:orphan:
.. _general_examples:
Examples
========
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Release Highlights
------------------
These examples illustrate the main features of the releases of scikit-learn.
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.. image:: /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`
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A demo of K-Means clustering on the handwritten digits data
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.. image:: /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`
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A demo of structured Ward hierarchical clustering on an image of coins
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.. image:: /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`
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A demo of the mean-shift clustering algorithm
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.. image:: /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`
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Adjustment for chance in clustering performance evaluation
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.. image:: /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`
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Agglomerative clustering with and without structure
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.. image:: /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`
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Agglomerative clustering with different metrics
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.. image:: /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`
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An example of K-Means++ initialization
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.. image:: /auto_examples/cluster/images/thumb/sphx_glr_plot_bisect_kmeans_thumb.png
:alt: Bisecting K-Means and Regular K-Means Performance Comparison
:ref:`sphx_glr_auto_examples_cluster_plot_bisect_kmeans.py`
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Bisecting K-Means and Regular K-Means Performance Comparison
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.. image:: /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`
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Color Quantization using K-Means
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.. image:: /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`
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Compare BIRCH and MiniBatchKMeans
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.. image:: /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`
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Comparing different clustering algorithms on toy datasets
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.. image:: /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`
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Comparing different hierarchical linkage methods on toy datasets
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.. image:: /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`
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Comparison of the K-Means and MiniBatchKMeans clustering algorithms
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.. image:: /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`
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Demo of DBSCAN clustering algorithm
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.. image:: /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`
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Demo of OPTICS clustering algorithm
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.. image:: /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`
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Demo of affinity propagation clustering algorithm
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.. image:: /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`
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Demonstration of k-means assumptions
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.. image:: /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`
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Empirical evaluation of the impact of k-means initialization
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.. image:: /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`
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Feature agglomeration
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.. image:: /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`
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Feature agglomeration vs. univariate selection
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.. image:: /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`
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Hierarchical clustering: structured vs unstructured ward
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.. image:: /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`
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Inductive Clustering
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.. image:: /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`
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K-means Clustering
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.. image:: /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`
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Online learning of a dictionary of parts of faces
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.. image:: /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`
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Plot Hierarchical Clustering Dendrogram
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.. image:: /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`
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Segmenting the picture of greek coins in regions
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.. image:: /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`
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Selecting the number of clusters with silhouette analysis on KMeans clustering
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.. image:: /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`
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Spectral clustering for image segmentation
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.. image:: /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`
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Various Agglomerative Clustering on a 2D embedding of digits
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.. image:: /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`
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Vector Quantization Example
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Covariance estimation
---------------------
Examples concerning the :mod:`sklearn.covariance` module.
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.. image:: /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`
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Beta-divergence loss functions
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.. image:: /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`
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Blind source separation using FastICA
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.. image:: /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`
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Comparison of LDA and PCA 2D projection of Iris dataset
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.. image:: /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`
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Faces dataset decompositions
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.. image:: /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`
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Factor Analysis (with rotation) to visualize patterns
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.. image:: /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`
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FastICA on 2D point clouds
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.. image:: /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`
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Image denoising using dictionary learning
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.. image:: /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`
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Incremental PCA
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.. image:: /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`
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Kernel PCA
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.. image:: /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`
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Model selection with Probabilistic PCA and Factor Analysis (FA)
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.. image:: /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`
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PCA example with Iris Data-set
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.. image:: /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`
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Principal components analysis (PCA)
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.. image:: /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`
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Sparse coding with a precomputed dictionary
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Ensemble methods
----------------
Examples concerning the :mod:`sklearn.ensemble` module.
