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doc/modules/classes.rst
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Guillaume Lemaitre
DOC fixes references issues given by sphinx (#27432)
03 окт 2023, 21:25
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.. _api_ref: ============= API Reference ============= This is the class and function reference of scikit-learn. Please refer to the :ref:`full user guide <user_guide>` for further details, as the class and function raw specifications may not be enough to give full guidelines on their uses. For reference on concepts repeated across the API, see :ref:`glossary`. :mod:`sklearn`: Settings and information tools ============================================== .. automodule:: sklearn :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst config_context get_config set_config show_versions :mod:`sklearn.base`: Base classes and utility functions ======================================================= .. automodule:: sklearn.base :no-members: :no-inherited-members: Base classes ------------ .. currentmodule:: sklearn .. autosummary:: :nosignatures: :toctree: generated/ :template: class.rst base.BaseEstimator base.BiclusterMixin base.ClassifierMixin base.ClusterMixin base.DensityMixin base.RegressorMixin base.TransformerMixin base.MetaEstimatorMixin base.OneToOneFeatureMixin base.OutlierMixin base.ClassNamePrefixFeaturesOutMixin feature_selection.SelectorMixin Functions --------- .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst base.clone base.is_classifier base.is_regressor .. _calibration_ref: :mod:`sklearn.calibration`: Probability Calibration =================================================== .. automodule:: sklearn.calibration :no-members: :no-inherited-members: **User guide:** See the :ref:`calibration` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst calibration.CalibratedClassifierCV .. autosummary:: :toctree: generated/ :template: function.rst calibration.calibration_curve .. _cluster_ref: :mod:`sklearn.cluster`: Clustering ================================== .. automodule:: sklearn.cluster :no-members: :no-inherited-members: **User guide:** See the :ref:`clustering` and :ref:`biclustering` sections for further details. Classes ------- .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst cluster.AffinityPropagation cluster.AgglomerativeClustering cluster.Birch cluster.DBSCAN cluster.HDBSCAN cluster.FeatureAgglomeration cluster.KMeans cluster.BisectingKMeans cluster.MiniBatchKMeans cluster.MeanShift cluster.OPTICS cluster.SpectralClustering cluster.SpectralBiclustering cluster.SpectralCoclustering Functions --------- .. autosummary:: :toctree: generated/ :template: function.rst cluster.affinity_propagation cluster.cluster_optics_dbscan cluster.cluster_optics_xi cluster.compute_optics_graph cluster.dbscan cluster.estimate_bandwidth cluster.k_means cluster.kmeans_plusplus cluster.mean_shift cluster.spectral_clustering cluster.ward_tree .. _compose_ref: :mod:`sklearn.compose`: Composite Estimators ============================================ .. automodule:: sklearn.compose :no-members: :no-inherited-members: **User guide:** See the :ref:`combining_estimators` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst compose.ColumnTransformer compose.TransformedTargetRegressor .. autosummary:: :toctree: generated/ :template: function.rst compose.make_column_transformer compose.make_column_selector .. _covariance_ref: :mod:`sklearn.covariance`: Covariance Estimators ================================================ .. automodule:: sklearn.covariance :no-members: :no-inherited-members: **User guide:** See the :ref:`covariance` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst covariance.EmpiricalCovariance covariance.EllipticEnvelope covariance.GraphicalLasso covariance.GraphicalLassoCV covariance.LedoitWolf covariance.MinCovDet covariance.OAS covariance.ShrunkCovariance .. autosummary:: :toctree: generated/ :template: function.rst covariance.empirical_covariance covariance.graphical_lasso covariance.ledoit_wolf covariance.ledoit_wolf_shrinkage covariance.oas covariance.shrunk_covariance .. _cross_decomposition_ref: :mod:`sklearn.cross_decomposition`: Cross decomposition ======================================================= .. automodule:: sklearn.cross_decomposition :no-members: :no-inherited-members: **User guide:** See the :ref:`cross_decomposition` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst cross_decomposition.CCA cross_decomposition.PLSCanonical cross_decomposition.PLSRegression cross_decomposition.PLSSVD .. _datasets_ref: :mod:`sklearn.datasets`: Datasets ================================= .. automodule:: sklearn.datasets :no-members: :no-inherited-members: **User guide:** See the :ref:`datasets` section for further details. Loaders ------- .