:orphan: .. _sphx_glr_auto_examples: .. _general_examples: Examples ======== .. raw:: html
.. _sphx_glr_auto_examples_release_highlights: .. _release_highlights_examples: Release Highlights ------------------ These examples illustrate the main features of the releases of scikit-learn. .. raw:: html
.. only:: html .. figure:: /auto_examples/release_highlights/images/thumb/sphx_glr_plot_release_highlights_0_23_0_thumb.png :alt: Release Highlights for scikit-learn 0.23 :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_0_23_0.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/release_highlights/plot_release_highlights_0_23_0 .. raw:: html
.. only:: html .. figure:: /auto_examples/release_highlights/images/thumb/sphx_glr_plot_release_highlights_0_24_0_thumb.png :alt: Release Highlights for scikit-learn 0.24 :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_0_24_0.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/release_highlights/plot_release_highlights_0_24_0 .. raw:: html
.. only:: html .. figure:: /auto_examples/release_highlights/images/thumb/sphx_glr_plot_release_highlights_0_22_0_thumb.png :alt: Release Highlights for scikit-learn 0.22 :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_0_22_0.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/release_highlights/plot_release_highlights_0_22_0 .. raw:: html
.. _sphx_glr_auto_examples_bicluster: .. _bicluster_examples: Biclustering ------------ Examples concerning the :mod:`sklearn.cluster.bicluster` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/bicluster/images/thumb/sphx_glr_plot_spectral_coclustering_thumb.png :alt: A demo of the Spectral Co-Clustering algorithm :ref:`sphx_glr_auto_examples_bicluster_plot_spectral_coclustering.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/bicluster/plot_spectral_coclustering .. raw:: html
.. only:: html .. figure:: /auto_examples/bicluster/images/thumb/sphx_glr_plot_spectral_biclustering_thumb.png :alt: A demo of the Spectral Biclustering algorithm :ref:`sphx_glr_auto_examples_bicluster_plot_spectral_biclustering.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/bicluster/plot_spectral_biclustering .. raw:: html
.. only:: html .. figure:: /auto_examples/bicluster/images/thumb/sphx_glr_plot_bicluster_newsgroups_thumb.png :alt: Biclustering documents with the Spectral Co-clustering algorithm :ref:`sphx_glr_auto_examples_bicluster_plot_bicluster_newsgroups.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/bicluster/plot_bicluster_newsgroups .. raw:: html
.. _sphx_glr_auto_examples_calibration: .. _calibration_examples: Calibration ----------------------- Examples illustrating the calibration of predicted probabilities of classifiers. .. raw:: html
.. only:: html .. figure:: /auto_examples/calibration/images/thumb/sphx_glr_plot_compare_calibration_thumb.png :alt: Comparison of Calibration of Classifiers :ref:`sphx_glr_auto_examples_calibration_plot_compare_calibration.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/calibration/plot_compare_calibration .. raw:: html
.. only:: html .. figure:: /auto_examples/calibration/images/thumb/sphx_glr_plot_calibration_curve_thumb.png :alt: Probability Calibration curves :ref:`sphx_glr_auto_examples_calibration_plot_calibration_curve.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/calibration/plot_calibration_curve .. raw:: html
.. only:: html .. figure:: /auto_examples/calibration/images/thumb/sphx_glr_plot_calibration_thumb.png :alt: Probability calibration of classifiers :ref:`sphx_glr_auto_examples_calibration_plot_calibration.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/calibration/plot_calibration .. raw:: html
.. only:: html .. figure:: /auto_examples/calibration/images/thumb/sphx_glr_plot_calibration_multiclass_thumb.png :alt: Probability Calibration for 3-class classification :ref:`sphx_glr_auto_examples_calibration_plot_calibration_multiclass.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/calibration/plot_calibration_multiclass .. raw:: html
.. _sphx_glr_auto_examples_classification: .. _classification_examples: Classification ----------------------- General examples about classification algorithms. .. raw:: html
.. only:: html .. figure:: /auto_examples/classification/images/thumb/sphx_glr_plot_digits_classification_thumb.png :alt: Recognizing hand-written digits :ref:`sphx_glr_auto_examples_classification_plot_digits_classification.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/classification/plot_digits_classification .. raw:: html
.. only:: html .. figure:: /auto_examples/classification/images/thumb/sphx_glr_plot_lda_thumb.png :alt: Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification :ref:`sphx_glr_auto_examples_classification_plot_lda.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/classification/plot_lda .. raw:: html
.. only:: html .. figure:: /auto_examples/classification/images/thumb/sphx_glr_plot_classification_probability_thumb.png :alt: Plot classification probability :ref:`sphx_glr_auto_examples_classification_plot_classification_probability.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/classification/plot_classification_probability .. raw:: html
.. only:: html .. figure:: /auto_examples/classification/images/thumb/sphx_glr_plot_classifier_comparison_thumb.png :alt: Classifier comparison :ref:`sphx_glr_auto_examples_classification_plot_classifier_comparison.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/classification/plot_classifier_comparison .. raw:: html
.. only:: html .. figure:: /auto_examples/classification/images/thumb/sphx_glr_plot_lda_qda_thumb.png :alt: Linear and Quadratic Discriminant Analysis with covariance ellipsoid :ref:`sphx_glr_auto_examples_classification_plot_lda_qda.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/classification/plot_lda_qda .. raw:: html
.. _sphx_glr_auto_examples_cluster: .. _cluster_examples: Clustering ---------- Examples concerning the :mod:`sklearn.cluster` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_kmeans_plusplus_thumb.png :alt: An example of K-Means++ initialization :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_plusplus.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_kmeans_plusplus .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_agglomerative_dendrogram_thumb.png :alt: Plot Hierarchical Clustering Dendrogram :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_dendrogram.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_agglomerative_dendrogram .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_digits_agglomeration_thumb.png :alt: Feature agglomeration :ref:`sphx_glr_auto_examples_cluster_plot_digits_agglomeration.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_digits_agglomeration .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_mean_shift_thumb.png :alt: A demo of the mean-shift clustering algorithm :ref:`sphx_glr_auto_examples_cluster_plot_mean_shift.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_mean_shift .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_kmeans_assumptions_thumb.png :alt: Demonstration of k-means assumptions :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_assumptions.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_kmeans_assumptions .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_dict_face_patches_thumb.png :alt: Online learning of a dictionary of parts of faces :ref:`sphx_glr_auto_examples_cluster_plot_dict_face_patches.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_dict_face_patches .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_face_compress_thumb.png :alt: Vector Quantization Example :ref:`sphx_glr_auto_examples_cluster_plot_face_compress.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_face_compress .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_affinity_propagation_thumb.png :alt: Demo of affinity propagation clustering algorithm :ref:`sphx_glr_auto_examples_cluster_plot_affinity_propagation.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_affinity_propagation .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_agglomerative_clustering_thumb.png :alt: Agglomerative clustering with and without structure :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_clustering.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_agglomerative_clustering .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_digits_linkage_thumb.png :alt: Various Agglomerative Clustering on a 2D embedding of digits :ref:`sphx_glr_auto_examples_cluster_plot_digits_linkage.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_digits_linkage .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_coin_segmentation_thumb.png :alt: Segmenting the picture of greek coins in regions :ref:`sphx_glr_auto_examples_cluster_plot_coin_segmentation.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_coin_segmentation .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_cluster_iris_thumb.png :alt: K-means Clustering :ref:`sphx_glr_auto_examples_cluster_plot_cluster_iris.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_cluster_iris .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_segmentation_toy_thumb.png :alt: Spectral clustering for image segmentation :ref:`sphx_glr_auto_examples_cluster_plot_segmentation_toy.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_segmentation_toy .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_coin_ward_segmentation_thumb.png :alt: A demo of structured Ward hierarchical clustering on an image of coins :ref:`sphx_glr_auto_examples_cluster_plot_coin_ward_segmentation.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_coin_ward_segmentation .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_dbscan_thumb.png :alt: Demo of DBSCAN clustering algorithm :ref:`sphx_glr_auto_examples_cluster_plot_dbscan.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_dbscan .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_color_quantization_thumb.png :alt: Color Quantization using K-Means :ref:`sphx_glr_auto_examples_cluster_plot_color_quantization.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_color_quantization .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_ward_structured_vs_unstructured_thumb.png :alt: Hierarchical clustering: structured vs unstructured ward :ref:`sphx_glr_auto_examples_cluster_plot_ward_structured_vs_unstructured.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_ward_structured_vs_unstructured .