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.. image:: /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`
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Categorical Feature Support in Gradient Boosting
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.. image:: /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`
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Combine predictors using stacking
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.. image:: /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`
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Comparing random forests and the multi-output meta estimator
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.. image:: /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`
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Decision Tree Regression with AdaBoost
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.. image:: /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`
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Discrete versus Real AdaBoost
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.. image:: /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`
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Early stopping of Gradient Boosting
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.. image:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_forest_importances_thumb.png
:alt: Feature importances with a forest of trees
:ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances.py`
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Feature importances with a forest of trees
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.. image:: /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`
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Feature transformations with ensembles of trees
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.. image:: /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`
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Gradient Boosting Out-of-Bag estimates
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.. image:: /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`
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Gradient Boosting regression
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.. image:: /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`
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Gradient Boosting regularization
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.. image:: /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`
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Hashing feature transformation using Totally Random Trees
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.. image:: /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`
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IsolationForest example
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.. image:: /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`
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Monotonic Constraints
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.. image:: /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`
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Multi-class AdaBoosted Decision Trees
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.. image:: /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`
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OOB Errors for Random Forests
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.. image:: /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`
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Pixel importances with a parallel forest of trees
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.. image:: /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`
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Plot class probabilities calculated by the VotingClassifier
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.. image:: /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`
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Plot individual and voting regression predictions
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.. image:: /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`
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Plot the decision boundaries of a VotingClassifier
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.. image:: /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`
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Plot the decision surfaces of ensembles of trees on the iris dataset
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.. image:: /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`
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Prediction Intervals for Gradient Boosting Regression
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.. image:: /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`
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Single estimator versus bagging: bias-variance decomposition
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.. image:: /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`
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Two-class AdaBoost
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Examples based on real world datasets
-------------------------------------
Applications to real world problems with some medium sized datasets or
interactive user interface.
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.. image:: /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`
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Compressive sensing: tomography reconstruction with L1 prior (Lasso)
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.. image:: /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`
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Faces recognition example using eigenfaces and SVMs
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.. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_digits_denoising_thumb.png
:alt: Image denoising using kernel PCA
:ref:`sphx_glr_auto_examples_applications_plot_digits_denoising.py`
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Image denoising using kernel PCA
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.. image:: /auto_examples/applications/images/thumb/sphx_glr_svm_gui_thumb.png
:alt: Libsvm GUI
:ref:`sphx_glr_auto_examples_applications_svm_gui.py`
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Libsvm GUI
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.. image:: /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`
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Model Complexity Influence
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.. image:: /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`
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Out-of-core classification of text documents
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.. image:: /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`
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Outlier detection on a real data set
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.. image:: /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`
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Prediction Latency
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.. image:: /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`
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Species distribution modeling
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.. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_cyclical_feature_engineering_thumb.png
:alt: Time-related feature engineering
:ref:`sphx_glr_auto_examples_applications_plot_cyclical_feature_engineering.py`
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Time-related feature engineering
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.. image:: /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`
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Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation
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.. image:: /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`
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Visualizing the stock market structure
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.. image:: /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`
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Wikipedia principal eigenvector
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Feature Selection
-----------------------
Examples concerning the :mod:`sklearn.feature_selection` module.
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.. image:: /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`
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Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture
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.. image:: /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`
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Density Estimation for a Gaussian mixture
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.. image:: /auto_examples/mixture/images/thumb/sphx_glr_plot_gmm_init_thumb.png
:alt: GMM Initialization Methods
:ref:`sphx_glr_auto_examples_mixture_plot_gmm_init.py`
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GMM Initialization Methods
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.. image:: /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`
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GMM covariances
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.. image:: /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`
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Gaussian Mixture Model Ellipsoids
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.. image:: /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`
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Gaussian Mixture Model Selection
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.. image:: /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`
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Gaussian Mixture Model Sine Curve
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Gaussian Process for Machine Learning
-------------------------------------
Examples concerning the :mod:`sklearn.gaussian_process` module.
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.. image:: /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`
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Comparison of kernel ridge and Gaussian process regression
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.. image:: /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`
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Gaussian Processes regression: basic introductory example
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.. image:: /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`
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Gaussian process classification (GPC) on iris dataset
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.. image:: /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`
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Gaussian process regression (GPR) on Mauna Loa CO2 data
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.. image:: /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`
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Gaussian process regression (GPR) with noise-level estimation
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.. image:: /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`
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Gaussian processes on discrete data structures
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.. image:: /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`
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Illustration of Gaussian process classification (GPC) on the XOR dataset
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.. image:: /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`
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Illustration of prior and posterior Gaussian process for different kernels
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.. image:: /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`
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Iso-probability lines for Gaussian Processes classification (GPC)
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.. image:: /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`
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Probabilistic predictions with Gaussian process classification (GPC)
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Generalized Linear Models
-------------------------
Examples concerning the :mod:`sklearn.linear_model` module.