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst datasets.clear_data_home datasets.dump_svmlight_file datasets.fetch_20newsgroups datasets.fetch_20newsgroups_vectorized datasets.fetch_california_housing datasets.fetch_covtype datasets.fetch_kddcup99 datasets.fetch_lfw_pairs datasets.fetch_lfw_people datasets.fetch_olivetti_faces datasets.fetch_openml datasets.fetch_rcv1 datasets.fetch_species_distributions datasets.get_data_home datasets.load_breast_cancer datasets.load_diabetes datasets.load_digits datasets.load_files datasets.load_iris datasets.load_linnerud datasets.load_sample_image datasets.load_sample_images datasets.load_svmlight_file datasets.load_svmlight_files datasets.load_wine Samples generator ----------------- .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst datasets.make_biclusters datasets.make_blobs datasets.make_checkerboard datasets.make_circles datasets.make_classification datasets.make_friedman1 datasets.make_friedman2 datasets.make_friedman3 datasets.make_gaussian_quantiles datasets.make_hastie_10_2 datasets.make_low_rank_matrix datasets.make_moons datasets.make_multilabel_classification datasets.make_regression datasets.make_s_curve datasets.make_sparse_coded_signal datasets.make_sparse_spd_matrix datasets.make_sparse_uncorrelated datasets.make_spd_matrix datasets.make_swiss_roll .. _decomposition_ref: :mod:`sklearn.decomposition`: Matrix Decomposition ================================================== .. automodule:: sklearn.decomposition :no-members: :no-inherited-members: **User guide:** See the :ref:`decompositions` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst decomposition.DictionaryLearning decomposition.FactorAnalysis decomposition.FastICA decomposition.IncrementalPCA decomposition.KernelPCA decomposition.LatentDirichletAllocation decomposition.MiniBatchDictionaryLearning decomposition.MiniBatchSparsePCA decomposition.NMF decomposition.MiniBatchNMF decomposition.PCA decomposition.SparsePCA decomposition.SparseCoder decomposition.TruncatedSVD .. autosummary:: :toctree: generated/ :template: function.rst decomposition.dict_learning decomposition.dict_learning_online decomposition.fastica decomposition.non_negative_factorization decomposition.sparse_encode .. _lda_ref: :mod:`sklearn.discriminant_analysis`: Discriminant Analysis =========================================================== .. automodule:: sklearn.discriminant_analysis :no-members: :no-inherited-members: **User guide:** See the :ref:`lda_qda` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst discriminant_analysis.LinearDiscriminantAnalysis discriminant_analysis.QuadraticDiscriminantAnalysis .. _dummy_ref: :mod:`sklearn.dummy`: Dummy estimators ====================================== .. automodule:: sklearn.dummy :no-members: :no-inherited-members: **User guide:** See the :ref:`model_evaluation` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst dummy.DummyClassifier dummy.DummyRegressor .. autosummary:: :toctree: generated/ :template: function.rst .. _ensemble_ref: :mod:`sklearn.ensemble`: Ensemble Methods ========================================= .. automodule:: sklearn.ensemble :no-members: :no-inherited-members: **User guide:** See the :ref:`ensemble` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst ensemble.AdaBoostClassifier ensemble.AdaBoostRegressor ensemble.BaggingClassifier ensemble.BaggingRegressor ensemble.ExtraTreesClassifier ensemble.ExtraTreesRegressor ensemble.GradientBoostingClassifier ensemble.GradientBoostingRegressor ensemble.IsolationForest ensemble.RandomForestClassifier ensemble.RandomForestRegressor ensemble.RandomTreesEmbedding ensemble.StackingClassifier ensemble.StackingRegressor ensemble.VotingClassifier ensemble.VotingRegressor ensemble.HistGradientBoostingRegressor ensemble.HistGradientBoostingClassifier .. autosummary:: :toctree: generated/ :template: function.rst .. _exceptions_ref: :mod:`sklearn.exceptions`: Exceptions and warnings ================================================== .. automodule:: sklearn.exceptions :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst exceptions.ConvergenceWarning exceptions.DataConversionWarning exceptions.DataDimensionalityWarning exceptions.EfficiencyWarning exceptions.FitFailedWarning exceptions.InconsistentVersionWarning