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Examples¶

Biclustering¶

Examples concerning the sklearn.cluster.bicluster module.

../_images/sphx_glr_plot_spectral_coclustering_thumb.png

A demo of the Spectral Co-Clustering algorithm¶

../_images/sphx_glr_plot_spectral_biclustering_thumb.png

A demo of the Spectral Biclustering algorithm¶

../_images/sphx_glr_plot_bicluster_newsgroups_thumb.png

Biclustering documents with the Spectral Co-clustering algorithm¶

Calibration¶

Examples illustrating the calibration of predicted probabilities of classifiers.

../_images/sphx_glr_plot_compare_calibration_thumb.png

Comparison of Calibration of Classifiers¶

../_images/sphx_glr_plot_calibration_curve_thumb.png

Probability Calibration curves¶

../_images/sphx_glr_plot_calibration_thumb.png

Probability calibration of classifiers¶

../_images/sphx_glr_plot_calibration_multiclass_thumb.png

Probability Calibration for 3-class classification¶

Classification¶

General examples about classification algorithms.

../_images/sphx_glr_plot_lda_thumb.png

Normal and Shrinkage Linear Discriminant Analysis for classification¶

../_images/sphx_glr_plot_digits_classification_thumb.png

Recognizing hand-written digits¶

../_images/sphx_glr_plot_classification_probability_thumb.png

Plot classification probability¶

../_images/sphx_glr_plot_classifier_comparison_thumb.png

Classifier comparison¶

../_images/sphx_glr_plot_lda_qda_thumb.png

Linear and Quadratic Discriminant Analysis with covariance ellipsoid¶

Clustering¶

Examples concerning the sklearn.cluster module.

