0.0

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

00:04.933

0.0

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

00:04.913

0.0

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

00:04.907

0.0

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

00:04.733

0.0

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

00:04.662

0.0

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

00:04.526

0.0

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

00:04.475

0.0

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

00:04.389

0.0

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

00:04.166

0.0

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

00:04.142

0.0

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

00:04.033

0.0

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

00:03.861

0.0

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

00:03.702

0.0

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

00:03.700

0.0

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

00:03.699

0.0

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

00:03.584

0.0

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

00:03.487

0.0

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

00:03.231

0.0

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

00:03.214

0.0

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

00:03.111

0.0

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

00:03.068

0.0

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

00:03.055

0.0

Principal Component Analysis (PCA) on Iris Dataset (../examples/decomposition/plot_pca_iris.py)

00:03.044

0.0

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

00:02.985

0.0

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

00:02.942

0.0

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

00:02.864

0.0

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

00:02.817

0.0

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

00:02.740

0.0

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

00:02.632

0.0

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

00:02.472

0.0

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

00:02.426

0.0

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

00:02.411

0.0

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

00:02.406

0.0

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

00:02.266

0.0

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

00:02.168

0.0

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

00:02.159

0.0

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

00:02.158

0.0

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

00:02.150

0.0

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

00:02.101

0.0

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

00:02.076

0.0

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

00:01.993

0.0

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

00:01.990

0.0

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

00:01.985

0.0

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

00:01.934

0.0

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

00:01.852

0.0

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

00:01.821

0.0

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

00:01.731

0.0

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

00:01.720

0.0

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

00:01.713

0.0

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

00:01.647

0.0

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

00:01.600

0.0

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

00:01.560

0.0

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

00:01.548

0.0

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

00:01.510

0.0

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

00:01.509

0.0

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

00:01.507

0.0

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

00:01.459

0.0

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

00:01.436

0.0

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

00:01.360

0.0

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

00:01.319

0.0

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

00:01.304

0.0

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

00:01.295

0.0

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

00:01.262

0.0

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

00:01.255

0.0

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

00:01.226

0.0

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

00:01.210

0.0

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

00:01.173

0.0

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

00:01.164

0.0

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

00:01.159

0.0

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

00:01.127

0.0

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

00:01.111

0.0

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

00:01.058

0.0

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

00:01.057

0.0

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

00:01.035

0.0

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

00:00.966

0.0

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

00:00.964

0.0

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

00:00.957

0.0

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

00:00.951

0.0

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

00:00.937

0.0

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

00:00.903

0.0

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

00:00.880

0.0

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

00:00.878

0.0

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

00:00.850

0.0

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

00:00.802

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)

00:00.788

0.0

Lasso, Lasso-LARS, and Elastic Net paths (../examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.py)

00:00.783

0.0

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

00:00.750

0.0

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

00:00.727

0.0

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

00:00.667

0.0

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

00:00.655

0.0

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

00:00.653

0.0

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

00:00.635

0.0

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

00:00.633

0.0

Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression (../examples/linear_model/plot_logistic_multinomial.py)

00:00.612

0.0

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

00:00.609

0.0

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

00:00.607

0.0

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

00:00.599

0.0

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

00:00.584

0.0

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

00:00.557

0.0

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

00:00.541

0.0

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

00:00.538

0.0

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

00:00.536

0.0

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

00:00.532

0.0

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

00:00.528

0.0

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

00:00.518

0.0

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

00:00.516

0.0

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

00:00.503

0.0

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

00:00.502

0.0

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

00:00.491

0.0

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

00:00.485

0.0

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

00:00.474

0.0

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

00:00.474

0.0

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

00:00.470

0.0

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

00:00.463

0.0

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

00:00.456

0.0

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

00:00.455

0.0

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

00:00.453

0.0

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

00:00.447

0.0

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

00:00.442

0.0

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

00:00.441

0.0

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

00:00.434

0.0

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

00:00.432

0.0

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

00:00.420

0.0

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

00:00.420

0.0

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

00:00.408

0.0

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

00:00.408

0.0

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

00:00.405

0.0

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

00:00.401

0.0

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

00:00.397

0.0

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

00:00.383

0.0

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

00:00.378

0.0

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

00:00.376

0.0

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

00:00.359

0.0

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

00:00.359

0.0

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

00:00.353

0.0

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

00:00.341

0.0

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

00:00.338

0.0

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

00:00.336

0.0

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

00:00.331

0.0

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

00:00.330

0.0

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

00:00.327

0.0

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

00:00.323

0.0

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

00:00.322

0.0

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

00:00.321

0.0

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

00:00.318

0.0

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

00:00.307

0.0

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

00:00.292

0.0

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

00:00.291

0.0

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

00:00.278

0.0

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

00:00.267

0.0

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

00:00.265

0.0

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

00:00.260

0.0

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

00:00.259

0.0

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

00:00.234

0.0

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

00:00.227

0.0

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

00:00.216

0.0

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

00:00.213

0.0

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

00:00.212

0.0

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

00:00.209

0.0

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

00:00.200

0.0

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

00:00.198

0.0

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

00:00.195

0.0

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

00:00.188

0.0

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

00:00.188

0.0

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

00:00.187

0.0

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

00:00.186

0.0

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

00:00.183

0.0

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

00:00.183

0.0

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

00:00.180

0.0

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

00:00.179

0.0

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

00:00.177

0.0

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

00:00.177

0.0

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

00:00.174

0.0

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

00:00.172

0.0

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

00:00.171

0.0

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

00:00.163

0.0

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

00:00.162

0.0

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

00:00.159

0.0

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

00:00.148

0.0

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

00:00.147

0.0

Ordinary Least Squares Example (../examples/linear_model/plot_ols.py)

00:00.146

0.0

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

00:00.137

0.0

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

00:00.136

0.0

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

00:00.133

0.0

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

00:00.131

0.0

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

00:00.131

0.0

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

00:00.131

0.0

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

00:00.115

0.0

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

00:00.108

0.0

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

00:00.108

0.0

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

00:00.105

0.0

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

00:00.099

0.0

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

00:00.098

0.0

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

00:00.096

0.0

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

00:00.092

0.0

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

00:00.092

0.0

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

00:00.089

0.0

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

00:00.089

0.0

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

00:00.084

0.0

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

00:00.081

0.0

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

00:00.071

0.0

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

00:00.068

0.0

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

00:00.067

0.0

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

00:00.065

0.0

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

00:00.065

0.0

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

00:00.063

0.0

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

00:00.061

0.0

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

00:00.040

0.0

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

00:00.027

0.0

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

00:00.017

0.0

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

00:00.012

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