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_agglomerative_clustering_metrics_thumb.png :alt: Agglomerative clustering with different metrics :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_clustering_metrics.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_agglomerative_clustering_metrics .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_inductive_clustering_thumb.png :alt: Inductive Clustering :ref:`sphx_glr_auto_examples_cluster_plot_inductive_clustering.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_inductive_clustering .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_optics_thumb.png :alt: Demo of OPTICS clustering algorithm :ref:`sphx_glr_auto_examples_cluster_plot_optics.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_optics .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_birch_vs_minibatchkmeans_thumb.png :alt: Compare BIRCH and MiniBatchKMeans :ref:`sphx_glr_auto_examples_cluster_plot_birch_vs_minibatchkmeans.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_birch_vs_minibatchkmeans .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_kmeans_stability_low_dim_dense_thumb.png :alt: Empirical evaluation of the impact of k-means initialization :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_stability_low_dim_dense.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_kmeans_stability_low_dim_dense .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_adjusted_for_chance_measures_thumb.png :alt: Adjustment for chance in clustering performance evaluation :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_adjusted_for_chance_measures .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_mini_batch_kmeans_thumb.png :alt: Comparison of the K-Means and MiniBatchKMeans clustering algorithms :ref:`sphx_glr_auto_examples_cluster_plot_mini_batch_kmeans.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_mini_batch_kmeans .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_feature_agglomeration_vs_univariate_selection_thumb.png :alt: Feature agglomeration vs. univariate selection :ref:`sphx_glr_auto_examples_cluster_plot_feature_agglomeration_vs_univariate_selection.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_linkage_comparison_thumb.png :alt: Comparing different hierarchical linkage methods on toy datasets :ref:`sphx_glr_auto_examples_cluster_plot_linkage_comparison.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_linkage_comparison .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_kmeans_digits_thumb.png :alt: A demo of K-Means clustering on the handwritten digits data :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_kmeans_digits .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_kmeans_silhouette_analysis_thumb.png :alt: Selecting the number of clusters with silhouette analysis on KMeans clustering :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_kmeans_silhouette_analysis .. raw:: html
.. only:: html .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_cluster_comparison_thumb.png :alt: Comparing different clustering algorithms on toy datasets :ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cluster/plot_cluster_comparison .. raw:: html
.. _sphx_glr_auto_examples_covariance: .. _covariance_examples: Covariance estimation --------------------- Examples concerning the :mod:`sklearn.covariance` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/covariance/images/thumb/sphx_glr_plot_lw_vs_oas_thumb.png :alt: Ledoit-Wolf vs OAS estimation :ref:`sphx_glr_auto_examples_covariance_plot_lw_vs_oas.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/covariance/plot_lw_vs_oas .. raw:: html
.. only:: html .. figure:: /auto_examples/covariance/images/thumb/sphx_glr_plot_sparse_cov_thumb.png :alt: Sparse inverse covariance estimation :ref:`sphx_glr_auto_examples_covariance_plot_sparse_cov.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/covariance/plot_sparse_cov .. raw:: html
.. only:: html .. figure:: /auto_examples/covariance/images/thumb/sphx_glr_plot_covariance_estimation_thumb.png :alt: Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/covariance/plot_covariance_estimation .. raw:: html
.. only:: html .. figure:: /auto_examples/covariance/images/thumb/sphx_glr_plot_mahalanobis_distances_thumb.png :alt: Robust covariance estimation and Mahalanobis distances relevance :ref:`sphx_glr_auto_examples_covariance_plot_mahalanobis_distances.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/covariance/plot_mahalanobis_distances .. raw:: html
.. only:: html .. figure:: /auto_examples/covariance/images/thumb/sphx_glr_plot_robust_vs_empirical_covariance_thumb.png :alt: Robust vs Empirical covariance estimate :ref:`sphx_glr_auto_examples_covariance_plot_robust_vs_empirical_covariance.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/covariance/plot_robust_vs_empirical_covariance .. raw:: html
.. _sphx_glr_auto_examples_cross_decomposition: .. _cross_decomposition_examples: Cross decomposition ------------------- Examples concerning the :mod:`sklearn.cross_decomposition` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/cross_decomposition/images/thumb/sphx_glr_plot_pcr_vs_pls_thumb.png :alt: Principal Component Regression vs Partial Least Squares Regression :ref:`sphx_glr_auto_examples_cross_decomposition_plot_pcr_vs_pls.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cross_decomposition/plot_pcr_vs_pls .. raw:: html
.. only:: html .. figure:: /auto_examples/cross_decomposition/images/thumb/sphx_glr_plot_compare_cross_decomposition_thumb.png :alt: Compare cross decomposition methods :ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/cross_decomposition/plot_compare_cross_decomposition .. raw:: html
.. _sphx_glr_auto_examples_datasets: .. _dataset_examples: Dataset examples ----------------------- Examples concerning the :mod:`sklearn.datasets` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/datasets/images/thumb/sphx_glr_plot_digits_last_image_thumb.png :alt: The Digit Dataset :ref:`sphx_glr_auto_examples_datasets_plot_digits_last_image.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/datasets/plot_digits_last_image .. raw:: html
.. only:: html .. figure:: /auto_examples/datasets/images/thumb/sphx_glr_plot_iris_dataset_thumb.png :alt: The Iris Dataset :ref:`sphx_glr_auto_examples_datasets_plot_iris_dataset.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/datasets/plot_iris_dataset .. raw:: html
.. only:: html .. figure:: /auto_examples/datasets/images/thumb/sphx_glr_plot_random_dataset_thumb.png :alt: Plot randomly generated classification dataset :ref:`sphx_glr_auto_examples_datasets_plot_random_dataset.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/datasets/plot_random_dataset .. raw:: html
.. only:: html .. figure:: /auto_examples/datasets/images/thumb/sphx_glr_plot_random_multilabel_dataset_thumb.png :alt: Plot randomly generated multilabel dataset :ref:`sphx_glr_auto_examples_datasets_plot_random_multilabel_dataset.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/datasets/plot_random_multilabel_dataset .. raw:: html
.. _sphx_glr_auto_examples_tree: .. _tree_examples: Decision Trees -------------- Examples concerning the :mod:`sklearn.tree` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/tree/images/thumb/sphx_glr_plot_tree_regression_thumb.png :alt: Decision Tree Regression :ref:`sphx_glr_auto_examples_tree_plot_tree_regression.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/tree/plot_tree_regression .. raw:: html
.. only:: html .. figure:: /auto_examples/tree/images/thumb/sphx_glr_plot_tree_regression_multioutput_thumb.png :alt: Multi-output Decision Tree Regression :ref:`sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/tree/plot_tree_regression_multioutput .. raw:: html
.. only:: html .. figure:: /auto_examples/tree/images/thumb/sphx_glr_plot_iris_dtc_thumb.png :alt: Plot the decision surface of a decision tree on the iris dataset :ref:`sphx_glr_auto_examples_tree_plot_iris_dtc.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/tree/plot_iris_dtc .. raw:: html
.. only:: html .. figure:: /auto_examples/tree/images/thumb/sphx_glr_plot_cost_complexity_pruning_thumb.png :alt: Post pruning decision trees with cost complexity pruning :ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/tree/plot_cost_complexity_pruning .. raw:: html
.. only:: html .. figure:: /auto_examples/tree/images/thumb/sphx_glr_plot_unveil_tree_structure_thumb.png :alt: Understanding the decision tree structure :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/tree/plot_unveil_tree_structure .. raw:: html
.. _sphx_glr_auto_examples_decomposition: .. _decomposition_examples: Decomposition ------------- Examples concerning the :mod:`sklearn.decomposition` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_beta_divergence_thumb.png :alt: Beta-divergence loss functions :ref:`sphx_glr_auto_examples_decomposition_plot_beta_divergence.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/decomposition/plot_beta_divergence .. raw:: html
.. only:: html .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_pca_iris_thumb.png :alt: PCA example with Iris Data-set :ref:`sphx_glr_auto_examples_decomposition_plot_pca_iris.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/decomposition/plot_pca_iris .. raw:: html
.. only:: html .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_incremental_pca_thumb.png :alt: Incremental PCA :ref:`sphx_glr_auto_examples_decomposition_plot_incremental_pca.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/decomposition/plot_incremental_pca .. raw:: html
.. only:: html .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_pca_vs_lda_thumb.png :alt: Comparison of LDA and PCA 2D projection of Iris dataset :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_lda.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/decomposition/plot_pca_vs_lda .. raw:: html
.. only:: html .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_varimax_fa_thumb.png :alt: Factor Analysis (with rotation) to visualize patterns :ref:`sphx_glr_auto_examples_decomposition_plot_varimax_fa.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/decomposition/plot_varimax_fa .. raw:: html
.. only:: html .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_ica_blind_source_separation_thumb.png :alt: Blind source separation using FastICA :ref:`sphx_glr_auto_examples_decomposition_plot_ica_blind_source_separation.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/decomposition/plot_ica_blind_source_separation .. raw:: html