../_images/sphx_glr_plot_agglomerative_dendrogram_thumb.png

Plot Hierarchical Clustering Dendrogram¶

../_images/sphx_glr_plot_digits_agglomeration_thumb.png

Feature agglomeration¶

../_images/sphx_glr_plot_mean_shift_thumb.png

A demo of the mean-shift clustering algorithm¶

../_images/sphx_glr_plot_kmeans_assumptions_thumb.png

Demonstration of k-means assumptions¶

../_images/sphx_glr_plot_dict_face_patches_thumb.png

Online learning of a dictionary of parts of faces¶

../_images/sphx_glr_plot_face_compress_thumb.png

Vector Quantization Example¶

../_images/sphx_glr_plot_affinity_propagation_thumb.png

Demo of affinity propagation clustering algorithm¶

../_images/sphx_glr_plot_agglomerative_clustering_thumb.png

Agglomerative clustering with and without structure¶

../_images/sphx_glr_plot_coin_segmentation_thumb.png

Segmenting the picture of greek coins in regions¶

../_images/sphx_glr_plot_digits_linkage_thumb.png

Various Agglomerative Clustering on a 2D embedding of digits¶

../_images/sphx_glr_plot_cluster_iris_thumb.png

K-means Clustering¶

../_images/sphx_glr_plot_segmentation_toy_thumb.png

Spectral clustering for image segmentation¶

../_images/sphx_glr_plot_coin_ward_segmentation_thumb.png

A demo of structured Ward hierarchical clustering on an image of coins¶

../_images/sphx_glr_plot_dbscan_thumb.png

Demo of DBSCAN clustering algorithm¶

../_images/sphx_glr_plot_color_quantization_thumb.png

Color Quantization using K-Means¶

../_images/sphx_glr_plot_ward_structured_vs_unstructured_thumb.png

Hierarchical clustering: structured vs unstructured ward¶

../_images/sphx_glr_plot_agglomerative_clustering_metrics_thumb.png

Agglomerative clustering with different metrics¶

../_images/sphx_glr_plot_inductive_clustering_thumb.png

Inductive Clustering¶

../_images/sphx_glr_plot_optics_thumb.png

Demo of OPTICS clustering algorithm¶

../_images/sphx_glr_plot_birch_vs_minibatchkmeans_thumb.png

Compare BIRCH and MiniBatchKMeans¶

../_images/sphx_glr_plot_kmeans_stability_low_dim_dense_thumb.png

Empirical evaluation of the impact of k-means initialization¶

../_images/sphx_glr_plot_adjusted_for_chance_measures_thumb.png

Adjustment for chance in clustering performance evaluation¶

../_images/sphx_glr_plot_mini_batch_kmeans_thumb.png

Comparison of the K-Means and MiniBatchKMeans clustering algorithms¶

../_images/sphx_glr_plot_feature_agglomeration_vs_univariate_selection_thumb.png

Feature agglomeration vs. univariate selection¶

../_images/sphx_glr_plot_kmeans_digits_thumb.png

A demo of K-Means clustering on the handwritten digits data¶

../_images/sphx_glr_plot_linkage_comparison_thumb.png

Comparing different hierarchical linkage methods on toy datasets¶

../_images/sphx_glr_plot_kmeans_silhouette_analysis_thumb.png

Selecting the number of clusters with silhouette analysis on KMeans clustering¶

../_images/sphx_glr_plot_cluster_comparison_thumb.png

Comparing different clustering algorithms on toy datasets¶

Covariance estimation¶

Examples concerning the sklearn.covariance module.

../_images/sphx_glr_plot_lw_vs_oas_thumb.png

Ledoit-Wolf vs OAS estimation¶

../_images/sphx_glr_plot_sparse_cov_thumb.png

Sparse inverse covariance estimation¶

../_images/sphx_glr_plot_covariance_estimation_thumb.png

Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood¶

../_images/sphx_glr_plot_mahalanobis_distances_thumb.png

Robust covariance estimation and Mahalanobis distances relevance¶

../_images/sphx_glr_plot_robust_vs_empirical_covariance_thumb.png

Robust vs Empirical covariance estimate¶

Cross decomposition¶

Examples concerning the sklearn.cross_decomposition module.

../_images/sphx_glr_plot_compare_cross_decomposition_thumb.png

Compare cross decomposition methods¶

Dataset examples¶

Examples concerning the sklearn.datasets module.

../_images/sphx_glr_plot_digits_last_image_thumb.png

The Digit Dataset¶

../_images/sphx_glr_plot_iris_dataset_thumb.png

The Iris Dataset¶

../_images/sphx_glr_plot_random_dataset_thumb.png

Plot randomly generated classification dataset¶

../_images/sphx_glr_plot_random_multilabel_dataset_thumb.png

Plot randomly generated multilabel dataset¶

Decision Trees¶

Examples concerning the sklearn.tree module.

../_images/sphx_glr_plot_tree_regression_thumb.png

Decision Tree Regression¶

../_images/sphx_glr_plot_tree_regression_multioutput_thumb.png

Multi-output Decision Tree Regression¶

../_images/sphx_glr_plot_iris_dtc_thumb.png

Plot the decision surface of a decision tree on the iris dataset¶

../_images/sphx_glr_plot_cost_complexity_pruning_thumb.png

Post pruning decision trees with cost complexity pruning¶

../_images/sphx_glr_plot_unveil_tree_structure_thumb.png

Understanding the decision tree structure¶

Ensemble methods¶

Examples concerning the sklearn.ensemble module.