exceptions.NotFittedError exceptions.UndefinedMetricWarning :mod:`sklearn.experimental`: Experimental ========================================= .. automodule:: sklearn.experimental :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ experimental.enable_iterative_imputer experimental.enable_halving_search_cv .. _feature_extraction_ref: :mod:`sklearn.feature_extraction`: Feature Extraction ===================================================== .. automodule:: sklearn.feature_extraction :no-members: :no-inherited-members: **User guide:** See the :ref:`feature_extraction` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst feature_extraction.DictVectorizer feature_extraction.FeatureHasher From images ----------- .. automodule:: sklearn.feature_extraction.image :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst feature_extraction.image.extract_patches_2d feature_extraction.image.grid_to_graph feature_extraction.image.img_to_graph feature_extraction.image.reconstruct_from_patches_2d :template: class.rst feature_extraction.image.PatchExtractor .. _text_feature_extraction_ref: From text --------- .. automodule:: sklearn.feature_extraction.text :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst feature_extraction.text.CountVectorizer feature_extraction.text.HashingVectorizer feature_extraction.text.TfidfTransformer feature_extraction.text.TfidfVectorizer .. _feature_selection_ref: :mod:`sklearn.feature_selection`: Feature Selection =================================================== .. automodule:: sklearn.feature_selection :no-members: :no-inherited-members: **User guide:** See the :ref:`feature_selection` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst feature_selection.GenericUnivariateSelect feature_selection.SelectPercentile feature_selection.SelectKBest feature_selection.SelectFpr feature_selection.SelectFdr feature_selection.SelectFromModel feature_selection.SelectFwe feature_selection.SequentialFeatureSelector feature_selection.RFE feature_selection.RFECV feature_selection.VarianceThreshold .. autosummary:: :toctree: generated/ :template: function.rst feature_selection.chi2 feature_selection.f_classif feature_selection.f_regression feature_selection.r_regression feature_selection.mutual_info_classif feature_selection.mutual_info_regression .. _gaussian_process_ref: :mod:`sklearn.gaussian_process`: Gaussian Processes =================================================== .. automodule:: sklearn.gaussian_process :no-members: :no-inherited-members: **User guide:** See the :ref:`gaussian_process` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst gaussian_process.GaussianProcessClassifier gaussian_process.GaussianProcessRegressor Kernels ------- .. automodule:: sklearn.gaussian_process.kernels :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class_with_call.rst gaussian_process.kernels.CompoundKernel gaussian_process.kernels.ConstantKernel gaussian_process.kernels.DotProduct gaussian_process.kernels.ExpSineSquared gaussian_process.kernels.Exponentiation gaussian_process.kernels.Hyperparameter gaussian_process.kernels.Kernel gaussian_process.kernels.Matern gaussian_process.kernels.PairwiseKernel gaussian_process.kernels.Product gaussian_process.kernels.RBF gaussian_process.kernels.RationalQuadratic gaussian_process.kernels.Sum gaussian_process.kernels.WhiteKernel .. _impute_ref: :mod:`sklearn.impute`: Impute ============================= .. automodule:: sklearn.impute :no-members: :no-inherited-members: **User guide:** See the :ref:`Impute` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst impute.SimpleImputer impute.IterativeImputer impute.MissingIndicator impute.KNNImputer .. _inspection_ref: :mod:`sklearn.inspection`: Inspection ===================================== .. automodule:: sklearn.inspection :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst inspection.partial_dependence inspection.permutation_importance Plotting -------- .