.. only:: html .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_pca_3d_thumb.png :alt: Principal components analysis (PCA) :ref:`sphx_glr_auto_examples_decomposition_plot_pca_3d.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/decomposition/plot_pca_3d .. raw:: html
.. only:: html .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_ica_vs_pca_thumb.png :alt: FastICA on 2D point clouds :ref:`sphx_glr_auto_examples_decomposition_plot_ica_vs_pca.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/decomposition/plot_ica_vs_pca .. raw:: html
.. only:: html .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_kernel_pca_thumb.png :alt: Kernel PCA :ref:`sphx_glr_auto_examples_decomposition_plot_kernel_pca.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/decomposition/plot_kernel_pca .. raw:: html
.. only:: html .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_pca_vs_fa_model_selection_thumb.png :alt: Model selection with Probabilistic PCA and Factor Analysis (FA) :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_fa_model_selection.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/decomposition/plot_pca_vs_fa_model_selection .. raw:: html
.. only:: html .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_sparse_coding_thumb.png :alt: Sparse coding with a precomputed dictionary :ref:`sphx_glr_auto_examples_decomposition_plot_sparse_coding.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/decomposition/plot_sparse_coding .. raw:: html
.. only:: html .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_image_denoising_thumb.png :alt: Image denoising using dictionary learning :ref:`sphx_glr_auto_examples_decomposition_plot_image_denoising.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/decomposition/plot_image_denoising .. raw:: html
.. only:: html .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_faces_decomposition_thumb.png :alt: Faces dataset decompositions :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/decomposition/plot_faces_decomposition .. raw:: html
.. _sphx_glr_auto_examples_ensemble: .. _ensemble_examples: Ensemble methods ---------------- Examples concerning the :mod:`sklearn.ensemble` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_forest_importances_faces_thumb.png :alt: Pixel importances with a parallel forest of trees :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_forest_importances_faces .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_adaboost_regression_thumb.png :alt: Decision Tree Regression with AdaBoost :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_regression.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_adaboost_regression .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_voting_regressor_thumb.png :alt: Plot individual and voting regression predictions :ref:`sphx_glr_auto_examples_ensemble_plot_voting_regressor.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_voting_regressor .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_forest_importances_thumb.png :alt: Feature importances with forests of trees :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_forest_importances .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_isolation_forest_thumb.png :alt: IsolationForest example :ref:`sphx_glr_auto_examples_ensemble_plot_isolation_forest.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_isolation_forest .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_monotonic_constraints_thumb.png :alt: Monotonic Constraints :ref:`sphx_glr_auto_examples_ensemble_plot_monotonic_constraints.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_monotonic_constraints .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_voting_decision_regions_thumb.png :alt: Plot the decision boundaries of a VotingClassifier :ref:`sphx_glr_auto_examples_ensemble_plot_voting_decision_regions.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_voting_decision_regions .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_random_forest_regression_multioutput_thumb.png :alt: Comparing random forests and the multi-output meta estimator :ref:`sphx_glr_auto_examples_ensemble_plot_random_forest_regression_multioutput.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_random_forest_regression_multioutput .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_gradient_boosting_quantile_thumb.png :alt: Prediction Intervals for Gradient Boosting Regression :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_quantile.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_gradient_boosting_quantile .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_gradient_boosting_regularization_thumb.png :alt: Gradient Boosting regularization :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_gradient_boosting_regularization .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_voting_probas_thumb.png :alt: Plot class probabilities calculated by the VotingClassifier :ref:`sphx_glr_auto_examples_ensemble_plot_voting_probas.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_voting_probas .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_gradient_boosting_regression_thumb.png :alt: Gradient Boosting regression :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_gradient_boosting_regression .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_ensemble_oob_thumb.png :alt: OOB Errors for Random Forests :ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_ensemble_oob .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_adaboost_twoclass_thumb.png :alt: Two-class AdaBoost :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_twoclass.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_adaboost_twoclass .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_random_forest_embedding_thumb.png :alt: Hashing feature transformation using Totally Random Trees :ref:`sphx_glr_auto_examples_ensemble_plot_random_forest_embedding.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_random_forest_embedding .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_adaboost_multiclass_thumb.png :alt: Multi-class AdaBoosted Decision Trees :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_multiclass.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_adaboost_multiclass .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_adaboost_hastie_10_2_thumb.png :alt: Discrete versus Real AdaBoost :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_hastie_10_2.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_adaboost_hastie_10_2 .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_gradient_boosting_early_stopping_thumb.png :alt: Early stopping of Gradient Boosting :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_gradient_boosting_early_stopping .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_feature_transformation_thumb.png :alt: Feature transformations with ensembles of trees :ref:`sphx_glr_auto_examples_ensemble_plot_feature_transformation.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_feature_transformation .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_gradient_boosting_oob_thumb.png :alt: Gradient Boosting Out-of-Bag estimates :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_oob.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_gradient_boosting_oob .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_bias_variance_thumb.png :alt: Single estimator versus bagging: bias-variance decomposition :ref:`sphx_glr_auto_examples_ensemble_plot_bias_variance.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_bias_variance .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_gradient_boosting_categorical_thumb.png :alt: Categorical Feature Support in Gradient Boosting :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_gradient_boosting_categorical .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_forest_iris_thumb.png :alt: Plot the decision surfaces of ensembles of trees on the iris dataset :ref:`sphx_glr_auto_examples_ensemble_plot_forest_iris.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_forest_iris .. raw:: html
.. only:: html .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_stack_predictors_thumb.png :alt: Combine predictors using stacking :ref:`sphx_glr_auto_examples_ensemble_plot_stack_predictors.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/ensemble/plot_stack_predictors .. raw:: html
.. _sphx_glr_auto_examples_applications: .. _realworld_examples: Examples based on real world datasets ------------------------------------- Applications to real world problems with some medium sized datasets or interactive user interface. .. raw:: html
.. only:: html .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_outlier_detection_wine_thumb.png :alt: Outlier detection on a real data set :ref:`sphx_glr_auto_examples_applications_plot_outlier_detection_wine.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/applications/plot_outlier_detection_wine .. raw:: html
.. only:: html .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_tomography_l1_reconstruction_thumb.png :alt: Compressive sensing: tomography reconstruction with L1 prior (Lasso) :ref:`sphx_glr_auto_examples_applications_plot_tomography_l1_reconstruction.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/applications/plot_tomography_l1_reconstruction .. raw:: html
.. only:: html .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_topics_extraction_with_nmf_lda_thumb.png :alt: Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation :ref:`sphx_glr_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/applications/plot_topics_extraction_with_nmf_lda .. raw:: html
.. only:: html .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_face_recognition_thumb.png :alt: Faces recognition example using eigenfaces and SVMs :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/applications/plot_face_recognition .. raw:: html
.. only:: html .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_model_complexity_influence_thumb.png :alt: Model Complexity Influence :ref:`sphx_glr_auto_examples_applications_plot_model_complexity_influence.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/applications/plot_model_complexity_influence .. raw:: html