../_images/sphx_glr_plot_forest_importances_faces_thumb.png

Pixel importances with a parallel forest of trees¶

../_images/sphx_glr_plot_adaboost_regression_thumb.png

Decision Tree Regression with AdaBoost¶

../_images/sphx_glr_plot_voting_regressor_thumb.png

Plot individual and voting regression predictions¶

../_images/sphx_glr_plot_forest_importances_thumb.png

Feature importances with forests of trees¶

../_images/sphx_glr_plot_isolation_forest_thumb.png

IsolationForest example¶

../_images/sphx_glr_plot_voting_decision_regions_thumb.png

Plot the decision boundaries of a VotingClassifier¶

../_images/sphx_glr_plot_random_forest_regression_multioutput_thumb.png

Comparing random forests and the multi-output meta estimator¶

../_images/sphx_glr_plot_gradient_boosting_quantile_thumb.png

Prediction Intervals for Gradient Boosting Regression¶

../_images/sphx_glr_plot_gradient_boosting_regularization_thumb.png

Gradient Boosting regularization¶

../_images/sphx_glr_plot_voting_probas_thumb.png

Plot class probabilities calculated by the VotingClassifier¶

../_images/sphx_glr_plot_gradient_boosting_regression_thumb.png

Gradient Boosting regression¶

../_images/sphx_glr_plot_ensemble_oob_thumb.png

OOB Errors for Random Forests¶

../_images/sphx_glr_plot_adaboost_twoclass_thumb.png

Two-class AdaBoost¶

../_images/sphx_glr_plot_random_forest_embedding_thumb.png

Hashing feature transformation using Totally Random Trees¶

../_images/sphx_glr_plot_adaboost_multiclass_thumb.png

Multi-class AdaBoosted Decision Trees¶

../_images/sphx_glr_plot_adaboost_hastie_10_2_thumb.png

Discrete versus Real AdaBoost¶

../_images/sphx_glr_plot_stack_predictors_thumb.png

Combine predictors using stacking¶

../_images/sphx_glr_plot_gradient_boosting_early_stopping_thumb.png

Early stopping of Gradient Boosting¶

../_images/sphx_glr_plot_feature_transformation_thumb.png

Feature transformations with ensembles of trees¶

../_images/sphx_glr_plot_gradient_boosting_oob_thumb.png

Gradient Boosting Out-of-Bag estimates¶

../_images/sphx_glr_plot_bias_variance_thumb.png

Single estimator versus bagging: bias-variance decomposition¶

../_images/sphx_glr_plot_forest_iris_thumb.png

Plot the decision surfaces of ensembles of trees on the iris dataset¶

Examples based on real world datasets¶

Applications to real world problems with some medium sized datasets or interactive user interface.

../_images/sphx_glr_plot_outlier_detection_housing_thumb.png

Outlier detection on a real data set¶

../_images/sphx_glr_plot_tomography_l1_reconstruction_thumb.png

Compressive sensing: tomography reconstruction with L1 prior (Lasso)¶

../_images/sphx_glr_plot_topics_extraction_with_nmf_lda_thumb.png

Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation¶

../_images/sphx_glr_plot_face_recognition_thumb.png

Faces recognition example using eigenfaces and SVMs¶

../_images/sphx_glr_plot_model_complexity_influence_thumb.png

Model Complexity Influence¶

../_images/sphx_glr_plot_stock_market_thumb.png

Visualizing the stock market structure¶

../_images/sphx_glr_wikipedia_principal_eigenvector_thumb.png

Wikipedia principal eigenvector¶

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Species distribution modeling¶

../_images/sphx_glr_svm_gui_thumb.png

../_images/sphx_glr_plot_prediction_latency_thumb.png

Prediction Latency¶

../_images/sphx_glr_plot_out_of_core_classification_thumb.png

Out-of-core classification of text documents¶

Feature Selection¶

Examples concerning the sklearn.feature_selection module.

../_images/sphx_glr_plot_rfe_digits_thumb.png

Recursive feature elimination¶

../_images/sphx_glr_plot_f_test_vs_mi_thumb.png

Comparison of F-test and mutual information¶

../_images/sphx_glr_plot_feature_selection_pipeline_thumb.png

Pipeline Anova SVM¶

../_images/sphx_glr_plot_rfe_with_cross_validation_thumb.png

Recursive feature elimination with cross-validation¶

../_images/sphx_glr_plot_select_from_model_boston_thumb.png

Feature selection using SelectFromModel and LassoCV¶

../_images/sphx_glr_plot_permutation_test_for_classification_thumb.png

Test with permutations the significance of a classification score¶

../_images/sphx_glr_plot_feature_selection_thumb.png

Univariate Feature Selection¶

Gaussian Mixture Models¶

Examples concerning the sklearn.mixture module.