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: display_only_from_estimator.rst inspection.DecisionBoundaryDisplay inspection.PartialDependenceDisplay .. _isotonic_ref: :mod:`sklearn.isotonic`: Isotonic regression ============================================ .. automodule:: sklearn.isotonic :no-members: :no-inherited-members: **User guide:** See the :ref:`isotonic` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst isotonic.IsotonicRegression .. autosummary:: :toctree: generated/ :template: function.rst isotonic.check_increasing isotonic.isotonic_regression .. _kernel_approximation_ref: :mod:`sklearn.kernel_approximation`: Kernel Approximation ========================================================= .. automodule:: sklearn.kernel_approximation :no-members: :no-inherited-members: **User guide:** See the :ref:`kernel_approximation` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst kernel_approximation.AdditiveChi2Sampler kernel_approximation.Nystroem kernel_approximation.PolynomialCountSketch kernel_approximation.RBFSampler kernel_approximation.SkewedChi2Sampler .. _kernel_ridge_ref: :mod:`sklearn.kernel_ridge`: Kernel Ridge Regression ==================================================== .. automodule:: sklearn.kernel_ridge :no-members: :no-inherited-members: **User guide:** See the :ref:`kernel_ridge` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst kernel_ridge.KernelRidge .. _linear_model_ref: :mod:`sklearn.linear_model`: Linear Models ========================================== .. automodule:: sklearn.linear_model :no-members: :no-inherited-members: **User guide:** See the :ref:`linear_model` section for further details. The following subsections are only rough guidelines: the same estimator can fall into multiple categories, depending on its parameters. .. currentmodule:: sklearn Linear classifiers ------------------ .. autosummary:: :toctree: generated/ :template: class.rst linear_model.LogisticRegression linear_model.LogisticRegressionCV linear_model.PassiveAggressiveClassifier linear_model.Perceptron linear_model.RidgeClassifier linear_model.RidgeClassifierCV linear_model.SGDClassifier linear_model.SGDOneClassSVM Classical linear regressors --------------------------- .. autosummary:: :toctree: generated/ :template: class.rst linear_model.LinearRegression linear_model.Ridge linear_model.RidgeCV linear_model.SGDRegressor Regressors with variable selection ---------------------------------- The following estimators have built-in variable selection fitting procedures, but any estimator using a L1 or elastic-net penalty also performs variable selection: typically :class:`~linear_model.SGDRegressor` or :class:`~sklearn.linear_model.SGDClassifier` with an appropriate penalty. .. autosummary:: :toctree: generated/ :template: class.rst linear_model.ElasticNet linear_model.ElasticNetCV linear_model.Lars linear_model.LarsCV linear_model.Lasso linear_model.LassoCV linear_model.LassoLars linear_model.LassoLarsCV linear_model.LassoLarsIC linear_model.OrthogonalMatchingPursuit linear_model.OrthogonalMatchingPursuitCV Bayesian regressors ------------------- .. autosummary:: :toctree: generated/ :template: class.rst linear_model.ARDRegression linear_model.BayesianRidge Multi-task linear regressors with variable selection ---------------------------------------------------- These estimators fit multiple regression problems (or tasks) jointly, while inducing sparse coefficients. While the inferred coefficients may differ between the tasks, they are constrained to agree on the features that are selected (non-zero coefficients). .. autosummary:: :toctree: generated/ :template: class.rst linear_model.MultiTaskElasticNet linear_model.MultiTaskElasticNetCV linear_model.MultiTaskLasso linear_model.MultiTaskLassoCV Outlier-robust regressors ------------------------- Any estimator using the Huber loss would also be robust to outliers, e.g. :class:`~linear_model.SGDRegressor` with ``loss='huber'``. .. autosummary:: :toctree: generated/ :template: class.rst linear_model.HuberRegressor linear_model.QuantileRegressor linear_model.RANSACRegressor linear_model.TheilSenRegressor Generalized linear models (GLM) for regression ---------------------------------------------- These models allow for response variables to have error distributions other than a normal distribution: .. autosummary:: :toctree: generated/ :template: class.rst linear_model.PoissonRegressor linear_model.TweedieRegressor linear_model.GammaRegressor Miscellaneous ------------- .. autosummary:: :toctree: generated/ :template: classes.rst linear_model.PassiveAggressiveRegressor .. autosummary:: :toctree: generated/ :template: function.rst linear_model.enet_path linear_model.lars_path linear_model.lars_path_gram linear_model.lasso_path linear_model.orthogonal_mp linear_model.orthogonal_mp_gram linear_model.ridge_regression .. _manifold_ref: :mod:`sklearn.manifold`: Manifold Learning ========================================== .. automodule:: sklearn.manifold :no-members: :no-inherited-members: **User guide:** See the :ref:`manifold` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated :template: class.rst