.. only:: html .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_stock_market_thumb.png :alt: Visualizing the stock market structure :ref:`sphx_glr_auto_examples_applications_plot_stock_market.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/applications/plot_stock_market .. raw:: html
.. only:: html .. figure:: /auto_examples/applications/images/thumb/sphx_glr_wikipedia_principal_eigenvector_thumb.png :alt: Wikipedia principal eigenvector :ref:`sphx_glr_auto_examples_applications_wikipedia_principal_eigenvector.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/applications/wikipedia_principal_eigenvector .. raw:: html
.. only:: html .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_species_distribution_modeling_thumb.png :alt: Species distribution modeling :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/applications/plot_species_distribution_modeling .. raw:: html
.. only:: html .. figure:: /auto_examples/applications/images/thumb/sphx_glr_svm_gui_thumb.png :alt: Libsvm GUI :ref:`sphx_glr_auto_examples_applications_svm_gui.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/applications/svm_gui .. raw:: html
.. only:: html .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_prediction_latency_thumb.png :alt: Prediction Latency :ref:`sphx_glr_auto_examples_applications_plot_prediction_latency.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/applications/plot_prediction_latency .. raw:: html
.. only:: html .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_out_of_core_classification_thumb.png :alt: Out-of-core classification of text documents :ref:`sphx_glr_auto_examples_applications_plot_out_of_core_classification.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/applications/plot_out_of_core_classification .. raw:: html
.. _sphx_glr_auto_examples_feature_selection: .. _feature_selection_examples: Feature Selection ----------------------- Examples concerning the :mod:`sklearn.feature_selection` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/feature_selection/images/thumb/sphx_glr_plot_rfe_digits_thumb.png :alt: Recursive feature elimination :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_digits.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/feature_selection/plot_rfe_digits .. raw:: html
.. only:: html .. figure:: /auto_examples/feature_selection/images/thumb/sphx_glr_plot_f_test_vs_mi_thumb.png :alt: Comparison of F-test and mutual information :ref:`sphx_glr_auto_examples_feature_selection_plot_f_test_vs_mi.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/feature_selection/plot_f_test_vs_mi .. raw:: html
.. only:: html .. figure:: /auto_examples/feature_selection/images/thumb/sphx_glr_plot_feature_selection_pipeline_thumb.png :alt: Pipeline Anova SVM :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection_pipeline.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/feature_selection/plot_feature_selection_pipeline .. raw:: html
.. only:: html .. figure:: /auto_examples/feature_selection/images/thumb/sphx_glr_plot_rfe_with_cross_validation_thumb.png :alt: Recursive feature elimination with cross-validation :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/feature_selection/plot_rfe_with_cross_validation .. raw:: html
.. only:: html .. figure:: /auto_examples/feature_selection/images/thumb/sphx_glr_plot_select_from_model_diabetes_thumb.png :alt: Model-based and sequential feature selection :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/feature_selection/plot_select_from_model_diabetes .. raw:: html
.. only:: html .. figure:: /auto_examples/feature_selection/images/thumb/sphx_glr_plot_permutation_test_for_classification_thumb.png :alt: Test with permutations the significance of a classification score :ref:`sphx_glr_auto_examples_feature_selection_plot_permutation_test_for_classification.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/feature_selection/plot_permutation_test_for_classification .. raw:: html
.. only:: html .. figure:: /auto_examples/feature_selection/images/thumb/sphx_glr_plot_feature_selection_thumb.png :alt: Univariate Feature Selection :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/feature_selection/plot_feature_selection .. raw:: html
.. _sphx_glr_auto_examples_mixture: .. _mixture_examples: Gaussian Mixture Models ----------------------- Examples concerning the :mod:`sklearn.mixture` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/mixture/images/thumb/sphx_glr_plot_gmm_pdf_thumb.png :alt: Density Estimation for a Gaussian mixture :ref:`sphx_glr_auto_examples_mixture_plot_gmm_pdf.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/mixture/plot_gmm_pdf .. raw:: html
.. only:: html .. figure:: /auto_examples/mixture/images/thumb/sphx_glr_plot_gmm_thumb.png :alt: Gaussian Mixture Model Ellipsoids :ref:`sphx_glr_auto_examples_mixture_plot_gmm.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/mixture/plot_gmm .. raw:: html
.. only:: html .. figure:: /auto_examples/mixture/images/thumb/sphx_glr_plot_gmm_selection_thumb.png :alt: Gaussian Mixture Model Selection :ref:`sphx_glr_auto_examples_mixture_plot_gmm_selection.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/mixture/plot_gmm_selection .. raw:: html
.. only:: html .. figure:: /auto_examples/mixture/images/thumb/sphx_glr_plot_gmm_covariances_thumb.png :alt: GMM covariances :ref:`sphx_glr_auto_examples_mixture_plot_gmm_covariances.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/mixture/plot_gmm_covariances .. raw:: html
.. only:: html .. figure:: /auto_examples/mixture/images/thumb/sphx_glr_plot_gmm_sin_thumb.png :alt: Gaussian Mixture Model Sine Curve :ref:`sphx_glr_auto_examples_mixture_plot_gmm_sin.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/mixture/plot_gmm_sin .. raw:: html
.. only:: html .. figure:: /auto_examples/mixture/images/thumb/sphx_glr_plot_concentration_prior_thumb.png :alt: Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture :ref:`sphx_glr_auto_examples_mixture_plot_concentration_prior.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/mixture/plot_concentration_prior .. raw:: html
.. _sphx_glr_auto_examples_gaussian_process: .. _gaussian_process_examples: Gaussian Process for Machine Learning ------------------------------------- Examples concerning the :mod:`sklearn.gaussian_process` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpc_xor_thumb.png :alt: Illustration of Gaussian process classification (GPC) on the XOR dataset :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpc_xor.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/gaussian_process/plot_gpc_xor .. raw:: html
.. only:: html .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpc_iris_thumb.png :alt: Gaussian process classification (GPC) on iris dataset :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpc_iris.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/gaussian_process/plot_gpc_iris .. raw:: html
.. only:: html .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_compare_gpr_krr_thumb.png :alt: Comparison of kernel ridge and Gaussian process regression :ref:`sphx_glr_auto_examples_gaussian_process_plot_compare_gpr_krr.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/gaussian_process/plot_compare_gpr_krr .. raw:: html
.. only:: html .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpr_prior_posterior_thumb.png :alt: Illustration of prior and posterior Gaussian process for different kernels :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_prior_posterior.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/gaussian_process/plot_gpr_prior_posterior .. raw:: html
.. only:: html .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpc_isoprobability_thumb.png :alt: Iso-probability lines for Gaussian Processes classification (GPC) :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpc_isoprobability.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/gaussian_process/plot_gpc_isoprobability .. raw:: html
.. only:: html .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpc_thumb.png :alt: Probabilistic predictions with Gaussian process classification (GPC) :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpc.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/gaussian_process/plot_gpc .. raw:: html
.. only:: html .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpr_noisy_thumb.png :alt: Gaussian process regression (GPR) with noise-level estimation :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_noisy.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/gaussian_process/plot_gpr_noisy .. raw:: html
.. only:: html .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpr_noisy_targets_thumb.png :alt: Gaussian Processes regression: basic introductory example :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_noisy_targets.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/gaussian_process/plot_gpr_noisy_targets .. raw:: html
.. only:: html .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpr_co2_thumb.png :alt: Gaussian process regression (GPR) on Mauna Loa CO2 data. :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_co2.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/gaussian_process/plot_gpr_co2 .. raw:: html
.. only:: html .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpr_on_structured_data_thumb.png :alt: Gaussian processes on discrete data structures :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_on_structured_data.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/gaussian_process/plot_gpr_on_structured_data .. raw:: html