../_images/sphx_glr_plot_gmm_pdf_thumb.png

Density Estimation for a Gaussian mixture¶

../_images/sphx_glr_plot_gmm_thumb.png

Gaussian Mixture Model Ellipsoids¶

../_images/sphx_glr_plot_gmm_selection_thumb.png

Gaussian Mixture Model Selection¶

../_images/sphx_glr_plot_gmm_covariances_thumb.png

GMM covariances¶

../_images/sphx_glr_plot_gmm_sin_thumb.png

Gaussian Mixture Model Sine Curve¶

../_images/sphx_glr_plot_concentration_prior_thumb.png

Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture¶

Generalized Linear Models¶

Examples concerning the sklearn.linear_model module.

../_images/sphx_glr_plot_lasso_lars_thumb.png

Lasso path using LARS¶

../_images/sphx_glr_plot_ridge_path_thumb.png

Plot Ridge coefficients as a function of the regularization¶

../_images/sphx_glr_plot_sgd_separating_hyperplane_thumb.png

SGD: Maximum margin separating hyperplane¶

../_images/sphx_glr_plot_sgd_loss_functions_thumb.png

SGD: convex loss functions¶

../_images/sphx_glr_plot_ols_ridge_variance_thumb.png

Ordinary Least Squares and Ridge Regression Variance¶

../_images/sphx_glr_plot_ridge_coeffs_thumb.png

Plot Ridge coefficients as a function of the L2 regularization¶

../_images/sphx_glr_plot_sgd_penalties_thumb.png

SGD: Penalties¶

../_images/sphx_glr_plot_polynomial_interpolation_thumb.png

Polynomial interpolation¶

../_images/sphx_glr_plot_logistic_thumb.png

Logistic function¶

../_images/sphx_glr_plot_logistic_path_thumb.png

Regularization path of L1- Logistic Regression¶

../_images/sphx_glr_plot_iris_logistic_thumb.png

Logistic Regression 3-class Classifier¶

../_images/sphx_glr_plot_sgd_weighted_samples_thumb.png

SGD: Weighted samples¶

../_images/sphx_glr_plot_ols_thumb.png

Linear Regression Example¶

../_images/sphx_glr_plot_ransac_thumb.png

Robust linear model estimation using RANSAC¶

../_images/sphx_glr_plot_ols_3d_thumb.png

Sparsity Example: Fitting only features 1 and 2¶

../_images/sphx_glr_plot_huber_vs_ridge_thumb.png

HuberRegressor vs Ridge on dataset with strong outliers¶

../_images/sphx_glr_plot_lasso_dense_vs_sparse_data_thumb.png

Lasso on dense and sparse data¶

../_images/sphx_glr_plot_sgd_comparison_thumb.png

Comparing various online solvers¶

../_images/sphx_glr_plot_multi_task_lasso_support_thumb.png

Joint feature selection with multi-task Lasso¶

../_images/sphx_glr_plot_sparse_logistic_regression_mnist_thumb.png

MNIST classification using multinomial logistic + L1¶

../_images/sphx_glr_plot_sgd_iris_thumb.png

Plot multi-class SGD on the iris dataset¶

../_images/sphx_glr_plot_omp_thumb.png

Orthogonal Matching Pursuit¶

../_images/sphx_glr_plot_lasso_and_elasticnet_thumb.png

Lasso and Elastic Net for Sparse Signals¶

../_images/sphx_glr_plot_bayesian_ridge_curvefit_thumb.png

Curve Fitting with Bayesian Ridge Regression¶

../_images/sphx_glr_plot_theilsen_thumb.png

Theil-Sen Regression¶

../_images/sphx_glr_plot_logistic_multinomial_thumb.png

Plot multinomial and One-vs-Rest Logistic Regression¶

../_images/sphx_glr_plot_robust_fit_thumb.png

Robust linear estimator fitting¶

../_images/sphx_glr_plot_logistic_l1_l2_sparsity_thumb.png

L1 Penalty and Sparsity in Logistic Regression¶

../_images/sphx_glr_plot_lasso_coordinate_descent_path_thumb.png

Lasso and Elastic Net¶

../_images/sphx_glr_plot_ard_thumb.png

Automatic Relevance Determination Regression (ARD)¶

../_images/sphx_glr_plot_bayesian_ridge_thumb.png

Bayesian Ridge Regression¶