manifold.Isomap manifold.LocallyLinearEmbedding manifold.MDS manifold.SpectralEmbedding manifold.TSNE .. autosummary:: :toctree: generated :template: function.rst manifold.locally_linear_embedding manifold.smacof manifold.spectral_embedding manifold.trustworthiness .. _metrics_ref: :mod:`sklearn.metrics`: Metrics =============================== See the :ref:`model_evaluation` section and the :ref:`metrics` section of the user guide for further details. .. automodule:: sklearn.metrics :no-members: :no-inherited-members: .. currentmodule:: sklearn Model Selection Interface ------------------------- See the :ref:`scoring_parameter` section of the user guide for further details. .. autosummary:: :toctree: generated/ :template: function.rst metrics.check_scoring metrics.get_scorer metrics.get_scorer_names metrics.make_scorer Classification metrics ---------------------- See the :ref:`classification_metrics` section of the user guide for further details. .. autosummary:: :toctree: generated/ :template: function.rst metrics.accuracy_score metrics.auc metrics.average_precision_score metrics.balanced_accuracy_score metrics.brier_score_loss metrics.class_likelihood_ratios metrics.classification_report metrics.cohen_kappa_score metrics.confusion_matrix metrics.dcg_score metrics.det_curve metrics.f1_score metrics.fbeta_score metrics.hamming_loss metrics.hinge_loss metrics.jaccard_score metrics.log_loss metrics.matthews_corrcoef metrics.multilabel_confusion_matrix metrics.ndcg_score metrics.precision_recall_curve metrics.precision_recall_fscore_support metrics.precision_score metrics.recall_score metrics.roc_auc_score metrics.roc_curve metrics.top_k_accuracy_score metrics.zero_one_loss Regression metrics ------------------ See the :ref:`regression_metrics` section of the user guide for further details. .. autosummary:: :toctree: generated/ :template: function.rst metrics.explained_variance_score metrics.max_error metrics.mean_absolute_error metrics.mean_squared_error metrics.mean_squared_log_error metrics.median_absolute_error metrics.mean_absolute_percentage_error metrics.r2_score metrics.root_mean_squared_log_error metrics.root_mean_squared_error metrics.mean_poisson_deviance metrics.mean_gamma_deviance metrics.mean_tweedie_deviance metrics.d2_tweedie_score metrics.mean_pinball_loss metrics.d2_pinball_score metrics.d2_absolute_error_score Multilabel ranking metrics -------------------------- See the :ref:`multilabel_ranking_metrics` section of the user guide for further details. .. autosummary:: :toctree: generated/ :template: function.rst metrics.coverage_error metrics.label_ranking_average_precision_score metrics.label_ranking_loss Clustering metrics ------------------ See the :ref:`clustering_evaluation` section of the user guide for further details. .. automodule:: sklearn.metrics.cluster :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst metrics.adjusted_mutual_info_score metrics.adjusted_rand_score metrics.calinski_harabasz_score metrics.davies_bouldin_score metrics.completeness_score metrics.cluster.contingency_matrix metrics.cluster.pair_confusion_matrix metrics.fowlkes_mallows_score metrics.homogeneity_completeness_v_measure metrics.homogeneity_score metrics.mutual_info_score metrics.normalized_mutual_info_score metrics.rand_score metrics.silhouette_score metrics.silhouette_samples metrics.v_measure_score Biclustering metrics -------------------- See the :ref:`biclustering_evaluation` section of the user guide for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst metrics.consensus_score Distance metrics ---------------- .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst metrics.DistanceMetric Pairwise metrics ---------------- See the :ref:`metrics` section of the user guide for further details. .. automodule:: sklearn.metrics.pairwise :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst metrics.pairwise.additive_chi2_kernel metrics.pairwise.chi2_kernel metrics.pairwise.cosine_similarity metrics.pairwise.cosine_distances metrics.pairwise.distance_metrics metrics.pairwise.euclidean_distances metrics.pairwise.haversine_distances metrics.pairwise.kernel_metrics metrics.pairwise.laplacian_kernel metrics.pairwise.linear_kernel metrics.pairwise.manhattan_distances metrics.pairwise.nan_euclidean_distances metrics.pairwise.pairwise_kernels metrics.pairwise.polynomial_kernel metrics.pairwise.rbf_kernel metrics.pairwise.sigmoid_kernel metrics.pairwise.paired_euclidean_distances metrics.pairwise.paired_manhattan_distances metrics.pairwise.paired_cosine_distances