.. _sphx_glr_auto_examples_linear_model: .. _linear_examples: Generalized Linear Models ------------------------- Examples concerning the :mod:`sklearn.linear_model` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_lasso_lars_thumb.png :alt: Lasso path using LARS :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_lars.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_lasso_lars .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sgd_separating_hyperplane_thumb.png :alt: SGD: Maximum margin separating hyperplane :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_separating_hyperplane.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_sgd_separating_hyperplane .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_ridge_path_thumb.png :alt: Plot Ridge coefficients as a function of the regularization :ref:`sphx_glr_auto_examples_linear_model_plot_ridge_path.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_ridge_path .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sgd_loss_functions_thumb.png :alt: SGD: convex loss functions :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_loss_functions.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_sgd_loss_functions .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_ols_ridge_variance_thumb.png :alt: Ordinary Least Squares and Ridge Regression Variance :ref:`sphx_glr_auto_examples_linear_model_plot_ols_ridge_variance.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_ols_ridge_variance .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_ridge_coeffs_thumb.png :alt: Plot Ridge coefficients as a function of the L2 regularization :ref:`sphx_glr_auto_examples_linear_model_plot_ridge_coeffs.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_ridge_coeffs .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sgd_penalties_thumb.png :alt: SGD: Penalties :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_penalties.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_sgd_penalties .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_logistic_thumb.png :alt: Logistic function :ref:`sphx_glr_auto_examples_linear_model_plot_logistic.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_logistic .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_polynomial_interpolation_thumb.png :alt: Polynomial interpolation :ref:`sphx_glr_auto_examples_linear_model_plot_polynomial_interpolation.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_polynomial_interpolation .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_logistic_path_thumb.png :alt: Regularization path of L1- Logistic Regression :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_logistic_path .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_iris_logistic_thumb.png :alt: Logistic Regression 3-class Classifier :ref:`sphx_glr_auto_examples_linear_model_plot_iris_logistic.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_iris_logistic .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sgd_weighted_samples_thumb.png :alt: SGD: Weighted samples :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_weighted_samples.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_sgd_weighted_samples .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_nnls_thumb.png :alt: Non-negative least squares :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_nnls .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_ols_thumb.png :alt: Linear Regression Example :ref:`sphx_glr_auto_examples_linear_model_plot_ols.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_ols .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_ransac_thumb.png :alt: Robust linear model estimation using RANSAC :ref:`sphx_glr_auto_examples_linear_model_plot_ransac.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_ransac .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_ols_3d_thumb.png :alt: Sparsity Example: Fitting only features 1 and 2 :ref:`sphx_glr_auto_examples_linear_model_plot_ols_3d.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_ols_3d .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_huber_vs_ridge_thumb.png :alt: HuberRegressor vs Ridge on dataset with strong outliers :ref:`sphx_glr_auto_examples_linear_model_plot_huber_vs_ridge.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_huber_vs_ridge .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_lasso_dense_vs_sparse_data_thumb.png :alt: Lasso on dense and sparse data :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_dense_vs_sparse_data.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_lasso_dense_vs_sparse_data .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sgd_comparison_thumb.png :alt: Comparing various online solvers :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_comparison.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_sgd_comparison .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_multi_task_lasso_support_thumb.png :alt: Joint feature selection with multi-task Lasso :ref:`sphx_glr_auto_examples_linear_model_plot_multi_task_lasso_support.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_multi_task_lasso_support .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sparse_logistic_regression_mnist_thumb.png :alt: MNIST classification using multinomial logistic + L1 :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_mnist.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_sparse_logistic_regression_mnist .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sgd_iris_thumb.png :alt: Plot multi-class SGD on the iris dataset :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_iris.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_sgd_iris .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_omp_thumb.png :alt: Orthogonal Matching Pursuit :ref:`sphx_glr_auto_examples_linear_model_plot_omp.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_omp .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_lasso_and_elasticnet_thumb.png :alt: Lasso and Elastic Net for Sparse Signals :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_lasso_and_elasticnet .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_bayesian_ridge_curvefit_thumb.png :alt: Curve Fitting with Bayesian Ridge Regression :ref:`sphx_glr_auto_examples_linear_model_plot_bayesian_ridge_curvefit.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_bayesian_ridge_curvefit .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_theilsen_thumb.png :alt: Theil-Sen Regression :ref:`sphx_glr_auto_examples_linear_model_plot_theilsen.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_theilsen .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_logistic_multinomial_thumb.png :alt: Plot multinomial and One-vs-Rest Logistic Regression :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_multinomial.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_logistic_multinomial .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_robust_fit_thumb.png :alt: Robust linear estimator fitting :ref:`sphx_glr_auto_examples_linear_model_plot_robust_fit.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_robust_fit .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_logistic_l1_l2_sparsity_thumb.png :alt: L1 Penalty and Sparsity in Logistic Regression :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_l1_l2_sparsity.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_logistic_l1_l2_sparsity .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_lasso_coordinate_descent_path_thumb.png :alt: Lasso and Elastic Net :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_coordinate_descent_path.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_lasso_coordinate_descent_path .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_ard_thumb.png :alt: Automatic Relevance Determination Regression (ARD) :ref:`sphx_glr_auto_examples_linear_model_plot_ard.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_ard .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_bayesian_ridge_thumb.png :alt: Bayesian Ridge Regression :ref:`sphx_glr_auto_examples_linear_model_plot_bayesian_ridge.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_bayesian_ridge .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_lasso_model_selection_thumb.png :alt: Lasso model selection: Cross-Validation / AIC / BIC :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_model_selection.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_lasso_model_selection .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sparse_logistic_regression_20newsgroups_thumb.png :alt: Multiclass sparse logistic regression on 20newgroups :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_20newsgroups.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sgd_early_stopping_thumb.png :alt: Early stopping of Stochastic Gradient Descent :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_early_stopping.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_sgd_early_stopping .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_poisson_regression_non_normal_loss_thumb.png :alt: Poisson regression and non-normal loss :ref:`sphx_glr_auto_examples_linear_model_plot_poisson_regression_non_normal_loss.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_poisson_regression_non_normal_loss .. raw:: html
.. only:: html .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_tweedie_regression_insurance_claims_thumb.png :alt: Tweedie regression on insurance claims :ref:`sphx_glr_auto_examples_linear_model_plot_tweedie_regression_insurance_claims.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/linear_model/plot_tweedie_regression_insurance_claims .. raw:: html
.. _sphx_glr_auto_examples_inspection: .. _inspection_examples: Inspection ---------- Examples related to the :mod:`sklearn.inspection` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/inspection/images/thumb/sphx_glr_plot_permutation_importance_multicollinear_thumb.png :alt: Permutation Importance with Multicollinear or Correlated Features :ref:`sphx_glr_auto_examples_inspection_plot_permutation_importance_multicollinear.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/inspection/plot_permutation_importance_multicollinear .. raw:: html
.. only:: html .. figure:: /auto_examples/inspection/images/thumb/sphx_glr_plot_permutation_importance_thumb.png :alt: Permutation Importance vs Random Forest Feature Importance (MDI) :ref:`sphx_glr_auto_examples_inspection_plot_permutation_importance.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/inspection/plot_permutation_importance .. raw:: html
.. only:: html .. figure:: /auto_examples/inspection/images/thumb/sphx_glr_plot_partial_dependence_thumb.png :alt: Partial Dependence and Individual Conditional Expectation Plots :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/inspection/plot_partial_dependence .. raw:: html
.. only:: html .. figure:: /auto_examples/inspection/images/thumb/sphx_glr_plot_linear_model_coefficient_interpretation_thumb.png :alt: Common pitfalls in interpretation of coefficients of linear models :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/inspection/plot_linear_model_coefficient_interpretation .. raw:: html
.. _sphx_glr_auto_examples_kernel_approximation: .. _kernel_approximation_examples: Kernel Approximation -------------------- Examples concerning the :mod:`sklearn.kernel_approximation` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/kernel_approximation/images/thumb/sphx_glr_plot_scalable_poly_kernels_thumb.png :alt: Scalable learning with polynomial kernel aproximation :ref:`sphx_glr_auto_examples_kernel_approximation_plot_scalable_poly_kernels.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/kernel_approximation/plot_scalable_poly_kernels .. raw:: html