../_images/sphx_glr_plot_sparse_logistic_regression_20newsgroups_thumb.png

Multiclass sparse logistic regression on 20newgroups¶

../_images/sphx_glr_plot_lasso_model_selection_thumb.png

Lasso model selection: Cross-Validation / AIC / BIC¶

../_images/sphx_glr_plot_sgd_early_stopping_thumb.png

Early stopping of Stochastic Gradient Descent¶

Inspection¶

Examples related to the sklearn.inspection module.

../_images/sphx_glr_plot_permutation_importance_multicollinear_thumb.png

Permutation Importance with Multicollinear or Correlated Features¶

../_images/sphx_glr_plot_permutation_importance_thumb.png

Permutation Importance vs Random Forest Feature Importance (MDI)¶

../_images/sphx_glr_plot_partial_dependence_thumb.png

Partial Dependence Plots¶

Manifold learning¶

Examples concerning the sklearn.manifold module.

../_images/sphx_glr_plot_swissroll_thumb.png

Swiss Roll reduction with LLE¶

../_images/sphx_glr_plot_compare_methods_thumb.png

Comparison of Manifold Learning methods¶

../_images/sphx_glr_plot_mds_thumb.png

Multi-dimensional scaling¶

../_images/sphx_glr_plot_t_sne_perplexity_thumb.png

t-SNE: The effect of various perplexity values on the shape¶

../_images/sphx_glr_plot_manifold_sphere_thumb.png

Manifold Learning methods on a severed sphere¶

../_images/sphx_glr_plot_lle_digits_thumb.png

Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…¶

Missing Value Imputation¶

Examples concerning the sklearn.impute module.

../_images/sphx_glr_plot_iterative_imputer_variants_comparison_thumb.png

Imputing missing values with variants of IterativeImputer¶

../_images/sphx_glr_plot_missing_values_thumb.png

Imputing missing values before building an estimator¶

Model Selection¶

Examples related to the sklearn.model_selection module.

../_images/sphx_glr_plot_cv_predict_thumb.png

Plotting Cross-Validated Predictions¶

../_images/sphx_glr_plot_confusion_matrix_thumb.png

Confusion matrix¶

../_images/sphx_glr_plot_validation_curve_thumb.png

Plotting Validation Curves¶

../_images/sphx_glr_plot_underfitting_overfitting_thumb.png

Underfitting vs. Overfitting¶

../_images/sphx_glr_plot_grid_search_digits_thumb.png

Parameter estimation using grid search with cross-validation¶

../_images/sphx_glr_plot_randomized_search_thumb.png

Comparing randomized search and grid search for hyperparameter estimation¶

../_images/sphx_glr_plot_train_error_vs_test_error_thumb.png

Train error vs Test error¶

../_images/sphx_glr_plot_roc_crossval_thumb.png

Receiver Operating Characteristic (ROC) with cross validation¶

../_images/sphx_glr_plot_nested_cross_validation_iris_thumb.png

Nested versus non-nested cross-validation¶

../_images/sphx_glr_plot_multi_metric_evaluation_thumb.png

Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV¶

../_images/sphx_glr_grid_search_text_feature_extraction_thumb.png

Sample pipeline for text feature extraction and evaluation¶

../_images/sphx_glr_plot_grid_search_refit_callable_thumb.png

Balance model complexity and cross-validated score¶

../_images/sphx_glr_plot_cv_indices_thumb.png

Visualizing cross-validation behavior in scikit-learn¶

../_images/sphx_glr_plot_roc_thumb.png

Receiver Operating Characteristic (ROC)¶

../_images/sphx_glr_plot_precision_recall_thumb.png

Precision-Recall¶

../_images/sphx_glr_plot_learning_curve_thumb.png

Plotting Learning Curves¶

Multioutput methods¶

Examples concerning the sklearn.multioutput module.