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.. image:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_ard_thumb.png
:alt: Comparing Linear Bayesian Regressors
:ref:`sphx_glr_auto_examples_linear_model_plot_ard.py`
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Comparing Linear Bayesian Regressors
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.. image:: /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`
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Comparing various online solvers
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.. image:: /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`
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Curve Fitting with Bayesian Ridge Regression
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.. image:: /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`
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Early stopping of Stochastic Gradient Descent
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.. image:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_elastic_net_precomputed_gram_matrix_with_weighted_samples_thumb.png
:alt: Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples
:ref:`sphx_glr_auto_examples_linear_model_plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py`
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Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples
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.. image:: /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`
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HuberRegressor vs Ridge on dataset with strong outliers
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.. image:: /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`
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Joint feature selection with multi-task Lasso
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.. image:: /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`
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L1 Penalty and Sparsity in Logistic Regression
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.. image:: /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`
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Lasso and Elastic Net
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.. image:: /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`
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Lasso and Elastic Net for Sparse Signals
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.. image:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_lasso_lars_ic_thumb.png
:alt: Lasso model selection via information criteria
:ref:`sphx_glr_auto_examples_linear_model_plot_lasso_lars_ic.py`
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Lasso model selection via information criteria
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.. image:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_lasso_model_selection_thumb.png
:alt: Lasso model selection: AIC-BIC / cross-validation
:ref:`sphx_glr_auto_examples_linear_model_plot_lasso_model_selection.py`
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Lasso model selection: AIC-BIC / cross-validation
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.. image:: /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`
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Lasso on dense and sparse data
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.. image:: /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`
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Lasso path using LARS
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.. image:: /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`
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Linear Regression Example
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.. image:: /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`
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Logistic Regression 3-class Classifier
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.. image:: /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`
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Logistic function
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.. image:: /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`
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MNIST classification using multinomial logistic + L1
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.. image:: /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`
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Multiclass sparse logistic regression on 20newgroups
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.. image:: /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`
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Non-negative least squares
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.. image:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sgdocsvm_vs_ocsvm_thumb.png
:alt: One-Class SVM versus One-Class SVM using Stochastic Gradient Descent
:ref:`sphx_glr_auto_examples_linear_model_plot_sgdocsvm_vs_ocsvm.py`
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One-Class SVM versus One-Class SVM using Stochastic Gradient Descent
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.. image:: /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`
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Ordinary Least Squares and Ridge Regression Variance
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.. image:: /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`
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Orthogonal Matching Pursuit
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.. image:: /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`
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Plot Ridge coefficients as a function of the L2 regularization
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.. image:: /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`
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Plot Ridge coefficients as a function of the regularization
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.. image:: /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`
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Plot multi-class SGD on the iris dataset
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.. image:: /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`
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Plot multinomial and One-vs-Rest Logistic Regression
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.. image:: /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`
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Poisson regression and non-normal loss
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.. image:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_polynomial_interpolation_thumb.png
:alt: Polynomial and Spline interpolation
:ref:`sphx_glr_auto_examples_linear_model_plot_polynomial_interpolation.py`
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Polynomial and Spline interpolation
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.. image:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_quantile_regression_thumb.png
:alt: Quantile regression
:ref:`sphx_glr_auto_examples_linear_model_plot_quantile_regression.py`
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Quantile regression
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.. image:: /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`
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Regularization path of L1- Logistic Regression
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.. image:: /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`
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Robust linear estimator fitting
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.. image:: /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`
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Robust linear model estimation using RANSAC
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.. image:: /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`
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SGD: Maximum margin separating hyperplane
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.. image:: /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`
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SGD: Penalties
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.. image:: /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`
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SGD: Weighted samples
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.. image:: /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`
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SGD: convex loss functions
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.. image:: /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`
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Sparsity Example: Fitting only features 1 and 2
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.. image:: /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`
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Theil-Sen Regression
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.. image:: /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`
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Tweedie regression on insurance claims
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Inspection
----------
Examples related to the :mod:`sklearn.inspection` module.