metrics.pairwise.paired_distances metrics.pairwise_distances metrics.pairwise_distances_argmin metrics.pairwise_distances_argmin_min metrics.pairwise_distances_chunked Plotting -------- See the :ref:`visualizations` section of the user guide for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: display_all_class_methods.rst metrics.ConfusionMatrixDisplay metrics.DetCurveDisplay metrics.PrecisionRecallDisplay metrics.PredictionErrorDisplay metrics.RocCurveDisplay calibration.CalibrationDisplay .. _mixture_ref: :mod:`sklearn.mixture`: Gaussian Mixture Models =============================================== .. automodule:: sklearn.mixture :no-members: :no-inherited-members: **User guide:** See the :ref:`mixture` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst mixture.BayesianGaussianMixture mixture.GaussianMixture .. _modelselection_ref: :mod:`sklearn.model_selection`: Model Selection =============================================== .. automodule:: sklearn.model_selection :no-members: :no-inherited-members: **User guide:** See the :ref:`cross_validation`, :ref:`grid_search` and :ref:`learning_curve` sections for further details. Splitter Classes ---------------- .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst model_selection.GroupKFold model_selection.GroupShuffleSplit model_selection.KFold model_selection.LeaveOneGroupOut model_selection.LeavePGroupsOut model_selection.LeaveOneOut model_selection.LeavePOut model_selection.PredefinedSplit model_selection.RepeatedKFold model_selection.RepeatedStratifiedKFold model_selection.ShuffleSplit model_selection.StratifiedKFold model_selection.StratifiedShuffleSplit model_selection.StratifiedGroupKFold model_selection.TimeSeriesSplit Splitter Functions ------------------ .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst model_selection.check_cv model_selection.train_test_split .. _hyper_parameter_optimizers: Hyper-parameter optimizers -------------------------- .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst model_selection.GridSearchCV model_selection.HalvingGridSearchCV model_selection.ParameterGrid model_selection.ParameterSampler model_selection.RandomizedSearchCV model_selection.HalvingRandomSearchCV Model validation ---------------- .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst model_selection.cross_validate model_selection.cross_val_predict model_selection.cross_val_score model_selection.learning_curve model_selection.permutation_test_score model_selection.validation_curve Visualization ------------- .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: display_only_from_estimator.rst model_selection.LearningCurveDisplay model_selection.ValidationCurveDisplay .. _multiclass_ref: :mod:`sklearn.multiclass`: Multiclass classification ==================================================== .. automodule:: sklearn.multiclass :no-members: :no-inherited-members: **User guide:** See the :ref:`multiclass_classification` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst multiclass.OneVsRestClassifier multiclass.OneVsOneClassifier multiclass.OutputCodeClassifier .. _multioutput_ref: :mod:`sklearn.multioutput`: Multioutput regression and classification ===================================================================== .. automodule:: sklearn.multioutput :no-members: :no-inherited-members: **User guide:** See the :ref:`multilabel_classification`, :ref:`multiclass_multioutput_classification`, and :ref:`multioutput_regression` sections for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated :template: class.rst multioutput.ClassifierChain multioutput.MultiOutputRegressor multioutput.MultiOutputClassifier multioutput.RegressorChain .. _naive_bayes_ref: :mod:`sklearn.naive_bayes`: Naive Bayes ======================================= .. automodule:: sklearn.naive_bayes :no-members: :no-inherited-members: **User guide:** See the :ref:`naive_bayes` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst naive_bayes.BernoulliNB naive_bayes.CategoricalNB naive_bayes.ComplementNB naive_bayes.GaussianNB naive_bayes.MultinomialNB .. _neighbors_ref: :mod:`sklearn.neighbors`: Nearest Neighbors =========================================== .. automodule:: sklearn.neighbors :no-members: :no-inherited-members: **User guide:** See the :ref:`neighbors` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst neighbors.BallTree neighbors.KDTree neighbors.KernelDensity neighbors.KNeighborsClassifier neighbors.KNeighborsRegressor neighbors.KNeighborsTransformer neighbors.LocalOutlierFactor neighbors.RadiusNeighborsClassifier