../_images/sphx_glr_plot_classifier_chain_yeast_thumb.png

Classifier Chain¶

Neural Networks¶

Examples concerning the sklearn.neural_network module.

../_images/sphx_glr_plot_mnist_filters_thumb.png

Visualization of MLP weights on MNIST¶

../_images/sphx_glr_plot_rbm_logistic_classification_thumb.png

Restricted Boltzmann Machine features for digit classification¶

../_images/sphx_glr_plot_mlp_alpha_thumb.png

Varying regularization in Multi-layer Perceptron¶

../_images/sphx_glr_plot_mlp_training_curves_thumb.png

Compare Stochastic learning strategies for MLPClassifier¶

Pipelines and composite estimators¶

Examples of how to compose transformers and pipelines from other estimators. See the User Guide.

../_images/sphx_glr_plot_feature_union_thumb.png

Concatenating multiple feature extraction methods¶

../_images/sphx_glr_plot_digits_pipe_thumb.png

Pipelining: chaining a PCA and a logistic regression¶

../_images/sphx_glr_plot_column_transformer_mixed_types_thumb.png

Column Transformer with Mixed Types¶

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Selecting dimensionality reduction with Pipeline and GridSearchCV¶

../_images/sphx_glr_plot_column_transformer_thumb.png

Column Transformer with Heterogeneous Data Sources¶

../_images/sphx_glr_plot_transformed_target_thumb.png

Effect of transforming the targets in regression model¶

Preprocessing¶

Examples concerning the sklearn.preprocessing module.

../_images/sphx_glr_plot_function_transformer_thumb.png

Using FunctionTransformer to select columns¶

../_images/sphx_glr_plot_discretization_thumb.png

Using KBinsDiscretizer to discretize continuous features¶

../_images/sphx_glr_plot_discretization_strategies_thumb.png

Demonstrating the different strategies of KBinsDiscretizer¶

../_images/sphx_glr_plot_scaling_importance_thumb.png

Importance of Feature Scaling¶

../_images/sphx_glr_plot_map_data_to_normal_thumb.png

Map data to a normal distribution¶

../_images/sphx_glr_plot_discretization_classification_thumb.png

Feature discretization¶

../_images/sphx_glr_plot_all_scaling_thumb.png

Compare the effect of different scalers on data with outliers¶

Release Highlights¶

These examples illustrate the main features of the releases of scikit-learn.

../_images/sphx_glr_plot_release_highlights_0_22_0_thumb.png

Release Highlights for scikit-learn 0.22¶

Semi Supervised Classification¶

Examples concerning the sklearn.semi_supervised module.

../_images/sphx_glr_plot_label_propagation_versus_svm_iris_thumb.png

Decision boundary of label propagation versus SVM on the Iris dataset¶

../_images/sphx_glr_plot_label_propagation_structure_thumb.png

Label Propagation learning a complex structure¶

../_images/sphx_glr_plot_label_propagation_digits_thumb.png

Label Propagation digits: Demonstrating performance¶

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Label Propagation digits active learning¶

Tutorial exercises¶

Exercises for the tutorials

../_images/sphx_glr_plot_digits_classification_exercise_thumb.png

Digits Classification Exercise¶

../_images/sphx_glr_plot_cv_digits_thumb.png

Cross-validation on Digits Dataset Exercise¶

../_images/sphx_glr_plot_iris_exercise_thumb.png

../_images/sphx_glr_plot_cv_diabetes_thumb.png

Cross-validation on diabetes Dataset Exercise¶

Working with text documents¶

Examples concerning the sklearn.feature_extraction.text module.

../_images/sphx_glr_plot_hashing_vs_dict_vectorizer_thumb.png

FeatureHasher and DictVectorizer Comparison¶

../_images/sphx_glr_plot_document_clustering_thumb.png

Clustering text documents using k-means¶

../_images/sphx_glr_plot_document_classification_20newsgroups_thumb.png

Classification of text documents using sparse features¶

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