.. _sphx_glr_auto_examples_manifold: .. _manifold_examples: Manifold learning ----------------------- Examples concerning the :mod:`sklearn.manifold` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/manifold/images/thumb/sphx_glr_plot_swissroll_thumb.png :alt: Swiss Roll reduction with LLE :ref:`sphx_glr_auto_examples_manifold_plot_swissroll.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/manifold/plot_swissroll .. raw:: html
.. only:: html .. figure:: /auto_examples/manifold/images/thumb/sphx_glr_plot_compare_methods_thumb.png :alt: Comparison of Manifold Learning methods :ref:`sphx_glr_auto_examples_manifold_plot_compare_methods.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/manifold/plot_compare_methods .. raw:: html
.. only:: html .. figure:: /auto_examples/manifold/images/thumb/sphx_glr_plot_mds_thumb.png :alt: Multi-dimensional scaling :ref:`sphx_glr_auto_examples_manifold_plot_mds.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/manifold/plot_mds .. raw:: html
.. only:: html .. figure:: /auto_examples/manifold/images/thumb/sphx_glr_plot_t_sne_perplexity_thumb.png :alt: t-SNE: The effect of various perplexity values on the shape :ref:`sphx_glr_auto_examples_manifold_plot_t_sne_perplexity.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/manifold/plot_t_sne_perplexity .. raw:: html
.. only:: html .. figure:: /auto_examples/manifold/images/thumb/sphx_glr_plot_manifold_sphere_thumb.png :alt: Manifold Learning methods on a severed sphere :ref:`sphx_glr_auto_examples_manifold_plot_manifold_sphere.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/manifold/plot_manifold_sphere .. raw:: html
.. only:: html .. figure:: /auto_examples/manifold/images/thumb/sphx_glr_plot_lle_digits_thumb.png :alt: Manifold learning on handwritten digits: Locally Linear Embedding, Isomap... :ref:`sphx_glr_auto_examples_manifold_plot_lle_digits.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/manifold/plot_lle_digits .. raw:: html
.. _sphx_glr_auto_examples_miscellaneous: .. _miscellaneous_examples: Miscellaneous ------------- Miscellaneous and introductory examples for scikit-learn. .. raw:: html
.. only:: html .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_changed_only_pprint_parameter_thumb.png :alt: Compact estimator representations :ref:`sphx_glr_auto_examples_miscellaneous_plot_changed_only_pprint_parameter.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/miscellaneous/plot_changed_only_pprint_parameter .. raw:: html
.. only:: html .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_roc_curve_visualization_api_thumb.png :alt: ROC Curve with Visualization API :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/miscellaneous/plot_roc_curve_visualization_api .. raw:: html
.. only:: html .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_display_object_visualization_thumb.png :alt: Visualizations with Display Objects :ref:`sphx_glr_auto_examples_miscellaneous_plot_display_object_visualization.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/miscellaneous/plot_display_object_visualization .. raw:: html
.. only:: html .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_isotonic_regression_thumb.png :alt: Isotonic Regression :ref:`sphx_glr_auto_examples_miscellaneous_plot_isotonic_regression.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/miscellaneous/plot_isotonic_regression .. raw:: html
.. only:: html .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_partial_dependence_visualization_api_thumb.png :alt: Advanced Plotting With Partial Dependence :ref:`sphx_glr_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/miscellaneous/plot_partial_dependence_visualization_api .. raw:: html
.. only:: html .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_multioutput_face_completion_thumb.png :alt: Face completion with a multi-output estimators :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/miscellaneous/plot_multioutput_face_completion .. raw:: html
.. only:: html .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_multilabel_thumb.png :alt: Multilabel classification :ref:`sphx_glr_auto_examples_miscellaneous_plot_multilabel.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/miscellaneous/plot_multilabel .. raw:: html
.. only:: html .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_anomaly_comparison_thumb.png :alt: Comparing anomaly detection algorithms for outlier detection on toy datasets :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/miscellaneous/plot_anomaly_comparison .. raw:: html
.. only:: html .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_johnson_lindenstrauss_bound_thumb.png :alt: The Johnson-Lindenstrauss bound for embedding with random projections :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound .. raw:: html
.. only:: html .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_kernel_ridge_regression_thumb.png :alt: Comparison of kernel ridge regression and SVR :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_ridge_regression.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/miscellaneous/plot_kernel_ridge_regression .. raw:: html
.. only:: html .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_kernel_approximation_thumb.png :alt: Explicit feature map approximation for RBF kernels :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/miscellaneous/plot_kernel_approximation .. raw:: html
.. _sphx_glr_auto_examples_impute: .. _impute_examples: Missing Value Imputation ------------------------ Examples concerning the :mod:`sklearn.impute` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/impute/images/thumb/sphx_glr_plot_iterative_imputer_variants_comparison_thumb.png :alt: Imputing missing values with variants of IterativeImputer :ref:`sphx_glr_auto_examples_impute_plot_iterative_imputer_variants_comparison.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/impute/plot_iterative_imputer_variants_comparison .. raw:: html
.. only:: html .. figure:: /auto_examples/impute/images/thumb/sphx_glr_plot_missing_values_thumb.png :alt: Imputing missing values before building an estimator :ref:`sphx_glr_auto_examples_impute_plot_missing_values.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/impute/plot_missing_values .. raw:: html
.. _sphx_glr_auto_examples_model_selection: .. _model_selection_examples: Model Selection ----------------------- Examples related to the :mod:`sklearn.model_selection` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_cv_predict_thumb.png :alt: Plotting Cross-Validated Predictions :ref:`sphx_glr_auto_examples_model_selection_plot_cv_predict.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_cv_predict .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_confusion_matrix_thumb.png :alt: Confusion matrix :ref:`sphx_glr_auto_examples_model_selection_plot_confusion_matrix.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_confusion_matrix .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_validation_curve_thumb.png :alt: Plotting Validation Curves :ref:`sphx_glr_auto_examples_model_selection_plot_validation_curve.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_validation_curve .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_det_thumb.png :alt: Detection error tradeoff (DET) curve :ref:`sphx_glr_auto_examples_model_selection_plot_det.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_det .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_successive_halving_iterations_thumb.png :alt: Successive Halving Iterations :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_iterations.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_successive_halving_iterations .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_underfitting_overfitting_thumb.png :alt: Underfitting vs. Overfitting :ref:`sphx_glr_auto_examples_model_selection_plot_underfitting_overfitting.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_underfitting_overfitting .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_grid_search_digits_thumb.png :alt: Parameter estimation using grid search with cross-validation :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_grid_search_digits .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_randomized_search_thumb.png :alt: Comparing randomized search and grid search for hyperparameter estimation :ref:`sphx_glr_auto_examples_model_selection_plot_randomized_search.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_randomized_search .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_train_error_vs_test_error_thumb.png :alt: Train error vs Test error :ref:`sphx_glr_auto_examples_model_selection_plot_train_error_vs_test_error.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_train_error_vs_test_error .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_roc_crossval_thumb.png :alt: Receiver Operating Characteristic (ROC) with cross validation :ref:`sphx_glr_auto_examples_model_selection_plot_roc_crossval.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_roc_crossval .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_nested_cross_validation_iris_thumb.png :alt: Nested versus non-nested cross-validation :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_nested_cross_validation_iris .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_multi_metric_evaluation_thumb.png :alt: Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV :ref:`sphx_glr_auto_examples_model_selection_plot_multi_metric_evaluation.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_multi_metric_evaluation .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_grid_search_text_feature_extraction_thumb.png :alt: Sample pipeline for text feature extraction and evaluation :ref:`sphx_glr_auto_examples_model_selection_grid_search_text_feature_extraction.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/grid_search_text_feature_extraction .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_grid_search_refit_callable_thumb.png :alt: Balance model complexity and cross-validated score :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_grid_search_refit_callable .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_successive_halving_heatmap_thumb.png :alt: Comparison between grid search and successive halving :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_heatmap.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_successive_halving_heatmap .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_cv_indices_thumb.png :alt: Visualizing cross-validation behavior in scikit-learn :ref:`sphx_glr_auto_examples_model_selection_plot_cv_indices.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_cv_indices .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_roc_thumb.png :alt: Receiver Operating Characteristic (ROC) :ref:`sphx_glr_auto_examples_model_selection_plot_roc.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_roc .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_precision_recall_thumb.png :alt: Precision-Recall :ref:`sphx_glr_auto_examples_model_selection_plot_precision_recall.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_precision_recall .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_learning_curve_thumb.png :alt: Plotting Learning Curves :ref:`sphx_glr_auto_examples_model_selection_plot_learning_curve.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_learning_curve .. raw:: html