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.. image:: /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`
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Advanced Plotting With Partial Dependence
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.. image:: /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`
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Compact estimator representations
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.. image:: /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`
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Comparing anomaly detection algorithms for outlier detection on toy datasets
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.. image:: /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`
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Comparison of kernel ridge regression and SVR
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.. image:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_pipeline_display_thumb.png
:alt: Displaying Pipelines
:ref:`sphx_glr_auto_examples_miscellaneous_plot_pipeline_display.py`
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Displaying Pipelines
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.. image:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_outlier_detection_bench_thumb.png
:alt: Evaluation of outlier detection estimators
:ref:`sphx_glr_auto_examples_miscellaneous_plot_outlier_detection_bench.py`
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Evaluation of outlier detection estimators
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.. image:: /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`
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Explicit feature map approximation for RBF kernels
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.. image:: /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`
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Face completion with a multi-output estimators
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.. image:: /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`
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Isotonic Regression
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.. image:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_multilabel_thumb.png
:alt: Multilabel classification
:ref:`sphx_glr_auto_examples_miscellaneous_plot_multilabel.py`
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Multilabel classification
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.. image:: /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`
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ROC Curve with Visualization API
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.. image:: /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`
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The Johnson-Lindenstrauss bound for embedding with random projections
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.. image:: /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`
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Visualizations with Display Objects
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Missing Value Imputation
------------------------
Examples concerning the :mod:`sklearn.impute` module.
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.. image:: /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`
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Balance model complexity and cross-validated score
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.. image:: /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`
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Comparing randomized search and grid search for hyperparameter estimation
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.. image:: /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`
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Comparison between grid search and successive halving
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.. image:: /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`
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Confusion matrix
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.. image:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_grid_search_digits_thumb.png
:alt: Custom refit strategy of a grid search with cross-validation
:ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`
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Custom refit strategy of a grid search with cross-validation
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.. image:: /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`
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Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV
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.. image:: /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`
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Detection error tradeoff (DET) curve
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.. image:: /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`
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Nested versus non-nested cross-validation
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.. image:: /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`
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Plotting Cross-Validated Predictions
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.. image:: /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`
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Plotting Learning Curves
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.. image:: /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`
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Plotting Validation Curves
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.. image:: /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`
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Precision-Recall
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.. image:: /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`
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Receiver Operating Characteristic (ROC)
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.. image:: /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`
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Receiver Operating Characteristic (ROC) with cross validation
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.. image:: /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`
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Sample pipeline for text feature extraction and evaluation
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.. image:: /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`
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Statistical comparison of models using grid search
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.. image:: /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`
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Successive Halving Iterations
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.. image:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_permutation_tests_for_classification_thumb.png
:alt: Test with permutations the significance of a classification score
:ref:`sphx_glr_auto_examples_model_selection_plot_permutation_tests_for_classification.py`
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Test with permutations the significance of a classification score
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.. image:: /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`
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Train error vs Test error
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.. image:: /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`
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Underfitting vs. Overfitting
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.. image:: /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`
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Visualizing cross-validation behavior in scikit-learn
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Multioutput methods
-------------------
Examples concerning the :mod:`sklearn.multioutput` module.
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.. image:: /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`
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Approximate nearest neighbors in TSNE
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.. image:: /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`
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Caching nearest neighbors
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.. image:: /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`
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Comparing Nearest Neighbors with and without Neighborhood Components Analysis
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.. image:: /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`
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Dimensionality Reduction with Neighborhood Components Analysis
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.. image:: /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`
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Kernel Density Estimate of Species Distributions
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.. image:: /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`
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Kernel Density Estimation
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.. image:: /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`
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Nearest Centroid Classification
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.. image:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_classification_thumb.png
:alt: Nearest Neighbors Classification
:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`
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Nearest Neighbors Classification
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.. image:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_regression_thumb.png
:alt: Nearest Neighbors regression
:ref:`sphx_glr_auto_examples_neighbors_plot_regression.py`
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Nearest Neighbors regression
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.. image:: /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`
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Neighborhood Components Analysis Illustration
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.. image:: /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`
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Novelty detection with Local Outlier Factor (LOF)
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.. image:: /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`
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Outlier detection with Local Outlier Factor (LOF)
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.. image:: /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`
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Simple 1D Kernel Density Estimation
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Neural Networks
-----------------------
Examples concerning the :mod:`sklearn.neural_network` module.
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