neighbors.RadiusNeighborsRegressor neighbors.RadiusNeighborsTransformer neighbors.NearestCentroid neighbors.NearestNeighbors neighbors.NeighborhoodComponentsAnalysis .. autosummary:: :toctree: generated/ :template: function.rst neighbors.kneighbors_graph neighbors.radius_neighbors_graph neighbors.sort_graph_by_row_values .. _neural_network_ref: :mod:`sklearn.neural_network`: Neural network models ==================================================== .. automodule:: sklearn.neural_network :no-members: :no-inherited-members: **User guide:** See the :ref:`neural_networks_supervised` and :ref:`neural_networks_unsupervised` sections for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst neural_network.BernoulliRBM neural_network.MLPClassifier neural_network.MLPRegressor .. _pipeline_ref: :mod:`sklearn.pipeline`: Pipeline ================================= .. automodule:: sklearn.pipeline :no-members: :no-inherited-members: **User guide:** See the :ref:`combining_estimators` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst pipeline.FeatureUnion pipeline.Pipeline .. autosummary:: :toctree: generated/ :template: function.rst pipeline.make_pipeline pipeline.make_union .. _preprocessing_ref: :mod:`sklearn.preprocessing`: Preprocessing and Normalization ============================================================= .. automodule:: sklearn.preprocessing :no-members: :no-inherited-members: **User guide:** See the :ref:`preprocessing` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst preprocessing.Binarizer preprocessing.FunctionTransformer preprocessing.KBinsDiscretizer preprocessing.KernelCenterer preprocessing.LabelBinarizer preprocessing.LabelEncoder preprocessing.MultiLabelBinarizer preprocessing.MaxAbsScaler preprocessing.MinMaxScaler preprocessing.Normalizer preprocessing.OneHotEncoder preprocessing.OrdinalEncoder preprocessing.PolynomialFeatures preprocessing.PowerTransformer preprocessing.QuantileTransformer preprocessing.RobustScaler preprocessing.SplineTransformer preprocessing.StandardScaler preprocessing.TargetEncoder .. autosummary:: :toctree: generated/ :template: function.rst preprocessing.add_dummy_feature preprocessing.binarize preprocessing.label_binarize preprocessing.maxabs_scale preprocessing.minmax_scale preprocessing.normalize preprocessing.quantile_transform preprocessing.robust_scale preprocessing.scale preprocessing.power_transform .. _random_projection_ref: :mod:`sklearn.random_projection`: Random projection =================================================== .. automodule:: sklearn.random_projection :no-members: :no-inherited-members: **User guide:** See the :ref:`random_projection` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst random_projection.GaussianRandomProjection random_projection.SparseRandomProjection .. autosummary:: :toctree: generated/ :template: function.rst random_projection.johnson_lindenstrauss_min_dim .. _semi_supervised_ref: :mod:`sklearn.semi_supervised`: Semi-Supervised Learning ======================================================== .. automodule:: sklearn.semi_supervised :no-members: :no-inherited-members: **User guide:** See the :ref:`semi_supervised` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst semi_supervised.LabelPropagation semi_supervised.LabelSpreading semi_supervised.SelfTrainingClassifier .. _svm_ref: :mod:`sklearn.svm`: Support Vector Machines =========================================== .. automodule:: sklearn.svm :no-members: :no-inherited-members: **User guide:** See the :ref:`svm` section for further details. Estimators ---------- .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst svm.LinearSVC svm.LinearSVR svm.NuSVC svm.NuSVR svm.OneClassSVM svm.SVC svm.SVR .. autosummary:: :toctree: generated/ :template: function.rst svm.l1_min_c .. _tree_ref: :mod:`sklearn.tree`: Decision Trees =================================== .. automodule:: sklearn.tree :no-members: :no-inherited-members: **User guide:** See the :ref:`tree` section for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst tree.DecisionTreeClassifier tree.DecisionTreeRegressor tree.ExtraTreeClassifier tree.ExtraTreeRegressor .. autosummary:: :toctree: generated/ :template: function.rst tree.export_graphviz tree.export_text Plotting -------- .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst tree.plot_tree .. _utils_ref: :mod:`sklearn.utils`: Utilities =============================== .. automodule:: sklearn.utils :no-members: :no-inherited-members: **Developer guide:** See the :ref:`developers-utils` page for further details. .