.. only:: html .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_grid_search_stats_thumb.png :alt: Statistical comparison of models using grid search :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_stats.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/model_selection/plot_grid_search_stats .. raw:: html
.. _sphx_glr_auto_examples_multioutput: .. _multioutput_examples: Multioutput methods ------------------- Examples concerning the :mod:`sklearn.multioutput` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/multioutput/images/thumb/sphx_glr_plot_classifier_chain_yeast_thumb.png :alt: Classifier Chain :ref:`sphx_glr_auto_examples_multioutput_plot_classifier_chain_yeast.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/multioutput/plot_classifier_chain_yeast .. raw:: html
.. _sphx_glr_auto_examples_neighbors: .. _neighbors_examples: Nearest Neighbors ----------------------- Examples concerning the :mod:`sklearn.neighbors` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_regression_thumb.png :alt: Nearest Neighbors regression :ref:`sphx_glr_auto_examples_neighbors_plot_regression.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/neighbors/plot_regression .. raw:: html
.. only:: html .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_lof_outlier_detection_thumb.png :alt: Outlier detection with Local Outlier Factor (LOF) :ref:`sphx_glr_auto_examples_neighbors_plot_lof_outlier_detection.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/neighbors/plot_lof_outlier_detection .. raw:: html
.. only:: html .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_nearest_centroid_thumb.png :alt: Nearest Centroid Classification :ref:`sphx_glr_auto_examples_neighbors_plot_nearest_centroid.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/neighbors/plot_nearest_centroid .. raw:: html
.. only:: html .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_digits_kde_sampling_thumb.png :alt: Kernel Density Estimation :ref:`sphx_glr_auto_examples_neighbors_plot_digits_kde_sampling.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/neighbors/plot_digits_kde_sampling .. raw:: html
.. only:: html .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_classification_thumb.png :alt: Nearest Neighbors Classification :ref:`sphx_glr_auto_examples_neighbors_plot_classification.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/neighbors/plot_classification .. raw:: html
.. only:: html .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_caching_nearest_neighbors_thumb.png :alt: Caching nearest neighbors :ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/neighbors/plot_caching_nearest_neighbors .. raw:: html
.. only:: html .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_nca_illustration_thumb.png :alt: Neighborhood Components Analysis Illustration :ref:`sphx_glr_auto_examples_neighbors_plot_nca_illustration.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/neighbors/plot_nca_illustration .. raw:: html
.. only:: html .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_lof_novelty_detection_thumb.png :alt: Novelty detection with Local Outlier Factor (LOF) :ref:`sphx_glr_auto_examples_neighbors_plot_lof_novelty_detection.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/neighbors/plot_lof_novelty_detection .. raw:: html
.. only:: html .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_nca_classification_thumb.png :alt: Comparing Nearest Neighbors with and without Neighborhood Components Analysis :ref:`sphx_glr_auto_examples_neighbors_plot_nca_classification.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/neighbors/plot_nca_classification .. raw:: html
.. only:: html .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_nca_dim_reduction_thumb.png :alt: Dimensionality Reduction with Neighborhood Components Analysis :ref:`sphx_glr_auto_examples_neighbors_plot_nca_dim_reduction.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/neighbors/plot_nca_dim_reduction .. raw:: html
.. only:: html .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_species_kde_thumb.png :alt: Kernel Density Estimate of Species Distributions :ref:`sphx_glr_auto_examples_neighbors_plot_species_kde.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/neighbors/plot_species_kde .. raw:: html
.. only:: html .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_kde_1d_thumb.png :alt: Simple 1D Kernel Density Estimation :ref:`sphx_glr_auto_examples_neighbors_plot_kde_1d.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/neighbors/plot_kde_1d .. raw:: html
.. only:: html .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_approximate_nearest_neighbors_thumb.png :alt: Approximate nearest neighbors in TSNE :ref:`sphx_glr_auto_examples_neighbors_approximate_nearest_neighbors.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/neighbors/approximate_nearest_neighbors .. raw:: html
.. _sphx_glr_auto_examples_neural_networks: .. _neural_network_examples: Neural Networks ----------------------- Examples concerning the :mod:`sklearn.neural_network` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/neural_networks/images/thumb/sphx_glr_plot_mnist_filters_thumb.png :alt: Visualization of MLP weights on MNIST :ref:`sphx_glr_auto_examples_neural_networks_plot_mnist_filters.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/neural_networks/plot_mnist_filters .. raw:: html
.. only:: html .. figure:: /auto_examples/neural_networks/images/thumb/sphx_glr_plot_rbm_logistic_classification_thumb.png :alt: Restricted Boltzmann Machine features for digit classification :ref:`sphx_glr_auto_examples_neural_networks_plot_rbm_logistic_classification.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/neural_networks/plot_rbm_logistic_classification .. raw:: html
.. only:: html .. figure:: /auto_examples/neural_networks/images/thumb/sphx_glr_plot_mlp_training_curves_thumb.png :alt: Compare Stochastic learning strategies for MLPClassifier :ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_training_curves.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/neural_networks/plot_mlp_training_curves .. raw:: html
.. only:: html .. figure:: /auto_examples/neural_networks/images/thumb/sphx_glr_plot_mlp_alpha_thumb.png :alt: Varying regularization in Multi-layer Perceptron :ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_alpha.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/neural_networks/plot_mlp_alpha .. raw:: html
.. _sphx_glr_auto_examples_compose: .. _compose_examples: Pipelines and composite estimators ---------------------------------- Examples of how to compose transformers and pipelines from other estimators. See the :ref:`User Guide `. .. raw:: html
.. only:: html .. figure:: /auto_examples/compose/images/thumb/sphx_glr_plot_feature_union_thumb.png :alt: Concatenating multiple feature extraction methods :ref:`sphx_glr_auto_examples_compose_plot_feature_union.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/compose/plot_feature_union .. raw:: html
.. only:: html .. figure:: /auto_examples/compose/images/thumb/sphx_glr_plot_digits_pipe_thumb.png :alt: Pipelining: chaining a PCA and a logistic regression :ref:`sphx_glr_auto_examples_compose_plot_digits_pipe.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/compose/plot_digits_pipe .. raw:: html
.. only:: html .. figure:: /auto_examples/compose/images/thumb/sphx_glr_plot_compare_reduction_thumb.png :alt: Selecting dimensionality reduction with Pipeline and GridSearchCV :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/compose/plot_compare_reduction .. raw:: html
.. only:: html .. figure:: /auto_examples/compose/images/thumb/sphx_glr_plot_column_transformer_mixed_types_thumb.png :alt: Column Transformer with Mixed Types :ref:`sphx_glr_auto_examples_compose_plot_column_transformer_mixed_types.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/compose/plot_column_transformer_mixed_types .. raw:: html
.. only:: html .. figure:: /auto_examples/compose/images/thumb/sphx_glr_plot_column_transformer_thumb.png :alt: Column Transformer with Heterogeneous Data Sources :ref:`sphx_glr_auto_examples_compose_plot_column_transformer.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/compose/plot_column_transformer .. raw:: html
.. only:: html .. figure:: /auto_examples/compose/images/thumb/sphx_glr_plot_transformed_target_thumb.png :alt: Effect of transforming the targets in regression model :ref:`sphx_glr_auto_examples_compose_plot_transformed_target.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/compose/plot_transformed_target .. raw:: html
.. _sphx_glr_auto_examples_preprocessing: .. _preprocessing_examples: Preprocessing ------------- Examples concerning the :mod:`sklearn.preprocessing` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/preprocessing/images/thumb/sphx_glr_plot_discretization_thumb.png :alt: Using KBinsDiscretizer to discretize continuous features :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/preprocessing/plot_discretization .. raw:: html
.. only:: html .. figure:: /auto_examples/preprocessing/images/thumb/sphx_glr_plot_discretization_strategies_thumb.png :alt: Demonstrating the different strategies of KBinsDiscretizer :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_strategies.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/preprocessing/plot_discretization_strategies .. raw:: html
.. only:: html .. figure:: /auto_examples/preprocessing/images/thumb/sphx_glr_plot_scaling_importance_thumb.png :alt: Importance of Feature Scaling :ref:`sphx_glr_auto_examples_preprocessing_plot_scaling_importance.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/preprocessing/plot_scaling_importance .. raw:: html
.. only:: html .. figure:: /auto_examples/preprocessing/images/thumb/sphx_glr_plot_map_data_to_normal_thumb.png :alt: Map data to a normal distribution :ref:`sphx_glr_auto_examples_preprocessing_plot_map_data_to_normal.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/preprocessing/plot_map_data_to_normal .. raw:: html
.. only:: html .. figure:: /auto_examples/preprocessing/images/thumb/sphx_glr_plot_discretization_classification_thumb.png :alt: Feature discretization :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_classification.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/preprocessing/plot_discretization_classification .. raw:: html
.. only:: html .. figure:: /auto_examples/preprocessing/images/thumb/sphx_glr_plot_all_scaling_thumb.png :alt: Compare the effect of different scalers on data with outliers :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/preprocessing/plot_all_scaling .. raw:: html
.. _sphx_glr_auto_examples_semi_supervised: .. _semi_supervised_examples: Semi Supervised Classification ------------------------------ Examples concerning the :mod:`sklearn.semi_supervised` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_structure_thumb.png :alt: Label Propagation learning a complex structure :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_structure.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/semi_supervised/plot_label_propagation_structure .. raw:: html
.. only:: html .. figure:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_digits_thumb.png :alt: Label Propagation digits: Demonstrating performance :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/semi_supervised/plot_label_propagation_digits .. raw:: html
.. only:: html .. figure:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_semi_supervised_versus_svm_iris_thumb.png :alt: Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset :ref:`sphx_glr_auto_examples_semi_supervised_plot_semi_supervised_versus_svm_iris.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris .. raw:: html
.. only:: html .. figure:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_self_training_varying_threshold_thumb.png :alt: Effect of varying threshold for self-training :ref:`sphx_glr_auto_examples_semi_supervised_plot_self_training_varying_threshold.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/semi_supervised/plot_self_training_varying_threshold .. raw:: html
.. only:: html .. figure:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_semi_supervised_newsgroups_thumb.png :alt: Semi-supervised Classification on a Text Dataset :ref:`sphx_glr_auto_examples_semi_supervised_plot_semi_supervised_newsgroups.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/semi_supervised/plot_semi_supervised_newsgroups .. raw:: html
.. only:: html .. figure:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_digits_active_learning_thumb.png :alt: Label Propagation digits active learning :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits_active_learning.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/semi_supervised/plot_label_propagation_digits_active_learning .. raw:: html
.. _sphx_glr_auto_examples_svm: .. _svm_examples: Support Vector Machines ----------------------- Examples concerning the :mod:`sklearn.svm` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_svm_nonlinear_thumb.png :alt: Non-linear SVM :ref:`sphx_glr_auto_examples_svm_plot_svm_nonlinear.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/svm/plot_svm_nonlinear .. raw:: html
.. only:: html .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_separating_hyperplane_thumb.png :alt: SVM: Maximum margin separating hyperplane :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/svm/plot_separating_hyperplane .. raw:: html
.. only:: html .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_custom_kernel_thumb.png :alt: SVM with custom kernel :ref:`sphx_glr_auto_examples_svm_plot_custom_kernel.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/svm/plot_custom_kernel .. raw:: html
.. only:: html .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_svm_tie_breaking_thumb.png :alt: SVM Tie Breaking Example :ref:`sphx_glr_auto_examples_svm_plot_svm_tie_breaking.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/svm/plot_svm_tie_breaking .. raw:: html
.. only:: html .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_weighted_samples_thumb.png :alt: SVM: Weighted samples :ref:`sphx_glr_auto_examples_svm_plot_weighted_samples.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/svm/plot_weighted_samples .. raw:: html
.. only:: html .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_linearsvc_support_vectors_thumb.png :alt: Plot the support vectors in LinearSVC :ref:`sphx_glr_auto_examples_svm_plot_linearsvc_support_vectors.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/svm/plot_linearsvc_support_vectors .. raw:: html
.. only:: html .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_separating_hyperplane_unbalanced_thumb.png :alt: SVM: Separating hyperplane for unbalanced classes :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane_unbalanced.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/svm/plot_separating_hyperplane_unbalanced .. raw:: html
.. only:: html .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_svm_kernels_thumb.png :alt: SVM-Kernels :ref:`sphx_glr_auto_examples_svm_plot_svm_kernels.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/svm/plot_svm_kernels .. raw:: html
.. only:: html .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_svm_anova_thumb.png :alt: SVM-Anova: SVM with univariate feature selection :ref:`sphx_glr_auto_examples_svm_plot_svm_anova.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/svm/plot_svm_anova .. raw:: html
.. only:: html .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_svm_regression_thumb.png :alt: Support Vector Regression (SVR) using linear and non-linear kernels :ref:`sphx_glr_auto_examples_svm_plot_svm_regression.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/svm/plot_svm_regression .. raw:: html
.. only:: html .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_svm_margin_thumb.png :alt: SVM Margins Example :ref:`sphx_glr_auto_examples_svm_plot_svm_margin.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/svm/plot_svm_margin .. raw:: html
.. only:: html .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_oneclass_thumb.png :alt: One-class SVM with non-linear kernel (RBF) :ref:`sphx_glr_auto_examples_svm_plot_oneclass.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/svm/plot_oneclass .. raw:: html
.. only:: html .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_iris_svc_thumb.png :alt: Plot different SVM classifiers in the iris dataset :ref:`sphx_glr_auto_examples_svm_plot_iris_svc.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/svm/plot_iris_svc .. raw:: html
.. only:: html .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_svm_scale_c_thumb.png :alt: Scaling the regularization parameter for SVCs :ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/svm/plot_svm_scale_c .. raw:: html
.. only:: html .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_rbf_parameters_thumb.png :alt: RBF SVM parameters :ref:`sphx_glr_auto_examples_svm_plot_rbf_parameters.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/svm/plot_rbf_parameters .. raw:: html
.. _sphx_glr_auto_examples_exercises: Tutorial exercises ------------------ Exercises for the tutorials .. raw:: html
.. only:: html .. figure:: /auto_examples/exercises/images/thumb/sphx_glr_plot_digits_classification_exercise_thumb.png :alt: Digits Classification Exercise :ref:`sphx_glr_auto_examples_exercises_plot_digits_classification_exercise.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/exercises/plot_digits_classification_exercise .. raw:: html
.. only:: html .. figure:: /auto_examples/exercises/images/thumb/sphx_glr_plot_cv_digits_thumb.png :alt: Cross-validation on Digits Dataset Exercise :ref:`sphx_glr_auto_examples_exercises_plot_cv_digits.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/exercises/plot_cv_digits .. raw:: html
.. only:: html .. figure:: /auto_examples/exercises/images/thumb/sphx_glr_plot_iris_exercise_thumb.png :alt: SVM Exercise :ref:`sphx_glr_auto_examples_exercises_plot_iris_exercise.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/exercises/plot_iris_exercise .. raw:: html
.. only:: html .. figure:: /auto_examples/exercises/images/thumb/sphx_glr_plot_cv_diabetes_thumb.png :alt: Cross-validation on diabetes Dataset Exercise :ref:`sphx_glr_auto_examples_exercises_plot_cv_diabetes.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/exercises/plot_cv_diabetes .. raw:: html
.. _sphx_glr_auto_examples_text: .. _text_examples: Working with text documents ---------------------------- Examples concerning the :mod:`sklearn.feature_extraction.text` module. .. raw:: html
.. only:: html .. figure:: /auto_examples/text/images/thumb/sphx_glr_plot_hashing_vs_dict_vectorizer_thumb.png :alt: FeatureHasher and DictVectorizer Comparison :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/text/plot_hashing_vs_dict_vectorizer .. raw:: html
.. only:: html .. figure:: /auto_examples/text/images/thumb/sphx_glr_plot_document_clustering_thumb.png :alt: Clustering text documents using k-means :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/text/plot_document_clustering .. raw:: html
.. only:: html .. figure:: /auto_examples/text/images/thumb/sphx_glr_plot_document_classification_20newsgroups_thumb.png :alt: Classification of text documents using sparse features :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` .. raw:: html
.. toctree:: :hidden: /auto_examples/text/plot_document_classification_20newsgroups .. raw:: html
.. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-gallery .. container:: sphx-glr-download sphx-glr-download-python :download:`Download all examples in Python source code: auto_examples_python.zip ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_