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: class.rst utils.Bunch .. autosummary:: :toctree: generated/ :template: function.rst utils.as_float_array utils.assert_all_finite utils.deprecated utils.estimator_html_repr utils.gen_batches utils.gen_even_slices utils.indexable utils.murmurhash3_32 utils.resample utils._safe_indexing utils.safe_mask utils.safe_sqr utils.shuffle Input and parameter validation ------------------------------ .. automodule:: sklearn.utils.validation :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst utils.check_X_y utils.check_array utils.check_scalar utils.check_consistent_length utils.check_random_state utils.validation.check_is_fitted utils.validation.check_memory utils.validation.check_symmetric utils.validation.column_or_1d utils.validation.has_fit_parameter Utilities used in meta-estimators --------------------------------- .. automodule:: sklearn.utils.metaestimators :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst utils.metaestimators.available_if Utilities to handle weights based on class labels ------------------------------------------------- .. automodule:: sklearn.utils.class_weight :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst utils.class_weight.compute_class_weight utils.class_weight.compute_sample_weight Utilities to deal with multiclass target in classifiers ------------------------------------------------------- .. automodule:: sklearn.utils.multiclass :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst utils.multiclass.type_of_target utils.multiclass.is_multilabel utils.multiclass.unique_labels Utilities for optimal mathematical operations --------------------------------------------- .. automodule:: sklearn.utils.extmath :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst utils.extmath.safe_sparse_dot utils.extmath.randomized_range_finder utils.extmath.randomized_svd utils.extmath.fast_logdet utils.extmath.density utils.extmath.weighted_mode Utilities to work with sparse matrices and arrays ------------------------------------------------- .. automodule:: sklearn.utils.sparsefuncs :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst utils.sparsefuncs.incr_mean_variance_axis utils.sparsefuncs.inplace_column_scale utils.sparsefuncs.inplace_row_scale utils.sparsefuncs.inplace_swap_row utils.sparsefuncs.inplace_swap_column utils.sparsefuncs.mean_variance_axis utils.sparsefuncs.inplace_csr_column_scale .. automodule:: sklearn.utils.sparsefuncs_fast :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst utils.sparsefuncs_fast.inplace_csr_row_normalize_l1 utils.sparsefuncs_fast.inplace_csr_row_normalize_l2 Utilities to work with graphs ----------------------------- .. automodule:: sklearn.utils.graph :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst utils.graph.single_source_shortest_path_length Utilities for random sampling ----------------------------- .. automodule:: sklearn.utils.random :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst utils.random.sample_without_replacement Utilities to operate on arrays ------------------------------ .. automodule:: sklearn.utils.arrayfuncs :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst utils.arrayfuncs.min_pos Metadata routing ---------------- .. automodule:: sklearn.utils.metadata_routing :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst utils.metadata_routing.get_routing_for_object utils.metadata_routing.process_routing .. autosummary:: :toctree: generated/ :template: class.rst utils.metadata_routing.MetadataRouter utils.metadata_routing.MetadataRequest utils.metadata_routing.MethodMapping Scikit-learn object discovery ----------------------------- .. automodule:: sklearn.utils.discovery :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst utils.discovery.all_estimators utils.discovery.all_displays utils.discovery.all_functions Scikit-learn compatibility checker ---------------------------------- .. automodule:: sklearn.utils.estimator_checks :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst utils.estimator_checks.check_estimator utils.estimator_checks.parametrize_with_checks Utilities for parallel computing -------------------------------- .. automodule:: sklearn.utils.parallel :no-members: :no-inherited-members: .. currentmodule:: sklearn .. autosummary:: :toctree: generated/ :template: function.rst utils.parallel.delayed utils.parallel_backend utils.register_parallel_backend .. autosummary:: :toctree: generated/ :template: class.rst utils.parallel.Parallel Recently deprecated ===================